[{"data":1,"prerenderedAt":2372},["ShallowReactive",2],{"construction-evidence":3},{"studies":4,"referenceEquipment":1753,"datasets":1954,"studyLabels":2301,"datasetLabels":2339,"taskOrder":2369,"excludedStudies":2370,"excludedDatasets":2371},[5,54,96,143,182,205,230,282,320,347,389,415,441,484,517,547,577,612,641,681,709,737,770,795,823,857,891,926,956,987,1020,1049,1090,1118,1151,1179,1205,1234,1266,1296,1325,1361,1384,1422,1453,1490,1525,1558,1579,1619,1651,1678,1715],{"id":6,"shortName":7,"title":8,"year":9,"fulltextStatus":10,"publicationStatus":11,"siteTypes":12,"taskLevel":14,"tasks":15,"platforms":17,"sensorTags":20,"reference":24,"referenceNote":25,"engineeringTask":26,"taskRequirement":27,"requirementSource":27,"siteCount":28,"independentValidation":27,"geometricQuality":29,"taskOutcome":27,"applicabilityClaim":27,"taskLimitations":30,"sourceLocator":31,"sensorsRaw":32,"platformRaw":38,"keyIdeaZh":41,"constructionRelevance":42,"strengths":43,"limitations":47},"asadi2020ugvuav","UGV-UAV (blimp) team","An integrated UGV-UAV system for construction site data collection",2020,"full_text_reviewed","peer_reviewed_published",[13],"controlled_experiment",false,[16],"robotic_capture",[18,19],"wheeled","uav",[21,22,23],"lidar","camera","imu","none","實驗只報告硬體使用率與相對位置的比較，沒有定位或點雲精度評估（Sec. 5、6）。","autonomous construction data collection (navigation + mapping)","not_reported","1 laboratory (NCSU Constructed Facilities Lab, test environments A and B)","none found (Tables 2-5 report hardware utilization only)","full text not reviewed","Abstract",[33,34,35,36,37],"stereo camera ZED (forward-looking, on a pan-tilt unit) feeding LNSNet segmentation and RTAB-Map (Sec. 3.1, 4.1, 4.2)","2D LiDAR HOKUYO URG 04LX-UG01 at the UGV front for a 2D occupancy map; scan limited to 180 deg of 240 deg, 4000 mm maximum radius (Sec. 3.1, 4)","wide-angle camera Point Grey Flea3 on a tilt unit tracking a Whycon marker on the blimp (Sec. 3.1, 4.3.1)","UGV wheel encoders fused with an IMU by EKF, used as odometry for RTAB-Map (Sec. 4.2)","blimp: Raspberry Pi camera v2 (rolling shutter, 410 x 308 at 30 fps) and Bosch BNO055 9-DOF IMU (100 Hz) for Vins-Mono (Sec. 3.2.1, 4.3.2)",[39,40],"wheeled UGV built on a Clearpath Husky A200 (Sec. 3.1)","custom indoor helium blimp, 1.83 m ellipsoid envelope, 300 g payload (Sec. 3.2)","作者以 Clearpath Husky A200 改裝的地面無人車（UGV）與自製室內氦氣飛艇組成異質機器人團隊。UGV 以 ZED 立體相機搭配自行訓練的 LNSNet 地面與非地面語意分割，經 RTAB-Map 建立三維分割占據地圖（里程計改用輪式編碼器與 IMU 的 EKF 融合），另以 Hokuyo 2D 光達建立二維占據地圖，並以 RRT 規劃路徑；飛艇的 Raspberry Pi 相機與 Bosch BNO055 IMU 資料串流到 UGV 筆電執行 VINS-Mono，並以 SLAM 位姿比對或 UGV 上 Flea3 相機追蹤 Whycon 標記取得相對位置，使飛艇跟隨 UGV，或在 UGV 無法到達時前往目標點蒐集影像。所有模組在單一筆電即時運行，實驗只報告硬體使用率與相對位置的比較，沒有定位或點雲精度評估。","測試於北卡州立大學 Constructed Facilities Lab（作者描述為類似室內工地、有材料堆置與構件製作），共三次實驗：一次在無氣流干擾的走廊（115 秒、約 21 m），兩次在該實驗室的雜亂環境 A 與 B；非施工中工地。作者明言驗證重點是系統整合與即時運作，未評估 RTAB-Map、VINS-Mono 或 LNSNet 的精度，也沒有點雲幾何精度評估；表 2 至 5 僅為硬體使用率（Sec. 5、6）。",[44,45,46],"feasibility of real-time UAV-UGV interaction demonstrated (Highlights)","Blimp followed the manually driven UGV through a 115 s hallway trial covering about 21 m at 0.2 m\u002Fs (Sec. 5.2, Fig. 21)","All modules ran in real time on one laptop during a 3 min test (Sec. 5.1)",[48,49,50,51,52,53],"tested in construction-like indoor environment rather than an active site (Abstract)","Validation targets integration and real-time operation, not the accuracy of RTAB-Map, Vins-Mono or LNSNet; drift was judged only from test videos (Sec. 5, 6)","Relative positions from marker detection and SLAM comparison differ by roughly 25 cm (x, y) and 30 cm (z), attributed to marker error (about 6 cm) and sensor offsets (Sec. 5.2)","Blimp is blown off course near air-conditioning vents and fans; no UAV obstacle avoidance (Sec. 5, 6)","UAV cannot follow the UGV above 1 m\u002Fs because movement commands arrive at 0.5 Hz (Sec. 5.2)","2D LiDAR and stereo maps are not integrated (Sec. 4, 6)",{"id":55,"shortName":56,"title":57,"year":58,"fulltextStatus":10,"publicationStatus":11,"siteTypes":59,"taskLevel":14,"tasks":61,"platforms":64,"sensorTags":65,"reference":66,"referenceNote":67,"engineeringTask":68,"taskRequirement":69,"requirementSource":27,"siteCount":70,"independentValidation":71,"geometricQuality":72,"taskOutcome":27,"applicabilityClaim":73,"taskLimitations":74,"sourceLocator":75,"sensorsRaw":76,"platformRaw":80,"keyIdeaZh":82,"constructionRelevance":83,"strengths":84,"limitations":88},"blum2021precisebim","Localization in architectural 3D plans","Precise Robot Localization in Architectural 3D Plans",2021,[60],"real_construction_site",[62,63],"bim_alignment","robot_localization",[18],[21,22,23],"independent","以全測站追蹤機器人上的稜鏡為參考；Table II 至 IV 的模型偏差是把網格上下兩側結構人為拉開 0.3 m 模擬，只有 Table I 使用未加偏差的平面圖（Sec. IV-C）。","construction robot localization relative to building plan","not_reported (motivated by on-site robotic fabrication accuracy)","1 site, 3 locations","total station tracking prism","position RMSE vs total-station prism; repeatability eigenvalues; failure rate (Tables I-IV)","outlier filtering and measurement selection are key for on-site precise localization","static robot; plan-derived mesh; small sample","Sec. IV-VI; Tables I-IV",[77,78,79],"3D LiDAR","3 cameras","IMU",[81],"wheeled UGV (supermegabot)","作者主張施工中牆體缺漏、臨時物與實作偏差使 ICP 對整棟 BIM 的對位不可靠，因此提出「局部參考」：先對整個平面圖模型做點到平面 ICP，再只對選定的參考牆面（至少三個互不平行的面）精修，並以影像密度估計網路的分數剔除或加權雜物、人員等離群點後融合到光達點。實驗在真實建築工地以靜止機器人搭配移動工人與雜物進行，並以全測站追蹤機器人上的稜鏡作為參考，且修正參考牆的竣工偏差；表 II 至 IV 的模型偏差是作者把網格上下兩側結構人為拉開 0.3 m 所模擬。","於真實建築工地以靜止機器人測試三個位置（每處約 1 分鐘、約 300 次光達掃描，結果為三次執行的平均），現場有移動工人、木板與設備箱等雜物，並以全測站追蹤機器人上的稜鏡作為參考，且依實測參考牆偏差修正參考值。表 II 至 IV 的模型偏差是作者在網格中把上下兩側結構人為拉開 0.3 m 所模擬，並非實測施工偏差；表 I 才是未加人為偏差的雜物影響比較。屬少數在施工中環境以全測站為獨立參考、在建築平面圖模型中定位的研究（Sec. IV）。",[85,86,87],"Lowest position RMSE per location came from selective localization with semantic information: 232 mm (filtered) vs 390 mm for full-model ICP at location A, 68 mm vs 222 mm at B, 52 mm (weighted) vs 76 mm at C, i.e. at least 30% lower error (Tables II-IV; Abstract)","Selective localization against reference walls constrains two directions well (trace close to the maximum eigenvalue), while full-model ICP is uncertain in more than one direction (Sec. V, Fig. 6)","Only on-board sensing, no markers or site preparation (Sec. I)",[89,90,91,92,93,94,95],"no single method combination always worked; semantic filtering performance location-dependent (Sec. V)","lateral uncertainty high where few walls constrain one axis (Sec. V)","robot stationary during evaluation; ~1 min \u002F 300 scans per location (Sec. IV-C)","Tables II-IV use a model whose upper and lower structures were moved 0.3 m apart to simulate a severe deviation; only Table I (157 to 218 mm at A and 74 to 83 mm at B with clutter) uses the unmodified plan (Sec. IV-C)","High failure rates for some variants: 76.5% (selective, weighted) and 50.3% (selective, full) at A, 52.6% (selective, weighted) at B, 25.6% (selective, filtered) at C (Tables II-IV)","Binary semantic filtering at location C removed nearly all points on the lateral reference wall; the density network, trained on NYU indoor data, partly filtered building structure outside its training domain (Sec. V)","Plan-derived mesh with a planar floor and equal wall heights because no floor or ceiling information was available (Sec. IV-B)",{"id":97,"shortName":98,"title":99,"year":100,"fulltextStatus":10,"publicationStatus":11,"siteTypes":101,"taskLevel":103,"tasks":104,"platforms":106,"sensorTags":108,"reference":66,"referenceNote":110,"engineeringTask":111,"taskRequirement":112,"requirementSource":113,"siteCount":114,"independentValidation":115,"geometricQuality":116,"taskOutcome":117,"applicabilityClaim":118,"taskLimitations":119,"sourceLocator":120,"sensorsRaw":121,"platformRaw":123,"keyIdeaZh":125,"constructionRelevance":126,"strengths":127,"limitations":134},"bosche2014flatness","TLS + BIM floor flatness control","Automating surface flatness control using terrestrial laser scanning and building information models",2014,[102],"completed_building",true,[105],"dimensional_qc",[107],"tripod",[109],"tls",null,"concrete floor surface flatness (regularity) compliance control","Straightedge maximum deviation tolerances and FF\u002FFL F-Number tolerances","BS EN 13670:2009, CONSTRUCT NSCS, BS 8204, ACI 117-06, ASTM E1155-96 (Sec. 2.1, Tables 1-2)","2 floor slabs in one building","manual 2 m straightedge and precision steel rule on a chalk-line grid matching the generated straightedges (Sec. 7.2)","per-straightedge deviations and FF values versus manual measurement; sensitivity to point subsampling","individual straightedge deviations and overall floor compliance compared with manual control (Fig. 12)","TLS provides sufficient accuracy for standard surface regularity control (Sec. 9)","two slabs; TLS only; F-Numbers not validated against manual F-Number survey","Sec. 2.1, 6.1, 7-9, Figs. 12-14",[122],"terrestrial laser scanner (FARO Focus3D)",[124],"static terrestrial (tripod)","作者以 Scan-vs-BIM 原理把工地 TLS 點雲對齊 BIM，並把每個點分派給對應的樓板構件，再自動套用兩種標準平整度檢查法：直尺法（Straightedge，含隨機、方格與作者新提的星形方格三種直尺配置）與依 ASTM E1155 計算 F-number（FF 平整度、FL 水平度）。系統在兩片約 25 年的混凝土實驗室樓板上，與粉筆方格加 2 m 直尺與鋼尺的人工量測比較，作者結論是 TLS 的精度足以執行標準平整度檢查，而且量測更完整、更快。論文第 2 節整理了 BS EN 13670、BS 8204、ACI 117、ASTM E1155 等容許差來源，可作為品質檢查列的需求依據。","已完工建物中兩片約 25 年的混凝土實驗室樓板（Heriot-Watt 大學，6.40 m × 6.70 m 與 4.80 m × 8.10 m 區段），以人工直尺為獨立參考並比對合格判定；非施工中工地，也非 SLAM 點雲。是本批次紀錄中唯一具標準化判定與獨立參考的任務方法錨點；是否為全語料中唯一未經系統檢索確認（推論）。",[128,129,130,131,132,133],"Straightedge deviations from TLS agreed with manual measurements; mean differences 1.2 mm (SD 1.0) and 0.7 mm (SD 0.4) for the two slabs with no significant difference in a two-tailed t-test at alpha 0.05 (Fig. 12)","Using 4%, 10% or 25% of the initial point clouds did not materially change straightedge or F-Number results (Sec. 8.1.1, 8.3)","Many more straightedges than a manual survey could afford; equivalent manual Random or Grid-Star surveys estimated at about 35 h and 23 h (Sec. 8.2)","Results link to BIM objects and are repeatable by other stakeholders (Sec. 9)","With 230 straightedges each on the Acoustic Lab slab (320 each on the Drainage Lab slab), the Random and Grid-Star methods found maximum deviations of 11.3 and 11.4 mm versus 7.6 mm with Grid-Square on the Acoustic Lab slab, identifying a non-compliant area (100% global flatness tolerance 10 mm) that Grid-Square missed (Sec. 8.2, Fig. 13)","The whole TLS workflow took about 1 h 50 min and 1 h versus 3 h (17 straightedges) and 1.5 h (10 straightedges) for manual Grid-Square control (Sec. 8.1.2, Table 5)",[135,136,137,138,139,140,141,142],"Two straightedges showed notable manual-versus-TLS differences whose cause (manual or scanning error) is unclear (Sec. 8.1.1)","F-Numbers agreement with 3 m straightedge equivalences was weaker for one slab and needs further validation (Sec. 8.3, Sec. 9)","Only two aged laboratory floor slabs tested (Sec. 7.1)","Aligning TLS scans with the BIM may need user input (Sec. 3)","Scanner error figures are indicative and depend on material and incidence angle (Sec. 2.2)","(inference) Evidence is for static TLS; whether SLAM point clouds with centimetre-level noise and drift support the same decisions is untested","Half of the TLS workflow time was scanning, with high-accuracy settings about five times slower than standard settings (Sec. 8.1.2)","Grid-Square straightedges missed a non-compliant area that Random and Grid-Star detected, so a standard sparse layout can under-report defects (Sec. 8.2)",{"id":144,"shortName":145,"title":146,"year":147,"fulltextStatus":10,"publicationStatus":11,"siteTypes":148,"taskLevel":14,"tasks":149,"platforms":150,"sensorTags":151,"reference":24,"referenceNote":110,"engineeringTask":152,"taskRequirement":153,"requirementSource":154,"siteCount":155,"independentValidation":156,"geometricQuality":157,"taskOutcome":158,"applicabilityClaim":159,"taskLimitations":160,"sourceLocator":161,"sensorsRaw":162,"platformRaw":164,"keyIdeaZh":165,"constructionRelevance":166,"strengths":167,"limitations":173},"bosche2010asbuiltdims","Scan-vs-BIM object recognition and as-built dimensions","Automated recognition of 3D CAD model objects in laser scans and calculation of as-built dimensions for dimensional compliance control in construction",2010,[60],[105],[107],[109],"dimensional compliance control of erected steel structure (column plumb, inter-column distances)","project dimensional tolerances, e.g. AISC 303-05 and MNL 135-00 (named as examples)","AISC 303-05, MNL 135-00 (Sec. 3.2)","1 construction project, 5 scans","none (authors state ground truth unavailable)","registration MSE and matched-point counts","as-built minus as-designed column positions and plumb (no ground truth)","potential for automated as-built dimension calculation and control; accuracy not established (Sec. 3.3.1)","no ground truth; range-dependent error; prefabricated-shape assumption","Sec. 3.2-3.3, Tables 4-6",[163],"terrestrial laser scanner (Trimble GX 3D per Sec. 1.1.2 and ref. [44])",[124],"作者改良先前的方法，先以人工選三組以上對應點把工地雷射掃描粗對齊專案 3D CAD 模型，再以新的 ICP 精對齊整個模型，依與各構件表面相符的點數與覆蓋面積判定構件是否被辨識。接著對每個被辨識的構件個別再做 ICP，求得其竣工位姿，並與設計位姿比較，推算柱垂直度與柱間距等尺寸以檢查是否符合容許差。實驗使用加拿大多倫多一座發電廠鋼構廠房施工期間的五次掃描。作者坦承缺乏真值，且位姿偏差與掃描距離相關，結果尚不足以判斷尺寸合規檢查的精度。","施工中鋼構廠房（Portland Energy Center 發電廠專案，多倫多）的現場 TLS 掃描，屬真實工地資料；以 AISC 303-05 與 MNL 135-00 為容許差來源示例（Sec. 3.2）。展示掃描誤差如何傳遞到竣工尺寸，但無獨立參考。",[168,169,170,171,172],"Model fine registration lowered MSE and increased matched points for all five scans compared with the earlier method (Table 2)","Object fine registration further lowered MSE (13-37 mm2) (Table 4)","Recognition is quasi-automated, robust to clutter and occlusion, and efficient (Sec. 4)","The author states that recall improved for all scans; Table 3 shows higher recall for Scans 2-5 and equal recall for Scan 1 (83%), overall recall 83% vs 80% and precision 93% vs 91%, but lower precision for Scans 4 and 5 (93% vs 94%, 82% vs 84%); gains were small because the manual coarse registrations were already accurate (Sec. 2.4.3, Table 3)","Model fine registration took about 2 min per iteration on CPU for about 650,000 points and about 20,000 facets; without the acceleration it would take about two orders of magnitude longer (Sec. 2.4.2)",[174,175,176,177,178,179,180,181],"Assumes each object's shape already complies with tolerances, reasonable only for prefabricated elements (Sec. 3.1)","No ground truth for as-built poses or dimensions; results not reliable enough to conclude on accuracy for dimensional compliance (Sec. 3.3.1)","Calculated pose deviations correlate with scanner range (r = 0.45; columns 20-80 m from the scanner), possibly from range-dependent scanner error or fewer recognized points (Sec. 3.3.1)","Per-object registration is often ill-conditioned because column ends are occluded (Sec. 3.3.1)","Coarse registration is manual; mesh models lack semantics, so control points were computed manually (Sec. 2.1, 3.3.2)","Recall and precision rely on object presence identified by manual visual inspection of each scan (footnote to Sec. 2.4.3)","The expert manual time estimate (a few hours to one day) is not based on field measurements (footnote to Sec. 3.3.2)","Object poses are refined independently, which may produce clashes between objects (Sec. 5)",{"id":183,"shortName":184,"title":185,"year":186,"fulltextStatus":10,"publicationStatus":11,"siteTypes":187,"taskLevel":14,"tasks":188,"platforms":189,"sensorTags":191,"reference":27,"referenceNote":110,"engineeringTask":110,"taskRequirement":110,"requirementSource":110,"siteCount":110,"independentValidation":110,"geometricQuality":110,"taskOutcome":110,"applicabilityClaim":110,"taskLimitations":110,"sourceLocator":110,"sensorsRaw":192,"platformRaw":195,"keyIdeaZh":196,"constructionRelevance":197,"strengths":198,"limitations":200},"bosche2012planebim","Plane-based scan-to-BIM coarse registration","Plane-based registration of construction laser scans with 3D\u002F4D building models",2012,[60],[62],[190],"not_stated",[109],[193,194],"[\"terrestrial laser scanner (model not reported)","Engineering V scans of a concrete structure under construction and Bicocca scans of about 5 M points each (Sec. 5.2, 5.3)\"]",[],"作者指出營建與設施管理（AEC\u002FFM）情境中，建物多由平面構成，掃描儀的垂直軸也通常與模型一致，但自相似、雜物與多物件模型使全自動的掃描與 BIM 配準複雜且常為病態問題。論文提出半自動的平面式粗配準：自動從 3D\u002F4D 模型網格擷取水平與垂直平面；點雲平面可用加上限制條件的 RANSAC 全自動擷取，或由使用者點選平面上任一點後以 RANSAC 一鍵擷取；再由使用者配對兩組不平行的垂直平面與一組水平平面，以 Horn 方法由法向量求旋轉，並由三平面交點求平移。以 University of Waterloo Engineering V 施工中混凝土結構的 12 次掃描測試，兩位使用者以本系統的平均粗配準時間為 3 分 34 秒與 4 分 12 秒，短於 Geomagic Studio 與 RealWorks，經 ICP 精配準後有 75% 至 92% 的情況品質相近或較佳；在 Bicocca 資料上，自動平面擷取失敗且耗時約 3 小時，半自動方法約 10 分鐘即正確完成。","直接針對營建工地雷射掃描與 3D\u002F4D BIM 的粗配準；實驗使用 University of Waterloo Engineering V 混凝土結構施工期間的 12 次掃描，以及義大利米蘭 Bicocca 集合住宅專案的掃描（Sec. 5.2、5.3）。",[199],"[\"registration quality at least as good as commonly used AEC\u002FFM software, simpler and faster (abstract, Sec. 6)\", \"after ICP fine registration, results were similar or better than the point-based packages in 75 to 92% of the 12 registrations (Table 2, Sec. 5.2)\", \"fine registration converged in fewer iterations from the proposed coarse alignments (Sec. 5.2)\", \"using 10% of the points did not affect accuracy and kept computation low (Sec. 5.2)\", \"semi-automated one-click extraction stayed accurate on the Bicocca data where automated extraction failed (Sec. 5.3)\"]",[201,202,203,204],"[\"plane matching is manual (abstract, Sec. 4.3)\", \"fully automated model-scan registration in AEC\u002FFM is complex and often ill-posed because of self-similarities, clutter and multi-object models (abstract, Sec. 3)\", \"fails when three non-parallel planes are not visible","one of the 12 scans failed because no horizontal plane was visible (Sec. 5.2, 6)\", \"automated scan plane extraction was poor and took about 3 h on distant Bicocca scans","w_plane is critical and smaller values raise run time about tenfold (Sec. 4.2.1, 5.3)\", \"cylindrical surfaces are not supported","octree acceleration and automated matching left for future work (Sec. 6)\", \"timing comparison is indicative only, with two users (Sec. 5.2)\"]",{"id":206,"shortName":207,"title":208,"year":209,"fulltextStatus":10,"publicationStatus":11,"siteTypes":210,"taskLevel":14,"tasks":212,"platforms":213,"sensorTags":214,"reference":216,"referenceNote":217,"engineeringTask":110,"taskRequirement":110,"requirementSource":110,"siteCount":110,"independentValidation":110,"geometricQuality":110,"taskOutcome":110,"applicabilityClaim":110,"taskLimitations":110,"sourceLocator":110,"sensorsRaw":218,"platformRaw":220,"keyIdeaZh":222,"constructionRelevance":223,"strengths":224,"limitations":226},"bueno2018plcs","4-PlCS scan-to-BIM registration","4-Plane congruent sets for automatic registration of as-is 3D point clouds with 3D BIM models",2018,[211,60],"simulation",[62],[211,190],[215],"unverified","not_independent","實際工地資料的真值轉換來自精配準結果，不是獨立測量（Sec. 5.2）。",[219],"[\"real datasets UW-E5 and Mercury-1: laser scans from construction sites, scanner model not reported in the paper (Sec. 5.1, Acknowledgments)\", \"simulated datasets House-1, House-2, Steel-1: points generated on BIM mesh surfaces with sigma = 2 mm noise, no subsampling (Sec. 5.1)\"]",[221],"[\"simulation\", \"real construction-site scans (acquisition platform not reported)\"]","作者指出營建品質與進度控制的 Scan-vs-BIM 流程需要準確的點雲與 BIM 配準，但現況點雲常不完整、含模型外物件，建物又常有對稱與自相似結構。論文把 4 點一致集合（4PCS）改為以平面為基元的 4-PlCS 粗配準，並回傳排序的候選轉換讓使用者快速選擇；若已知垂直軸則採 4.5-PlCS 變體。五組資料（三組模擬、兩組實際工地）中，正確轉換皆排在第一或第二。","直接針對營建 Scan-vs-BIM；五組資料涵蓋住宅、工業與商業建物，其中 UW-E5（University of Waterloo Engineering V，亦用於 bosche2012planebim）與 Mercury-1 為實際工地掃描，由 Mercury Engineering 與 University of Waterloo 等單位提供（Sec. 5.1、Table 2、Acknowledgments），其餘三組為由 BIM 模擬的點雲。",[225],"[\"correct transformation ranked first on the three simulated datasets and second on both real datasets (Tables 3 to 5, Sec. 6)\", \"correct transformation close to the reference before fine registration: eps_R at most 0.12 deg and eps_T at most 181.1 mm (Table 7)\", \"centroid support reduces congruent bases by a further factor of 200 or more (Sec. 5.3)\", \"4-PlCS ranks the correct transformation at least as well as 3-PlCS and better on Steel-1 (Table 8, Sec. 5.7)\", \"handles symmetry, self-similarity and clutter via plane-patch support (Sec. 6)\"]",[227,228,229],"[\"4-plane bases give no clear computational benefit over 3-plane bases","4-PlCS was slower on four of five datasets (abstract, Table 8, Sec. 5.7)\", \"on real data several plausible candidates with similar support (e.g., a one-floor offset on UW-E5) require a final visual choice by the user (Sec. 5.4, Tables 4 and 5)\", \"Point Support reduces candidates by only 5 to 10% and does not improve the ranking while adding cost (Sec. 5.5, Table 6)\", \"coarse only","fine registration still required (Sec. 6)\", \"relies on sufficient planar structure (inference)\", \"ground-truth transformations for the real datasets are the result of fine registration, not independent survey (Sec. 5.2)\"]",{"id":231,"shortName":232,"title":233,"year":234,"fulltextStatus":10,"publicationStatus":11,"siteTypes":235,"taskLevel":103,"tasks":237,"platforms":239,"sensorTags":240,"reference":241,"referenceNote":242,"engineeringTask":243,"taskRequirement":244,"requirementSource":27,"siteCount":245,"independentValidation":246,"geometricQuality":247,"taskOutcome":248,"applicabilityClaim":249,"taskLimitations":250,"sourceLocator":251,"sensorsRaw":252,"platformRaw":259,"keyIdeaZh":263,"constructionRelevance":264,"strengths":265,"limitations":271},"charron2026slamcentric","SLAM-centric infrastructure inspection","SLAM-centric visual inspection of civil infrastructure",2026,[236],"infrastructure",[238],"inspection",[18],[21,22,23],"partial","只以 7 個明確特徵的長度對照現場量測（儀器未說明）；沒有軌跡、地圖或缺陷尺寸的真值（Sec. 5.5）。","robot-aided visual defect inspection of civil infrastructure (defect localization and sizing on a bridge and a parking garage)","not_reported as an acceptance requirement; GSD analysis targets minimum detectable crack widths of 2.1 mm (garage) and 3.5 mm (bridge) (Table 3)","2 structures (1 bridge, 1 parking garage), UGV data","7 on-site length measurements of unambiguous features (instrument not stated); no trajectory, map or defect ground truth","reference-free point-to-plane surface thickness on extracted planes and CloudCompare density (Table 7)","defect hull areas, bounding boxes and crack lengths (Tables 5-6, no ground truth); 7 proxy feature lengths versus on-site measurements (Table 9)","accurate defect localization, dimensional quantification and dense inspection maps in real-world scenarios (abstract)","no trajectory ground truth; proxy features instead of defects; planar-defect assumption; UGV only in evaluation","Sec. 5.1-5.5, Tables 5-9",[253,254,255,256,257,258],"3D LiDAR Velodyne VLP-16 Lite (UGV adds an upward VLP-16)","SLAM camera FLIR Blackfly S 3.2 MP with 185 x 140 deg Fujinon lens","inspection camera FLIR Blackfly S 12.3 MP with 8 mm lens","IMU Xsens MTi-30 (400 Hz)","UGV adds an upward high-resolution RGB camera and a FLIR ADK infrared camera","Teensy 3.6 microcontroller for hardware time synchronization",[260,261,262],"UGV (Clearpath Husky) for both evaluated datasets (Sec. 5.1)","handheld rig with a backpack for computer and battery (Sec. 4.2); handheld recordings exist in the public dataset (Conestogo bridge, Structures lab) but are not evaluated (Table 4)","USV written as 'Huron from Clearpath Robotics' (Sec. 4.2); no USV recordings are listed in Table 4","作者提出以 SLAM 為中心的機器人輔助目視巡檢流程：線上 LiDAR-相機-慣性 SLAM（重新實作 LVI-SAM 架構，作者不主張其新穎性）、離線批次軌跡精修（以歐氏距離與 Scan Context 偵測迴圈）、與 SLAM 地圖解耦的巡檢點雲生成、影像缺陷分割（DIS-YOLO 與 SAM），以及把像素以加速射線追蹤投影到無序 LiDAR 點雲而不需建網格。實測對象為加拿大 Kitchener 的 Park Street 混凝土箱梁鐵路橋與 Duke Street 停車場，資料以 UGV 蒐集。作者明言缺乏軌跡真值，未評估漂移或 APE；地圖品質以自訂的平面點到平面厚度指標評估，尺寸精度以 7 個明確特徵對照現場量測，平均絕對誤差 3 cm（2.7%）；缺陷本身的尺寸沒有真值。","目標期刊中把 LiDAR-相機-慣性 SLAM 用於營運中民用基礎設施巡檢的研究：實測對象為加拿大 Kitchener 的一座混凝土箱梁鐵路橋與一座停車場（非施工中工地），另公開 8 處、18 筆紀錄的資料集。可補足營運中基礎設施巡檢的證據；在該補缺批次之前，同一證據叢集的已核實紀錄中屬基礎設施者僅 [hawley2022tunnelleakage]，其為鐵路隧道研究。作者相關博士論文（UWSpace，題名 Towards SLAM-Centric Inspection of Infrastructure，與本文不同）不作為本文證據。",[266,267,268,269,270],"Full offline refinement reduced the point-to-plane surface thickness from 4.164 to 3.647 cm (garage) and from 6.175 to 4.231 cm (bridge), with CloudCompare density rising accordingly (Table 7)","Seven proxy measurements of unambiguous features differed from on-site measurements by 3 cm (2.7%) on average, SD 2.3 cm (Sec. 5.5.3, Table 9)","Hardware time synchronization via microcontroller and documented sensor selection; base module about 958 g (Sec. 4.2-4.3)","Public dataset of 18 recordings, 3294 s, 327.12 Gb and about 84k images; the text says 8 locations (4 labs, 2 bridges, 1 garage, 1 open outdoor site) but Table 4 lists 7 location rows (Sec. 5.2, Table 4)","Authors report an average ray-tracing speed-up of 9000x over naive ray-tracing (Sec. 5.5.2); per-image ratios computable from Table 8 are about 390x, 1820x and 7040x (mean about 3080x), so the 9000x figure cannot be reproduced from Table 8 (inference from table values)",[272,273,274,275,276,277,278,279,280,281],"No ground truth for trajectories or defects; drift and absolute pose error were not evaluated (Sec. 5.5, Sec. 6)","Sec. 3 states the work relies on qualitative assessment to validate the approach because no benchmarks exist for inspection maps","Map quality is judged with a self-defined, reference-free point-to-plane metric on extracted planes (Sec. 5.5.1)","Measurement accuracy is validated on 7 proxy features, not on the defects; true in-field defect errors may be higher (Sec. 5.5.3)","Defect area assumes planar defects; defect-tracking accuracy after map alignment is not assessed (Sec. 3.3.2)","Reliable SLAM remains a challenge and the engineering burden is high (Sec. 6)","Table 9 row 3 is internally inconsistent (site 2.41 m and map 2.68 m, but abs error 0.08 m and 3.3%)","(inference) Both evaluated datasets used the UGV only, so the platform-agnostic claim rests on design arguments rather than cross-platform evaluation","Sec. 4.1 text swaps the camera descriptions relative to Table 1 (12.6 MP with 185 x 140 deg in the text vs Table 1: 12.3 MP inspection camera with a 50.8 x 38.6 deg lens); Table 1 is taken as authoritative","Sec. 5.5.3 links the Table 5 defect to measurements 4 and 5, but the matching dimensions correspond to measurements 2 and 3",{"id":283,"shortName":284,"title":285,"year":286,"fulltextStatus":10,"publicationStatus":11,"siteTypes":287,"taskLevel":14,"tasks":288,"platforms":289,"sensorTags":291,"reference":66,"referenceNote":293,"engineeringTask":294,"taskRequirement":27,"requirementSource":27,"siteCount":295,"independentValidation":296,"geometricQuality":297,"taskOutcome":298,"applicabilityClaim":299,"taskLimitations":300,"sourceLocator":301,"sensorsRaw":302,"platformRaw":306,"keyIdeaZh":307,"constructionRelevance":308,"strengths":309,"limitations":313},"chen2025quadrupedinspection","4D-BIM quadruped reality capture","Automated reality capture for indoor inspection using BIM and a multi-sensor quadruped robot",2025,[102],[238,62],[290],"legged",[21,292,23],"rgbd","重建以 10 站 TLS 為參考；定位真值由迴圈閉合與對 TLS 點雲的尺度對齊推得，部分來自受評資料本身，屬半獨立（推論；Sec. 4.1）。","indoor inspection reality capture (navigation, localization, reconstruction, object detection)","1 (HKUST academic building corridor)","TLS for reconstruction; localization GT semi-independent","mean Chamfer distance 0.119 m (SD 0.136 m) vs TLS; position drift 0.405 m over 92.77 m (71.77% below VINS-Mono, ~12% below RTAB-Map)","collision-free navigation; mAP 73.7% (2D) and 62.9% (3D)","authors claim suitability for indoor inspection; early-stage construction features (e.g., scaffolds) mentioned as assumption","completed occupied building; single corridor","Abstract; Sec. 3-4.2.1; Tables 3, 6",[303,304,305],"2D LiDAR (SLAMTEC RPLIDAR A2)","RGB-D camera (Intel RealSense D455)","IMU (camera built-in)",[290],"作者將 4D BIM（IFC）依施工時程、任務空間與元件外框轉為佔據網格，用於四足機器人的初始定位（AMCL）與路徑規劃。定位端以 IMU 重力方向校正四足行走造成的 2D 光達掃描傾斜，再以位姿圖融合雷射里程計與視覺慣性里程計；3D 重建則採 RGB-D 面元（surfel）建圖，並以深度學習偵測門與家具。實驗以地面雷射掃描作為重建參考。","於香港科技大學學術大樓內結合實驗室與辦公室的室內區域（含擁擠走廊）測試，BIM 為既有建築模型；非施工中工地（Sec. 4.1）。",[310,311,312],"position drift 0.405 m (0.44%) over 92.77 m, 71.77% lower than VINS-Mono baseline and ~12% lower than RTAB-Map (0.4587 m) (Table 7)","reconstruction mean Chamfer distance 0.119 m (SD 0.136 m) to TLS, 42.83% lower than RTAB-Map (reconstruction accuracy table)","collision-free navigation over a 92.77 m trip in 195 s (Table 6)",[314,315,316,317,318,319],"battery: about 20% (1000 mAh) per inspection trip, at most five trips per charge (Sec. 4.2.1)","assumes limited pitch, roll and z-displacement; scan rectification does not correct yaw drift (Sec. 3.2, Sec. 3.2.1)","step-wise navigation needed to avoid local optima or navigation failure (Sec. 4.2)","ceiling areas not reconstructed because of RGB-D range and illumination; point density lower than RTAB-Map after noise removal (Sec. 4.2.3)","path planning not optimized for legged motion; one small quadruped with custom sensors limits generalizability; map shifting expected in featureless spaces (Sec. 5)","(inference, not stated by the authors as a limitation) localization reference derived from loop closure and scale alignment to the TLS cloud, so it is not fully independent of the evaluated data (Sec. 4.1)",{"id":321,"shortName":322,"title":323,"year":234,"fulltextStatus":10,"publicationStatus":11,"siteTypes":324,"taskLevel":103,"tasks":325,"platforms":327,"sensorTags":328,"reference":216,"referenceNote":329,"engineeringTask":110,"taskRequirement":110,"requirementSource":110,"siteCount":110,"independentValidation":110,"geometricQuality":110,"taskOutcome":110,"applicabilityClaim":110,"taskLimitations":110,"sourceLocator":110,"sensorsRaw":330,"platformRaw":332,"keyIdeaZh":334,"constructionRelevance":335,"strengths":336,"limitations":341},"chowdhury2026_gema","GEMA","Video-driven Gaussian splatting for as-built building geometry with energy simulation",[102],[326],"asbuilt_modelling",[19],[22],"Building 1 至 3 的幾何與能耗比較基準是作者先前的 RENSA 模型，其餘建物只比對來源未說明的參考尺寸，沒有獨立量測（Sec. 4.2.1；Tables 3、4）。",[331],"monocular camera (drone video)",[333],"UAV","GEMA 將無人機環繞拍攝的影片以 COLMAP 求得相機位姿與稀疏點，再以 MiDaS 單目深度加密牆面與屋頂等低頻區域的初始點，接著用二維高斯潑濺（2DGS）最佳化，並以 Open3D TSDF 融合與 marching cubes 產生網格，最後經抽減、補洞與近垂直面校正，得到可供建築能源模擬的 LoD3 封閉外殼。三棟獨棟住宅的尺寸比例差異最大 20.47%，體積差異 0.99% 至 13.06%，逐時 CV(RMSE) 皆在 ASHRAE 建議的 30% 以內；但幾何與能耗的比較基準是作者先前的 RENSA 模型，並非獨立量測。此為離線影像重建流程，非 SLAM。","以無人機影片重建三棟獨棟住宅，以及雙併住宅、連棟住宅與公寓各一棟的外殼，並以夏洛特敦典型氣象年資料做 EnergyPlus 能源模擬作任務驗證。三棟獨棟住宅的幾何與能耗比較基準是作者先前的 RENSA 模型；其餘三棟只比對來源未說明的參考尺寸，且沒有能耗基準。論文未提及全測站或雷射掃描等獨立量測參考，尺寸比例差異最高約 20%。屬竣工外殼建模的邊界案例，非 SLAM。",[337,338,339,340],"Hourly CV(RMSE) versus ground-truth (RENSA) simulations of 16.97, 8.80 and 6.58% (Building 1), 12.07, 16.20 and 15.49% (Building 2), 23.27, 11.64 and 1.79% (Building 3) for heating, cooling and electricity, all within the ASHRAE 30% limit (Sec. 4.2.1)","Post-GEMA P-SSIM of 0.8806, 0.9032 and 0.9003 (Table 1)","Annual energy within about 0.5 to 12.5% of the reference under 2x and 4x input downscaling; densification kept the 480x270 Building 1 model simulatable (Table 5; Sec. 4.2.3)","Densification takes 2.3 to 5.8 s on an RTX 3060 (Table 2)",[342,343,344,345,346],"Largest dimensional-ratio deviation 20.47% (Rh\u002FL, Building 1); footprint area error up to 12.81% and volume error up to 13.06% for single-detached houses; up to 13.97% area and 13.69% volume error for other typologies (Tables 3 and 4)","Reference geometry for Buildings 1 to 3 and all reference energy results come from the authors' RENSA models, not utility data (Sec. 1; Sec. 4.2.1); Buildings 4 to 6 are compared with 'ground truth image' dimensions of undescribed origin and have no energy reference (Table 4; Sec. 4.2.1)","Degrades in multi-object scenes and sparse flight coverage; reflectance-driven façade artifacts and holes; strong dependence on COLMAP initialization quality (Sec. 5)","Not evaluated on complex typologies such as high-rises or curved façades (Sec. 5)","P-SSIM ignores 3D spatial locality and can score visually poor meshes highly (Sec. 4.2.3)",{"id":348,"shortName":349,"title":350,"year":286,"fulltextStatus":10,"publicationStatus":11,"siteTypes":351,"taskLevel":103,"tasks":352,"platforms":353,"sensorTags":354,"reference":241,"referenceNote":355,"engineeringTask":356,"taskRequirement":357,"requirementSource":27,"siteCount":358,"independentValidation":359,"geometricQuality":360,"taskOutcome":361,"applicabilityClaim":362,"taskLimitations":363,"sourceLocator":364,"sensorsRaw":365,"platformRaw":371,"keyIdeaZh":373,"constructionRelevance":374,"strengths":375,"limitations":382},"chung2025aspar","ASPAR","Automated system of scaffold point cloud data acquisition using a robot dog",[13,60],[16],[290],[21,23,292,109],"以熟練人員的人工掃描為比較基準計算覆蓋率，不是幾何精度參考。","scaffold point cloud acquisition (scan planning and execution)","coverage relative to manual TLS scanning by skilled workers (voxel count ratio, 0.05 m voxels, Eq. 8)","1 controlled outdoor site (Site F) for termination-criteria and manual-comparison trials + 1 large-scale construction site; 6 sites for detector training\u002Ftesting data","comparison with manual scanning by skilled workers","coverage rate (relative to manual scans)","number of scan positions; coverage 106.1% and 96.8%","authors claim real-world applicability on construction sites","geometric accuracy of SLAM map not the output; details not read","Abstract; Sec. 3; Table 2",[366,367,368,369,370],"3D LiDAR (Velodyne VLP-32)","IMU (Microstrain 3DM-GX5-AHRS)","IR depth camera (Azure Kinect)","TLS (FARO Focus M70) on robot","3D LiDAR (Ouster OS0-128), detector training and test data only",[372],"legged (Unitree Aliengo)","ASPAR 以四足機器人先自主探索工地並以 LIO-SAM 建立 3D SLAM 地圖，再把地圖投影為鳥瞰圖以 YOLOv8-OBB 即時偵測鷹架單元。系統依多層網格與貪婪法選定掃描站位並以 A*、旅行推銷員問題規劃路徑，最後在各站以機載 TLS 進行靜態掃描。此設計將 SLAM 用於規劃與導航，而最終幾何成果仍採用 TLS。","Site F 由作者描述為小型施工工地（Sec. 4.3.1）；Sec. 4.3.2 在同一場址改動部分鷹架位置並加入類似鷹架的障礙物後重複試驗，屬受控條件。另於一處大型施工工地測試（Sec. 4.3.3），作者致謝 Ssangyong Construction 提供戶外實驗工地。覆蓋率為相對人工掃描平均體素數的比值（0.05 m 體素，Eq. 8），不是絕對幾何精度；大型工地覆蓋率在摘要與內文寫 96.8%，Table 7 為 96.6%。",[376,377,378,379,380,381],"scaffold detector on BEV images: precision 0.985, recall 0.955, F1 0.971, mAP@0.5 0.985 on 100 site-F test images (Table 3)","all six scaffolds registered in five exploration runs; average precision 0.971, recall 1, F1 0.985 (Table 4)","average coverage 106.1 % of the manual mean with 5.4 vs 4.7 scan positions (Table 6)","large-scale site: coverage 96.6 % (Table 7; 96.8 % in text) with one extra scan position","detector trained on Ouster OS0-128 data worked on Velodyne VLP-32 field data (Sec. 4.3.1)","handles moving-object noise by refreshing the planning map with the latest raw scans (Sec. 3.2.1)",[383,384,385,386,387,388],"small or dynamic obstacles may block planned paths, requiring re-planning (Sec. 3.4)","TLS line of sight can be blocked by the mobile LiDAR, requiring robot rotation (Sec. 3.4)","obstacle detection based on height and density cannot detect non-obstacle hazards such as falling hazards or ground conditions (Sec. 4.4)","noise from movable obstacles in the SLAM map affects scan-position evaluation (Sec. 4.4)","repeated exploration gives slightly different SLAM maps and hence different scan plans (Sec. 4.4)","(from Table 6 and Table 7 values; not stated by the authors as a limitation) automated scanning took longer than manual scanning: 720 s vs 592 s on average, 1079 s vs 549 s at the large site",{"id":390,"shortName":391,"title":392,"year":234,"fulltextStatus":393,"publicationStatus":11,"siteTypes":394,"taskLevel":14,"tasks":396,"platforms":397,"sensorTags":399,"reference":27,"referenceNote":110,"engineeringTask":400,"taskRequirement":27,"requirementSource":27,"siteCount":27,"independentValidation":27,"geometricQuality":27,"taskOutcome":401,"applicabilityClaim":27,"taskLimitations":27,"sourceLocator":31,"sensorsRaw":402,"platformRaw":404,"keyIdeaZh":406,"constructionRelevance":407,"strengths":408,"limitations":412},"dulanto2026portablelio","Portable FAST-LIO2 site inspection","Enhancing Construction Site Inspection with a Portable LiDAR-Inertial Odometry-Based Mapping System for Cost-Effective Point-Cloud Integration with BIM and Digital Designs","abstract_only",[395],"not_verified",[238],[398],"portable",[21,23,292],"construction site inspection and BIM integration","user perception (usefulness, ease of use, adoption)",[77,79,403],"RGB-D camera",[405],"handheld","作者以 FAST-LIO2 建構手持式光達慣性建圖原型，並融合 RGB-D 相機即時產生彩色點雲，以低成本方式支援工地巡檢與 BIM 整合。初步評估以 36 位營建管理學生的使用者感受為主，並與商用系統做成本效益比較；摘要未報告幾何精度。","以營建工地巡檢為應用情境；摘要未說明是否在施工中工地做幾何驗證。",[409,410,411],"perceived usefulness 61%, ease of use 68%, adoption potential 86% among 36 construction management students (Abstract)","authors claim lower computational load than other state-of-the-art LIO systems (Abstract)","authors claim greater versatility and usability than stationary and drone-based LiDAR platforms in complex indoor environments, at significantly lower implementation cost (Abstract)",[413,414],"concerns about cost and workflow integration noted by participants (Abstract)","(inference) no geometric accuracy evidence in abstract",{"id":416,"shortName":417,"title":418,"year":419,"fulltextStatus":393,"publicationStatus":11,"siteTypes":420,"taskLevel":14,"tasks":422,"platforms":425,"sensorTags":426,"reference":66,"referenceNote":427,"engineeringTask":428,"taskRequirement":27,"requirementSource":429,"siteCount":27,"independentValidation":430,"geometricQuality":27,"taskOutcome":27,"applicabilityClaim":27,"taskLimitations":27,"sourceLocator":31,"sensorsRaw":431,"platformRaw":435,"keyIdeaZh":436,"constructionRelevance":437,"strengths":438,"limitations":440},"ellmann2022minesurvey","Handheld SLAM for mine surveys","Advancements in underground mine surveys by using SLAM-enabled handheld laser scanners",2022,[421],"underground_or_tunnel",[423,424],"underground_survey","accuracy_eval",[398],[215],"摘要稱以 TLS 資料驗證（僅讀摘要）。","underground mine survey of extracted surfaces","contemporary mine survey requirements (not identified)","TLS",[432,433,434],"two SLAM-enabled handheld laser scanners provided by Hades Geodeesia OÜ (models not stated on the abstract page)","TLS reference","conventional mine surveying (compared; instruments not stated on the abstract page)",[405],"作者評估手持 SLAM 掃描用於地下礦場測量與開採後表面 3D 建模，並以 TLS 資料驗證；典型差異在水平與垂直方向分別約 2 cm 與 5 cm 以內。作者也指出傳統礦場測量結果受測量人員主觀影響，認為 SLAM 手持掃描最適合地下礦場測量。","愛沙尼亞地下油頁岩礦開採後表面的測量情境（關鍵字 Oil shale mine、Estonia）；致謝提到國營企業 Eesti Energia 的主管與專家提供在 Estonia 地下礦改進測量方法的機會，兩台掃描儀由測量公司 Hades Geodeesia OÜ 提供；與地下工程出渣或超挖量測概念相近（推論）；非施工工地。",[439],"typical discrepancies within 2 cm (horizontal) and 5 cm (vertical) vs TLS (Abstract)",[],{"id":442,"shortName":443,"title":444,"year":419,"fulltextStatus":10,"publicationStatus":11,"siteTypes":445,"taskLevel":103,"tasks":446,"platforms":448,"sensorTags":450,"reference":66,"referenceNote":110,"engineeringTask":452,"taskRequirement":453,"requirementSource":454,"siteCount":455,"independentValidation":456,"geometricQuality":457,"taskOutcome":458,"applicabilityClaim":459,"taskLimitations":460,"sourceLocator":461,"sensorsRaw":462,"platformRaw":465,"keyIdeaZh":466,"constructionRelevance":467,"strengths":468,"limitations":476},"fahle2022geotechmls","SLAM MLS for geotechnical mine monitoring","Analysis of SLAM-Based Lidar Data Quality Metrics for Geotechnical Underground Monitoring",[421],[447,424],"deformation",[449,398],"vehicle",[451],"commercial_slam","geotechnical monitoring: convergence and rockfall detection","LoD 0.05 m wall-to-wall convergence; 0.1 m rockfall","derived by authors from cited geotechnical literature (Sec. 2.1)","2 mines","FARO static TLS; survey targets; field confirmation of convergence","absolute\u002Frelative trueness and precision (median, MAD), drift vs trajectory length, SLAM intrinsic\u002Fextrinsic precision","detection of simulated rockfall (down to 2.5 x 5 cm in structured scene) and mine convergence","SLAM MLS provides data quality required to detect geotechnically relevant changes","mining context; vendor black-box SLAM; later sections not fully read","Sec. 2-4.7",[463,464],"commercial SLAM MLS: Kaarta Stencil 2 (VLP-16 + MEMS IMU), Emesent Hovermap (rotating VLP-16 + MEMS IMU)","static TLS: FARO Focus S70, FARO Focus3D X330",[449,405],"作者在一座營運中塊狀崩落法礦場與科羅拉多礦業學院 Edgar 實驗礦，以 Kaarta Stencil 2 與 Emesent Hovermap 兩款 SLAM 行動掃描比對 FARO 靜態掃描，建立非參數（中位數、MAD）的絕對與相對精度、SLAM 內在、外在精度與密度覆蓋指標。結果顯示迴圈閉合可大幅降低漂移，以 SLAM 將新期資料配準到基準圖可取得遠優於絕對精度的相對精度，並實證以 3 cm 偵測門檻偵測岩塊掉落與最多約 10 cm 的收斂變形。","地下礦場（包含營運中礦場）岩盤監測；其「相對精度」與收斂偵測對隧道施工監測具參考價值，但非土木施工隧道。",[469,470,471,472,473,474,475],"target-level absolute trueness within the sensor's +-3 cm: maximum mean 1.58 cm and median 1.65 cm (Stencil 2); Hovermap mean 0.75 cm and median 0.72 cm (Sec. 4.4, Tables 7-8)","relative target-level accuracy with M3C2: mean magnitude 0.22 to 0.58 mm, MAD 2.9 to 6.7 mm (Sec. 4.5, Table 9)","loop closure reduced drift from up to 1.0 m horizontal and 0.5 m vertical to at most 0.25 m and under 5 cm over the first 240 m (Sec. 4.6, Fig. 10)","loop closure lowered intrinsic SLAM sigma from 7.11 cm to 2.86 cm (Table 6)","SLAM-based registration: median 0.4 cm, MAD 2.9 cm (Stencil 2 with Mine Vision Systems software, to static) and median 0.05 cm, MAD 1.36 cm (Hovermap to Hovermap, Emesent software) (Sec. 4.6)","MLS 30 s vs static 18 min for a 45 m section; about five times higher effective sampling rate (Sec. 3.3, Table 3)","MLS wall density about 8000 to 10,000 pts\u002Fm2 at 10 km\u002Fh with more uniform coverage than static scans (Sec. 4.3)",[477,478,479,480,481,482,483],"error distributions non-Gaussian with heavy outliers on rough surfaces (Sec. 4.1)","entry-aligned drift reached 0.18 m at 150 m (0.12% of trajectory); global ICP hides this drift; at 0.12% drift, segments longer than about 25 m exceed the 3 cm sensor bound (Sec. 4.4, Sec. 5.2)","opposite-direction passes gave larger discrepancies (MAD 10.30 cm) than same-direction passes (4.26 cm) (Sec. 4.5, Table 6)","C2M produced false positives from mesh gaps and normal orientation errors on rough surfaces; the smallest rock was not identified with C2C in the unstructured scene (Sec. 4.7, Figs. 13-14)","site-level overfitting is a concern where drifts shift globally in the rock mass (Sec. 5.2)","dynamic mine operations make repeatable survey trajectories difficult (Sec. 5.1)","more work needed on optimal collection and processing (Abstract)",{"id":485,"shortName":486,"title":487,"year":234,"fulltextStatus":10,"publicationStatus":11,"siteTypes":488,"taskLevel":14,"tasks":489,"platforms":490,"sensorTags":491,"reference":27,"referenceNote":492,"engineeringTask":493,"taskRequirement":27,"requirementSource":27,"siteCount":27,"independentValidation":27,"geometricQuality":27,"taskOutcome":27,"applicabilityClaim":27,"taskLimitations":27,"sourceLocator":31,"sensorsRaw":494,"platformRaw":498,"keyIdeaZh":501,"constructionRelevance":502,"strengths":503,"limitations":509},"feng2026integratedslam","Integrated LiDAR SLAM for public-building sites","Integration and evaluation of a 3D LiDAR SLAM system for construction robots in large-scale public building sites",[211,60],[63],[18,211],[21],"實地 ATE 的軌跡真值來源未說明；地圖尺寸誤差以施工圖尺寸為參考，不是獨立量測（Sec. 4、5）。","construction robot localization and mapping",[495,496,497],"3D LiDAR RS-Helios-16P (16 beams, 10 Hz, +-15 deg vertical FOV, +-2 cm ranging)","nine-axis IMU at 200 Hz (recorded; the evaluated system is LiDAR-only)","camera (recorded; model not reported; not used by the method)",[499,500],"wheeled UGV (Ackermann-steered construction robot base, teleoperated at 0.50 m\u002Fs on site)","simulation (Gazebo, robot moved at 1.00 m\u002Fs)","作者不提出新演算法，而是整合並依工地條件調整既有模組：兩階段地面分割（RANSAC 粗分割加法向量一致性精分割）取出樓板地面，快速歐幾里得分群（FEC）處理非地面點以抑制工人與機具等動態物，兩步配準以地面平面特徵估計 z、roll、pitch，再以非地面邊緣特徵估計 x、y、yaw，構成只用光達的里程計，最後以 Scan Context++ 迴圈偵測與位姿圖最佳化修正漂移。平台為搭載 RS-Helios-16P 16 線光達與 NVIDIA Jetson Xavier NX 的阿克曼轉向輪式機器人。以西安某醫院門診大樓的 Gazebo 模擬（1293 m）與同一棟施工中大樓的實地資料（1004 m）比較 F-LOAM 與 LeGO-LOAM，本方法 ATE RMSE 分別為 5.40 m 與 2.87 m，以圖面尺寸評估的實地地圖平均誤差為 0.79%；實地軌跡真值來源未說明。","實地測試在西安某醫院門診大樓的施工中工地，當時處於機電安裝與裝修階段，現場有工人、機具、鷹架、升降平台與推車，部分區域因地坪施工封閉；機器人以遙控方式行走 1004 m（2067 s）。模擬以同一棟大樓 CAD 圖建立 Gazebo 場景（標準層 292 m x 142 m x 6 m，行走 1293 m）。實地軌跡真值來源未說明；地圖尺寸以施工圖尺寸為參考，並非獨立量測（Sec. 4、Sec. 5）。",[504,505,506,507,508],"simulation ATE RMSE 5.40 m vs 12.33 m (LeGO-LOAM) and 20.27 m (F-LOAM) over 1293 m (Table 1)","real-site ATE RMSE 2.87 m vs 7.97 m (LeGO-LOAM) and 19.95 m (F-LOAM) over 1004 m (Table 5)","RMSE increased 10.00% with moving objects vs 19.95% (LeGO-LOAM) and 23.98% (F-LOAM) (Table 4)","map dimension error averaged 0.79% on site vs 2.33% (LeGO-LOAM) and 5.21% (F-LOAM) (Tables 6, 8)","runs on Jetson Xavier NX at 121.62 ms per frame, 7.20% slower than LeGO-LOAM (Table 8)",[510,511,512,513,514,515,516],"dynamic objects still degrade accuracy; semantic segmentation and object tracking left for future work (Sec. 4.2.4, Sec. 6)","simulation omits dust, lighting and reflectivity effects and cannot reproduce Ackermann turning and stop-and-go motion (Sec. 4)","real-site run limited to 1004 m by safety closures; teleoperated rather than autonomous (Sec. 5.1, Sec. 5.2.5)","higher computational load: 121.62 ms per frame vs 65.63 ms for F-LOAM (Table 8)","residual z-axis error remains after loop closure (Sec. 5.2.1)","no comparison with tightly coupled LiDAR-inertial methods; loop-closure false positive and negative rates not quantified (Sec. 4.2, Sec. 6)","(inference) real-site ground-truth source not described, and the proposed-method RMSE values in Tables 1, 4, 5 and 7 are inconsistent with their own mean and STD",{"id":518,"shortName":519,"title":520,"year":286,"fulltextStatus":10,"publicationStatus":11,"siteTypes":521,"taskLevel":14,"tasks":522,"platforms":523,"sensorTags":524,"reference":241,"referenceNote":525,"engineeringTask":526,"taskRequirement":27,"requirementSource":27,"siteCount":27,"independentValidation":27,"geometricQuality":27,"taskOutcome":27,"applicabilityClaim":27,"taskLimitations":27,"sourceLocator":31,"sensorsRaw":527,"platformRaw":531,"keyIdeaZh":533,"constructionRelevance":534,"strengths":535,"limitations":541},"gan2025decoupled","Quadruped decoupled mapping","Automated indoor 3D scene reconstruction with decoupled mapping using quadruped robot and LiDAR sensor",[102],[326,16],[290],[21,23],"以雷射測距儀量得的 5 組距離為真值（平均偏差 0.041 m）；TLS 的比較值引自前期研究，不是同次蒐集（推論；Sec. 4.2；Table 2）。","automated indoor as-built 3D reconstruction",[528,529,530],"3D LiDAR Ouster OS1-128-Rev-07 (360 deg horizontal x 45 deg vertical FOV), mounted at 0.5 m, used for 3D reconstruction","2D LiDAR for navigation mapping (text gives 'e.g., RPLIDAR'; exact model not reported)","odometry and IMU data used by Lidarslam_ros2 (IMU source not specified; Sec. 5 mentions the quadruped robot's IMU)",[532],"legged (quadruped robot; model not reported)","作者以四足機器人搭載 Ouster OS1-128 3D 光達與一具 2D 光達，採「解耦」配置：2D 光達以 ROS2 Gmapping 即時建立占據格網地圖供導航與避碰，3D 光達資料則在掃描後以 Lidarslam_ros2 離線建圖（掃描匹配前端搭配 IMU 預積分與失真校正，後端為具迴路偵測的位姿圖最佳化）。掃描規劃將 .stl 模型體素化後隨機產生視點，以射線投射處理部分遮蔽，再以貪婪法挑選視點並以 TSP 求最短路徑。在 144 m² 室內測試場選出 3 個視點、理論覆蓋率 95%，實際掃描 8 分鐘。以雷射測距儀為真值，5 組距離的平均偏差為 0.041 m，引自前期研究的 TLS 為 0.028 m；152 mm 網格覆蓋率 83.12%（TLS 85.35%），但 25 mm 與 13 mm 網格的覆蓋率遠低於 TLS，作者也指出點密度低於 TLS。耦合與解耦兩種策略僅作定性比較。","測試場為 144 m² 室內空間（含牆、柱、外牆面、家具與走動人員），並非施工中工地；作者認為可用於施工進度監測與設施管理，但未在工地驗證（Sec. 4.1）。",[536,537,538,539,540],"mean discrepancy 0.041 m to laser rangefinder over 5 distances vs 0.028 m for TLS from Zhai et al. 2024 (Table 2, Sec. 4.2)","152 mm grid coverage 83.12% vs TLS 85.35%; slabs 84.32% and 95.82% vs TLS 75.11% and 85.34%; Wall 1C captured where TLS had NIL (Table 3)","8 min automated scan plus 20 to 30 min mapping vs about 30 min TLS field work plus 30 min registration (Sec. 4.2)","3 viewpoints giving 95% theoretical coverage of the 144 m2 test site (Sec. 4.1)","all listed robotic densities exceed the 18.84 x10^-4 \u002Fmm2 minimum (Table 4)",[542,543,544,545,546],"robot deviates from planned scan positions because of IMU precision, coordinate conversion between .stl and Gmapping frames and collision avoidance (Sec. 5)","onboard system-on-module has limited memory and compute for large scenes (Sec. 4.2, 5)","coverage at 25 mm and 13 mm grids (12.20% and 3.60%) far below TLS (74.53% and 67.61%) (Table 3); point density lower than TLS, which the authors attribute to an algorithmic limitation that keeps the Ouster OS1 hardware from being fully used (Sec. 4.4, Table 4)","(inference) TLS baseline comes from a different study (Zhai et al. 2024), not collected in the same campaign","(inference) coupled vs decoupled strategies are compared only qualitatively (Table 1)",{"id":548,"shortName":549,"title":550,"year":234,"fulltextStatus":10,"publicationStatus":11,"siteTypes":551,"taskLevel":14,"tasks":552,"platforms":553,"sensorTags":554,"reference":241,"referenceNote":555,"engineeringTask":556,"taskRequirement":27,"requirementSource":27,"siteCount":27,"independentValidation":27,"geometricQuality":557,"taskOutcome":27,"applicabilityClaim":27,"taskLimitations":27,"sourceLocator":31,"sensorsRaw":558,"platformRaw":560,"keyIdeaZh":561,"constructionRelevance":562,"strengths":563,"limitations":569},"han2026nifcyl","NIFCyl tunnel deformation from SLAM LiDAR","Full-field deformation quantification of underground tunnels using SLAM LiDAR point cloud based on unsupervised neural implicit learning",[211,13,421],[447],[398],[451],"合成變形情境以已知變形為真值；現地紙箱試驗以尺量測比對，實際擴挖案例只比對單一點的人工量測；文中未見 TLS 或全測站參考（Sec. 4.3、4.4、4.5）。","tunnel deformation monitoring","R2, RMSE vs synthetic deformation; MAE vs M3C2 in field",[559],"handheld Hovermap ST LiDAR SLAM scanner (Table 1: FoV 360 x 290 deg, range 0.40 to 100 m, LiDAR accuracy +\u002F-30 mm, mapping accuracy +\u002F-15 mm in typical underground and indoor environments, SLAM drift +\u002F-0.03%, up to 300,000 pts\u002Fs single return and 600,000 pts\u002Fs dual return)",[405],"作者提出 NIFCyl：以 8 層 MLP 非監督學習參考點雲的有號距離場，取其梯度作為尺度不變且方向一致的法向，再沿法向以圓柱鄰域平均兩期點雲的投影位置，求得全場變形，不需標註資料或局部 PCA 擬合。資料以手持 Hovermap ST 在西澳 Kalgoorlie 地下硬岩礦取得，兩期點雲以四個噴漆控制點做 SVD 粗配準，再以 ICP 細配準。合成變形情境中 NIFCyl 的 R² 為 0.963、RMSE 0.009 m、總計算 187 秒，三種尺度設定的 M3C2 中最佳者為 0.955、0.010 m、995 秒；現地放置紙箱的試驗中，平面區兩者 MAE 同為 0.022 m，曲面區為 0.032 m 對 0.034 m。實際擴挖案例只以單一點的人工量測（2.513 m）與模型結果（2.505 m）比對。","西澳 Kalgoorlie 地下硬岩礦的鑽炸巷道（岩面粗糙，鋼網與岩栓支撐），含相隔兩個月、因撬落浮石（scaling）造成約 2 m 以上擴挖的實例；與隧道開挖變形監測高度相關，但屬礦業巷道而非土木隧道施工（Sec. 2.2, 4.5）。",[564,565,566,567,568],"synthetic: R2 0.963 and RMSE 0.009 m vs best M3C2 (0.05 to 0.55 m scale) 0.955 and 0.010 m; 187 s vs 995 s (Table 4)","curved field target MAE 0.032 m vs 0.034 m for M3C2 (Sec. 4.4)","robust to 20% point removal (R2 0.956, RMSE 0.010 m) and to noise at 10% of deformation (R2 0.938, RMSE 0.012 m) (Table 5)","noise floor in stable regions: sigma 0.0035 m short-term field, 0.0151 m over two months (Table 6)","code and datasets shared on GitHub (Data availability)",[570,571,572,573,574,575,576],"evaluation relies on a limited set of multi-temporal scans from one mine (Sec. 4.8)","ICP-based registration contributes systemic noise; real-scenario detection threshold set at 3.0 cm (Sec. 4.7, 4.8)","tunnel treated as a single entity without semantic segmentation (Sec. 4.8)","for tunnels longer than about 60 m the authors recommend splitting the cloud into sections (sliding window) because a single global volume causes memory and time bottlenecks (Sec. 4.3)","planar field target shows no gain over M3C2 (both MAE 0.022 m) (Sec. 4.4)","real-deformation case checked only qualitatively plus one manual point-to-point measurement (Sec. 4.5)","at noise 20% of deformation R2 drops to 0.858 and RMSE rises to 0.018 m (Table 5)",{"id":578,"shortName":579,"title":580,"year":419,"fulltextStatus":10,"publicationStatus":11,"siteTypes":581,"taskLevel":14,"tasks":582,"platforms":583,"sensorTags":584,"reference":24,"referenceNote":585,"engineeringTask":586,"taskRequirement":27,"requirementSource":27,"siteCount":587,"independentValidation":588,"geometricQuality":589,"taskOutcome":590,"applicabilityClaim":591,"taskLimitations":592,"sourceLocator":593,"sensorsRaw":594,"platformRaw":596,"keyIdeaZh":599,"constructionRelevance":600,"strengths":601,"limitations":606},"hawley2022tunnelleakage","Hovermap intensity-based tunnel leakage mapping","Water leakage mapping in concrete railway tunnels using LiDAR generated point clouds",[236,13],[238],[398],[451],"萃取結果僅以 RGB 影像目視比對。","water leakage mapping in railway tunnels","2 tunnel sections of 50 m (DB and TBM)","qualitative only (RGB images)","range standard deviation for coloured targets (Table 1); no geometric accuracy of tunnel clouds","leakage areas summarized for the 50 m DB section (Table 2); qualitative visual agreement with RGB images","authors state the workflow extracts leakages quickly and efficiently","no quantitative leakage ground truth; intensity depends on range, incidence and colour","Sec. 4-5, 7; Tables 1-2",[595],"commercial SLAM scanner Emesent Hovermap with rotating Velodyne VLP-16 puck (360 deg FoV, 905 nm); IMU and SLAM algorithm not described in the article",[597,598],"handheld (field scans walked at 0.5 m\u002Fs along the track centre)","static placement for laboratory target scans (30 s each)","作者先在實驗室以 Emesent Hovermap（內含旋轉式 Velodyne VLP-16，905 nm）掃描 12 種顏色光澤的平滑與粗糙 MDF 靶，以及兩種骨材、三種粗糙度的混凝土靶，距離 2、4、6 m、入射角 0° 至 45°，量測回波強度與相對最佳擬合平面的測距標準差；飽和混凝土的強度平均比乾燥時低 70% 至 80%。現地在南非一條快速鐵路（依碩士論文為 Gautrain）兩段各 50 m 的鑽炸與 TBM 隧道步行掃描，以 Maptek PointStudio 自動篩出低強度點並產生滲水區域 3D 面：鑽炸段萃取出 11 處滲水並列出位置與面積，TBM 段未見滲水。驗證只以 RGB 影像目視比對，且在修補區誤判兩小塊為滲水。","營運中快速鐵路隧道（碩士論文指明為約翰尼斯堡 Gautrain）之維護檢測，兩段 50 m（鑽炸與 TBM）；屬既有基礎設施而非施工中隧道（Sec. 5.1）。",[602,603,604,605],"saturated concrete targets showed 70 to 80% lower intensity than dry; dry targets above 27, saturated at or below 11 (Sec. 4.4, 7)","range SD against best-fit plane stayed between 9.2 and 13.0 mm for all non-black targets up to 6 m (Table 1)","ambient lighting changed intensity by 3 units or less (Sec. 4.3)","11 leakage areas with location and area extracted in the 50 m DB section; extracted surfaces showed good visual agreement with RGB images (Table 2, Sec. 5.2, 7)",[607,608,609,610,611],"two small false leakage detections on patch repairs (Sec. 5.2, Fig. 18c, Sec. 7)","validation only visual against RGB images; no quantitative leakage ground truth or hydraulic head information (Sec. 5.2)","black targets gave larger range noise, up to +\u002F-26.9 mm SD (Table 1)","only one walking speed tested and two 50 m sections; longer sections and multi-epoch linking left to future work (Sec. 5.1, 7)","(inference) intensity depends on range, incidence and surface colour, requiring calibration per device",{"id":613,"shortName":614,"title":615,"year":616,"fulltextStatus":10,"publicationStatus":11,"siteTypes":617,"taskLevel":14,"tasks":618,"platforms":620,"sensorTags":621,"reference":241,"referenceNote":622,"engineeringTask":623,"taskRequirement":27,"requirementSource":27,"siteCount":395,"independentValidation":395,"geometricQuality":395,"taskOutcome":395,"applicabilityClaim":624,"taskLimitations":395,"sourceLocator":625,"sensorsRaw":626,"platformRaw":628,"keyIdeaZh":630,"constructionRelevance":631,"strengths":632,"limitations":636},"hsieh2023slamarbim","SLAM-based AR + BIM on-site progress","On-site Visual Construction Management System Based on the Integration of SLAM-based AR and BIM on a Handheld Device",2023,[60],[619],"progress",[398],[22,23],"只在移動速率試驗中，以預先標記刻度的實體圓柱量測虛實物件中心的距離；沒有軌跡或點雲的參考量測（Sec. 4.2.2）。","on-site AR visualization of construction progress against the BIM schedule","improved BIM overlay positioning on construction sites (abstract)","abstract",[627],"camera and motion sensors of an iOS handheld device accessed through Apple ARKit (visual-inertial; device model not reported)",[629],"handheld (iOS mobile device; model not reported)","作者在 iOS 手持裝置上以 Apple ARKit 內建的 SLAM 將 BIM 疊合於施工現場，並在一棟地下一層至地上六樓、樓地板面積 280 m² 的 RC 施工中建築實測，發現掃描路徑中斷、移動過快造成影像模糊、施工中表面不平，以及現場材料與設備變動，都會使模型疊合偏移。作者因此提出操作程序：沿地面連續掃描的封閉路徑並在角落設校正參考物、平均移動速率約 0.25 至 0.5 m\u002Fs 並以特徵點數提示、以整平的動態參考平面作為 BIM 放置基準，並每 1 至 3 個工作天重掃特徵點地圖。單次移動速率試驗中，1.00 m\u002Fs 時虛實物件中心相距 7.9 cm，0.5 m\u002Fs 以下為 4.3 至 5.2 cm。系統另以顏色在 AR 中顯示構件進度落後、如期或超前。","在一棟地下一層至地上六樓的 RC 施工中建築實測（樓地板面積 280 m²，原文未說明是否為每層），指出鋼筋綁紮、模板等不同施工階段的表面不連續、材料堆放與設備移動會使 ARKit 特徵點地圖失效；屬相機式 AR 定位，不產生稠密點雲（Sec. 4, 7）。",[633,634,635],"virtual-to-physical centre offset 4.3 to 5.2 cm at 0.50 m\u002Fs or slower vs 7.9 cm at 1.00 m\u002Fs (Table 2)","no positioning hardware needs to be mounted on site; relies on ARKit feature maps (Sec. 2)","five AR progress scenarios demonstrated on site (Sec. 7)",[637,638,639,640],"feature-point mapping affected by poor lighting and occlusion (Sec. 8)","effectiveness on large projects and integration with project management software not tested (Sec. 8)","feature-point map becomes invalid after about 1 to 3 working days of site change and must be rescanned (Sec. 4.2.4)","(inference) accuracy evidence limited to one movement-rate test with a single measurement per rate; no before-and-after comparison of the proposed procedure",{"id":642,"shortName":643,"title":644,"year":616,"fulltextStatus":10,"publicationStatus":11,"siteTypes":645,"taskLevel":14,"tasks":646,"platforms":647,"sensorTags":648,"reference":24,"referenceNote":649,"engineeringTask":650,"taskRequirement":651,"requirementSource":652,"siteCount":653,"independentValidation":654,"geometricQuality":655,"taskOutcome":656,"applicabilityClaim":657,"taskLimitations":658,"sourceLocator":659,"sensorsRaw":660,"platformRaw":663,"keyIdeaZh":666,"constructionRelevance":667,"strengths":668,"limitations":673},"hu2023robotassisted","Legged robot + solid-state LiDAR scan-to-BIM","Robot-assisted mobile scanning for automated 3D reconstruction and point cloud semantic segmentation of building interiors",[102],[326,16],[290,18],[292,21],"全文沒有 TLS 或測量參考；輪式平台只用於改用 RTAB-Map 的對照情境（Sec. 4.4）。","as-built interior reconstruction and semantic segmentation for scan-to-BIM","minimum point spacing thresholds s_v,lim = s_h,lim = 0.013 (units as in Table 3) from cited data quality requirements","cited point data quality requirements [53] (not verified)","1 test bed (NUS 5G Centre) + 6 training scenes","none found (no TLS or survey reference in full text)","completeness (orthographic rasterization) and minimum point density per component (Table 5); no absolute accuracy","segmentation mIoU 81.75%","authors claim efficient collection of high-quality point clouds for building interiors","completed buildings only; accuracy vs reference not verified","Abstract; Sec. 3-4.1; Tables 1-3",[661,662],"solid-state LiDAR RGB-D camera Intel RealSense L515 (HFOV 70 deg, VFOV 50 deg, angular resolution 0.07 deg)","2D LiDAR Slamtec Mapper (navigation)",[664,665],"legged (Unitree Go1)","wheeled UGV in the comparative scenario (model not reported)","作者以 Unitree Go1 四足機器人搭載 Intel RealSense L515 固態光達（RGB-D）與 Slamtec Mapper 2D 光達掃描室內：以結合覆蓋、點密度與障礙項的掃描適應度指標及網格逐方向（GBDD）演算法求取掃描站位，Hector 建圖、AMCL 定位、A* 規劃路徑，並以改良動態視窗法先轉向目標牆再平移，避免感測器朝向特徵稀少區而使 SLAM 追蹤失敗。RGB-D 影像送入 Dot3D 軟體以 SLAM 重建點雲，再以 ResPointNet++ 分割為牆、地板、柱、桌、椅與雜物六類。新加坡國立大學 5G Centre 試驗中 mIoU 為 81.75%；與改用 UGV 搭配 RTAB-Map 且無運動整合的對照情境相比，牆與地板完整度為 72.05% 與 47.26%（對照為 61.05% 與 30.86%），但全文沒有 TLS 或測量的幾何參考。","測試於新加坡國立大學 5G Centre 室內（既有建築）；訓練資料為教室、實驗室與辦公室（Table 1），非施工中工地。",[669,670,671,672],"overall mIoU 81.75%; IoU wall 90.33%, floor 93.36%, column 88.40%, table 87.17% (Table 4)","completeness wall 72.05% and floor 47.26% vs 61.05% and 30.86% for the comparative scenario (Table 5)","minimum densities column 0.001501, floor 0.01664, wall 0.005396 per mm2 vs 0.000829, 0.00143, 0.002773 for the comparative scenario (Table 5)","with the enhanced DWA SLAM mapping ran smoothly, while the conventional DWA made mapping fail between positions 3 and 4 (Sec. 4.1)",[674,675,676,677,678,679,680],"improper robot motion can cause point cloud mismatching due to limited sensor FoV (Sec. 1, Fig. 1)","only visual odometry used for LiDAR pose estimation; sensitive to lighting, blur and shadows (Sec. 5)","no independent geometric reference (TLS or survey) is used in the full text","column minimum density 0.0015 \u002Fmm2 below the GSA 0.00188 \u002Fmm2 requirement in poorly lit bottom regions (Sec. 4.2)","floor completeness below 50% because clutter blocked robot access; table tops not scanned due to sensor height (Sec. 4.2)","chair IoU only 62.55%, attributed to training and test chair geometry mismatch; segmentation needs fully labelled custom data (Sec. 4.3, 5)","(inference) the 'ablation' comparator changed platform (UGV), SLAM package (RTAB-Map) and motion integration at once, so gains cannot be attributed to motion-SLAM integration alone",{"id":682,"shortName":683,"title":684,"year":685,"fulltextStatus":10,"publicationStatus":11,"siteTypes":686,"taskLevel":14,"tasks":687,"platforms":688,"sensorTags":689,"reference":216,"referenceNote":690,"engineeringTask":691,"taskRequirement":27,"requirementSource":27,"siteCount":27,"independentValidation":27,"geometricQuality":27,"taskOutcome":27,"applicabilityClaim":27,"taskLimitations":27,"sourceLocator":31,"sensorsRaw":692,"platformRaw":695,"keyIdeaZh":697,"constructionRelevance":698,"strengths":699,"limitations":703},"ibrahim2019bimugv","BIM-driven UGV indoor progress mapping","BIM-driven mission planning and navigation for automatic indoor construction progress detection using robotic ground platform",2019,[13,60],[619,16],[18],[21,23],"點雲距離誤差（平均 0.51 m）以手動配準的 BIM 為參考，不是獨立量測；作者也指出手動配準降低了量得的精度（Experimental Setup and Results）。","indoor construction progress data collection",[693,694],"two orthogonal 2D LiDARs (horizontal, 16 m range, for Hector SLAM mapping and navigation; vertical, 10 m range, for cross-section scans; models not reported)","wheel odometer and IMU of the Clearpath Jackal",[696],"wheeled UGV (Clearpath Jackal)","作者以 Clearpath Jackal 地面機器人搭載兩具正交 2D 光達：水平者（16 m）以 Hector SLAM 建立占據格網並定位，垂直者（10 m）掃描斷面，再依時間戳記內插位姿累積成 3D 點雲，並在航點原地旋轉 360°。航點依 4D BIM 中預期有進度的構件，在 2D 平面圖上手動放置，再以對應點把平面圖配準到事先手動駕駛建立的初始地圖，之後以 A* 自動導航並閃避動態障礙物。實驗室測試與一棟住宅建築室內隔間施工階段的二樓實測（10 戶、20 個航點）中，點雲平均密度 6.85 pts\u002Fm²，相對手動配準 BIM 的平均距離誤差 0.51 m（標準差 0.65 m、中位數 0.3 m）；作者歸因於低價 2D 光達、航點配置、SLAM 漂移與手動配準。","在一棟建築室內隔間施工階段的二樓實測（長走廊連接 10 戶住宅單元、20 個航點），於下班後蒐集以避免干擾施工；另在模擬施工雜亂的結構實驗室測試。幾何品質以手動配準的 BIM 為參考，平均誤差 0.51 m，距離施工進度判定所需精度仍有差距（推論）。",[700,701,702],"safe indoor navigation with dynamic obstacle avoidance demonstrated in the lab (Experimental Setup and Results, Conclusions)","Hector SLAM scaled to mapping large indoor construction spaces and its occupancy map supported autonomous navigation (Conclusions)","higher point density associated with better accuracy (Fig. 8)",[704,705,706,707,708],"low resolution and accuracy of the low-cost 2D LiDAR (Experimental Setup and Results)","few, manually placed waypoints gave poor coverage; mid-room waypoints lowered density on far walls","SLAM drift caused incorrect registration of some local points","manual registration of the cloud to BIM lowered measured accuracy","initial occupancy map must be created by manual driving; site data were collected after working hours",{"id":710,"shortName":711,"title":712,"year":286,"fulltextStatus":10,"publicationStatus":11,"siteTypes":713,"taskLevel":103,"tasks":714,"platforms":715,"sensorTags":716,"reference":27,"referenceNote":717,"engineeringTask":110,"taskRequirement":110,"requirementSource":110,"siteCount":110,"independentValidation":110,"geometricQuality":110,"taskOutcome":110,"applicabilityClaim":110,"taskLimitations":110,"sourceLocator":110,"sensorsRaw":718,"platformRaw":722,"keyIdeaZh":723,"constructionRelevance":724,"strengths":725,"limitations":730},"jeon2025_nerf_construction","NeRF-BIM progress evaluation","Neural radiance fields for construction site scene representation and progress evaluation with BIM",[60],[619],[398,19],[22],"以 BIM 自動產生的遮罩為分割與尺寸的真值，NeRF 與 BIM 以現場棋盤格對位，屬任務層驗證，沒有點雲幾何的獨立參考（Sec. 3.2.1、3.3.2、4.3）。",[719,720,721],"smartphone camera video (iPhone 15 Pro, 1080 x 1920)","UAV camera video (DJI Mini 2 Pro as written, 1920 x 1080)","iPhone 15 Pro flash LiDAR with Pix4D apps (qualitative comparison only)",[405,333],"本研究以 iPhone 15 Pro 手機與 DJI 無人機拍攝的工地影片，先以 Pix4D 雲端 SfM 估計每張影格的位姿，再用 Nerfstudio 訓練 Instant-NGP。透過在現場與 BIM 相同位置放置棋盤格，求出 NeRF 與 BIM 之間的 4x4 轉換，並在 NVIDIA Omniverse Kit 依構件類型自動規劃距構件 2 m 的正交視角、由 BIM 自動產生真值遮罩，再以 SAM 點提示分割計算 IoU。完成澆置的場景中，構件分割平均絕對誤差為 8.7，牆、柱、梁的加權尺寸 RMSE 分別為 0.97%、0.85%、2.21%；模板組立中的場景，進度追蹤平均絕對誤差為 5.7。","已在韓國密陽市一處教育中心工地的混凝土澆置流程實測（完成澆置、模板組立、鋼筋綁紮三個場景），屬營建任務層級驗證。位姿來自 Pix4D 離線 SfM，尺度與座標由現場與 BIM 相同位置的棋盤格對位取得，並非 SLAM 系統。尺寸誤差由遮罩像素換算，受 NeRF 與 BIM 對位精度影響，作者估計在最差情況下，z 向對位誤差不超過約 ±4 cm、x 或 y 向不超過約 ±6 cm 時，追蹤誤差可維持在 10% 以內。",[726,727,728,729],"Per-class mean wRMSE of 0.97% (wall), 0.85% (pillar) and 2.21% (beam), mean 1.34%, in the completed scene_1; these are the values behind the abstract's '1% to 2.2%' (Table 5, Sec. 4.3.2)","Mean segmentation AE of 8.7 IoU points in scene_1 and mean progress-tracking AE of 5.7 in the ongoing scene_2 (Tables 5, 7)","BIM-generated ground-truth masks remove manual labelling for progress tracking (Sec. 3.3.2, Sec. 4.3.1)","NeRF renders tall walls and columns more completely than iPhone flash LiDAR or photogrammetry point clouds (qualitative, Sec. 4.2.2, Fig. 19)",[731,732,733,734,735,736],"NeRF rendering artefacts with moving objects or complex lighting affect evaluation; data were captured after work hours to avoid workers (Sec. 5, Sec. 6)","Inaccurate NeRF-BIM synchronization degrades IoU-based tracking and measurement; a 2 cm shift changes IoU by 0.03 (pillar, x or y) or 0.05 (wall, z), so about +-6 cm (x, y) or +-4 cm (z) keeps worst-case tracking error within 10% (Sec. 4.3.2, Sec. 6)","Alignment relies on checkerboards installed on site and replicated in BIM (Sec. 3.2.1, Sec. 6)","BIM holds only the final poured state, so formwork and rebar scenes were evaluated mainly by element counting; segmentation accuracy of incomplete elements needed manual labels (Sec. 4.2.1, Sec. 4.3.1)","Beams are less visible in the videos and show the largest errors (mAE 14.6, mwRMSE 2.21%) (Sec. 4.3.2, Table 5)","Rendering quality drops for the large outdoor UAV scene (site3_drone PSNR 19.27, SSIM 0.509) (Table 3)",{"id":738,"shortName":739,"title":740,"year":616,"fulltextStatus":10,"publicationStatus":11,"siteTypes":741,"taskLevel":14,"tasks":742,"platforms":743,"sensorTags":744,"reference":66,"referenceNote":110,"engineeringTask":745,"taskRequirement":746,"requirementSource":747,"siteCount":748,"independentValidation":749,"geometricQuality":750,"taskOutcome":751,"applicabilityClaim":752,"taskLimitations":753,"sourceLocator":754,"sensorsRaw":755,"platformRaw":757,"keyIdeaZh":758,"constructionRelevance":759,"strengths":760,"limitations":764},"keitaanniemi2023drift","ZEB-REVO drift and sectional post-processing","Drift analysis and sectional post-processing of indoor simultaneous localization and mapping (SLAM)-based laser scanning data",[102],[424,619],[398],[451],"multitemporal construction documentation \u002F change detection readiness","2 cm threshold used to flag drift-increasing locations (Table 4); no project tolerance","author-defined analysis threshold","1 building (~950 m2, 2 floors + outdoor strip)","Leica RTC360 TLS, 63 scans, 24 spheres, 3 mm absolute mean error","M3C2 mean and std on planar surfaces per section; section discontinuities (Table 3)","not_reported (no change-detection experiment)","method has potential to improve quality and cost-effectiveness of multitemporal construction documentation","single device, single run, completed building; correction depends on TLS reference","Sec. 3-5; Tables 2-4",[756],"2D rotating LiDAR + IMU in commercial handheld SLAM scanner (GeoSLAM ZEB-REVO)",[405],"作者以商用手持 SLAM 掃描儀（GeoSLAM ZEB-REVO）單次 10 分鐘、約 440 m 的封閉路徑掃描一棟 1960 年代四層校舍，並以 63 站 Leica RTC360 地面掃描（絕對平均誤差 3 mm）作為參考。以 M3C2 比較三個平面後發現漂移主要在垂直方向，且累積於距起終點最遠的軌跡中段（最大 10.6 cm）。作者以時間戳分段並對 TLS 參考小區塊做剛性或非剛性套合，將漂移降至約 0.2 cm，並依窄門、窄樓梯、小半徑內迴圈與室內外轉換等位置提出控制點配置建議。","目標為多時期施工紀錄，但實驗在既有校舍（完工建築）進行；作者指出漂移會在變更偵測中造成假變化（Sec. 1, 3.1.1）。",[761,762,763],"drift reduced from 10.6 cm to 0.2 cm (Abstract)","~30 s sections with non-rigid transformation gave better results than larger sections (Abstract)","vertical-plane mean distances already \u003C0.5 cm before post-processing (Fig. 11, Sec. 5.2)",[765,766,767,768,769],"drift accumulates at small-diameter internal loops in narrow hallways, narrow doorways, narrow staircases and outdoor transitions (Table 4)","post-processing requires reference (TLS) patches; small sections can slightly worsen already-good directions and raise std by 0.2-0.3 cm (Sec. 5.2)","commercial SLAM algorithms are black boxes to users (Sec. 1)","authors state that a workflow needing both a SLAM cloud and a more accurate TLS cloud is not an optimal way to post-process SLAM data (Sec. 5.2)","single device, single run in one building; repeatability in other environments left to future work (Sec. 6)",{"id":771,"shortName":772,"title":773,"year":774,"fulltextStatus":10,"publicationStatus":11,"siteTypes":775,"taskLevel":14,"tasks":776,"platforms":777,"sensorTags":778,"reference":241,"referenceNote":779,"engineeringTask":780,"taskRequirement":27,"requirementSource":27,"siteCount":27,"independentValidation":27,"geometricQuality":27,"taskOutcome":27,"applicabilityClaim":27,"taskLimitations":27,"sourceLocator":31,"sensorsRaw":781,"platformRaw":784,"keyIdeaZh":785,"constructionRelevance":786,"strengths":787,"limitations":790},"kelly2024blk2go","BLK2GO accuracy and drift","Assessment of Slam Lidar - An Accuracy Assessment and Drift Anaylsis of the Leica BLK2GO",2024,[102,13],[424],[398],[451],"以 Trimble SX10 量得的距離為參考：靜態測試為掃描儀原點至標靶，移動測試為牆面標靶之間的距離；只比較距離（Sec. 3.2、3.3；Table 3）。","instrument accuracy assessment",[782,783],"LiDAR in Leica BLK2GO (830 nm; FOV 360 deg h x 270 deg v; range 0.5 to 25 m; 420,000 pts\u002Fs)","3-camera panoramic vision system in BLK2GO (4.8 Mpixel, 300 x 135 deg, global shutter) used for visual SLAM",[405],"作者在美國西點軍校 Washington Hall 學術建築評估 Leica BLK2GO 手持視覺加光達 SLAM 掃描儀。靜態測試以 Trimble SX10 量得掃描儀原點至約 2 m 與 15 m 標靶的距離，連續八次各五分鐘收集，RMSE 分別為 0.001 m 與 0.021 m，且未見隨機身溫度上升而變化的測距漂移。移動測試以五、六樓牆面標靶間距離與 SX10 比較，九種路線條件（開放、單圈、雙圈；單層或兩層；視覺加光達或僅光達）的 RMSE 為 0.033 至 0.128 m，並與累積行走距離呈現可能顯著的線性相關（R² 0.63 至 0.80）。作者建議以尺度因子校正並反向重走路徑。","一般測繪儀器評估，於既有學術建築（美國西點軍校 Washington Hall 五、六樓）室內進行，未涉及施工中工地。行走距離越長誤差越大的結果，對大範圍室內竣工量測的控制點與路線規劃有參考價值（推論）。",[788,789],"stationary range RMSE 0.001 m at the ~2 m target and 0.021 m at the ~15 m target over eight 5-min collections, no range walk despite vent temperature rising from 22.3 to 45.0 deg C (Sec. 4.1)","error did not grow between consecutive targets or with target offset (Sec. 4.2, Fig. 3)",[791,792,793,794],"inter-target distance RMSE 0.033-0.128 m across nine circuits, largest (0.128 m) for the single two-floor circuit (Table 3)","RMSE correlates linearly with cumulative distance travelled (R2 0.63-0.80); scale factor and reverse-path repetition suggested (Sec. 4.2, Sec. 5)","failures to initialise, mostly with visual SLAM disabled; mid-scan orientation and alignment shifts only in multi-loop multi-floor circuits (Sec. 4.3)","repeating a path can push error beyond the SLAM correction bounds and cause catastrophic misalignment that is hard to fix without timestamps (Sec. 5)",{"id":796,"shortName":797,"title":798,"year":209,"fulltextStatus":10,"publicationStatus":11,"siteTypes":799,"taskLevel":14,"tasks":800,"platforms":802,"sensorTags":803,"reference":216,"referenceNote":804,"engineeringTask":110,"taskRequirement":110,"requirementSource":110,"siteCount":110,"independentValidation":110,"geometricQuality":110,"taskOutcome":110,"applicabilityClaim":110,"taskLimitations":110,"sourceLocator":110,"sensorsRaw":805,"platformRaw":808,"keyIdeaZh":810,"constructionRelevance":811,"strengths":812,"limitations":817},"kim2018construction","Visual+planar registration for construction","Automated Point Cloud Registration Using Visual and Planar Features for Construction Environments",[60],[801],"registration",[190],[21,22],"配準 RMSE 以編號較高的另一站掃描為基準計算，不是獨立量測（Results；Table 6）。",[806,807],"robotic hybrid 3D LiDAR system with four SICK 2D line laser scanners (80 m working range, 25 Hz scan speed, 200 s per 360 deg scan, 190 deg vertical line; 0.1667 deg vertical and 0.072 deg horizontal resolution; SICK model not stated)","regular digital camera on the scanner, eight images per 360 deg scan, used for RGB texture mapping through the mounting kinematics and a pinhole model",[809],"customized robotic hybrid LiDAR system whose base frame is a mobile robot platform; 360 deg scans (200 s each) at 6, 3 and 2 scan positions in the three testbeds","作者以自製的機器人式混合 LiDAR（四部 SICK 二維線雷射加一部數位相機）取得帶 RGB 紋理的點雲，先在相機影像上以 SURF 特徵配合 RANSAC 找出跨站對應，經紋理對應轉成三維點後以 Kabsch 法做初始對齊；再以 k-d 樹計算重疊率，重疊約 89% 以上時用點對點 LM-ICP，否則以 RANSAC 分割出三個最大且線性獨立的平面，由法向量求旋轉、由三平面交點求平移。在 Georgia Tech 校園的營建工地、建物旁與室內三個測試場，最終 RMSE 分別為 0.198 m、0.183 m 與 0.047 m（Table 6）；誤差以另一站掃描為基準，並非獨立量測。","資料取自 Georgia Tech 校園：Testbed #1 為營建工地（6 個掃描站），Testbed #2 在一棟目標建物旁（3 站），Testbed #3 為室內環境（2 站）（Results）。方法針對大型工地低重疊、長站距的多站靜態掃描，Testbed #1 只用其中 3 站即完成配準。誤差以編號較高的掃描為基準計算，不是獨立量測；施工階段未說明。",[813,814,815,816],"at 45% to 70% overlap and 24 m to 43 m scan spacing ICP RMSE rose to 2.265 to 4.293 m while plane matching stayed at 0.092 to 0.132 m (Table 2)","Testbed #1 registered with three of six scans (#1, #4, #6), reducing the number of scans needed (Table 6)","final RMSE 0.198 m, 0.183 m and 0.047 m for Testbeds #1, #2 and #3 (Table 6)","no artificial targets or manual alignment (Abstract, Discussion)",[818,819,820,821,822],"requires three planes with one intersection point in the overlapped area (Discussion and Conclusion)","only the single set of three largest planes and one corner point is used for fine registration (Final Alignment section)","ICP outperforms plane matching when overlap exceeds about 89% and scan distance is below about 10 m (Fig. 5, Table 2)","accuracy is measured against the higher-ID scan taken as ground truth, not an independent survey (Results)","(reviewer observation) Table 4 (0.427 deg) and Table 6 (0.637 deg) list final deviation angles above the \u003C 0.35 deg and \u003C 0.34 deg values in the abstract and text",{"id":824,"shortName":825,"title":826,"year":209,"fulltextStatus":10,"publicationStatus":11,"siteTypes":827,"taskLevel":14,"tasks":828,"platforms":829,"sensorTags":830,"reference":241,"referenceNote":831,"engineeringTask":832,"taskRequirement":833,"requirementSource":27,"siteCount":834,"independentValidation":835,"geometricQuality":836,"taskOutcome":837,"applicabilityClaim":838,"taskLimitations":839,"sourceLocator":840,"sensorsRaw":841,"platformRaw":846,"keyIdeaZh":848,"constructionRelevance":849,"strengths":850,"limitations":853},"kim2018slamdriven","GRoMI SLAM-driven registration","SLAM-driven robotic mapping and registration of 3D point clouds",[102,13],[16,801],[18],[21,23,22],"室內以相鄰掃描中編號較高者為參考計算 RMSE；室外距離比對所用的量測方式未說明；最終點雲沒有 TLS 或全測站參考（Tables 3、5）。","as-built 3D data collection and multi-scan registration (robotic reality capture)","not_reported (no tolerance stated)","2 test beds (one building floor; one outdoor area)","partial: consecutive-scan NN RMSE with the higher scan ID taken as reference (Table 3); outdoor SLAM inter-scan distances compared with measured 'actual distances' whose measurement method is not stated (Table 5); no TLS\u002Ftotal-station reference for the final cloud","inter-scan NN RMSE and axis deviation angles; SLAM vs measured distances (Tables 3, 5)","registration success vs feature-based baseline (Table 4)","authors claim improved automation and fewer scans for large sites","no active construction site; relative (not absolute) accuracy","Sec. 5-6; Tables 3-6",[842,79,843,844,845],"2D LiDAR (5 SICK 2D laser scanners: 1 horizontal used by Hector SLAM, 4 vertically mounted on a rotating frame; each -95 to 95 deg at 0.3333 deg increments)","wheel encoders (four wheels)","DSLR camera (RGB mapping of static scans)","navigation camera and object-avoidance sensors (navigation only)",[847],"wheeled UGV","本研究以自製的地面機器人 GRoMI 結合 2D Hector SLAM（同時定位與建圖）估計平面位姿，並以此位姿作為停走式（stop-and-go）靜態掃描之間的轉換，達成免標靶的點雲配準（registration）。行進中由垂直 2D 雷射堆疊得到的動態點雲雜訊較高，僅用於導航與預掃描；最終成果採用靜態掃描並映射 RGB。作者並以可視面積適應度選擇掃描站位，以減少站數與人工配準。","作者以營建工地資料蒐集為動機，但實驗在既有建築樓層與建築群間的室外區域進行，並未在施工中工地驗證（Sec. 5-6）。",[851,852],"SLAM-based registration succeeded where feature-based registration failed for low-overlap scan pairs (Table 3 vs Table 4)","fewer scans and no targets required for registration (Sec. 6)",[854,855,856],"dynamic (moving) SLAM point cloud noisier, lower resolution and RGB fusion not meaningful (Sec. 6)","tested indoors on a building floor and outdoors near buildings; real construction sites left to future work (Sec. 6)","(inference) 2D Hector SLAM assumes near-planar motion; unsuitable for multi-level or uneven terrain without 3D SLAM",{"id":858,"shortName":859,"title":860,"year":685,"fulltextStatus":10,"publicationStatus":11,"siteTypes":861,"taskLevel":14,"tasks":862,"platforms":863,"sensorTags":864,"reference":66,"referenceNote":110,"engineeringTask":865,"taskRequirement":866,"requirementSource":27,"siteCount":867,"independentValidation":868,"geometricQuality":869,"taskOutcome":870,"applicabilityClaim":871,"taskLimitations":872,"sourceLocator":873,"sensorsRaw":874,"platformRaw":879,"keyIdeaZh":880,"constructionRelevance":881,"strengths":882,"limitations":886},"kim2019uavassisted","UAV-assisted GRoMI","UAV-assisted autonomous mobile robot navigation for as-is 3D data collection and registration in cluttered environments",[13],[16,801],[18,19],[21,22],"as-is 3D data collection and registration in cluttered outdoor sites","scan completeness (LoC) and registration without targets; no tolerance stated","1 outdoor test yard","commercial TLS point cloud (stated ±3 mm device accuracy)","RMSE to commercial TLS (3.91 cm); lack-of-completeness in simulation","data acquisition and processing time vs TLS (Table 4)","authors state both TLS and robot achieved satisfactory accuracy and the robot was more time-efficient","single site, flat ground, TLS reference registration details not reported","Sec. 5; Tables 3-4",[875,876,877,878],"2D line laser scanners: 4 vertically mounted (60 m working range, 50 Hz, 190 deg vertical line) for 3D mapping and 1 horizontal for Hector SLAM","DSLR camera (RGB mapping)","UAV camera with 1\u002F2.3-inch CMOS sensor (SfM prior map, 30 m altitude, 80% overlap, GSD 1 cm)","navigation view camera and object-avoidance sensors (navigation only)",[847,333],"先以無人機（UAV）影像經運動恢復結構（SfM）產生粗略現況點雲，轉為可通行網格與體素地圖，以射線追蹤與貪婪覆蓋選出地面機器人的最佳靜態掃描站位與路徑。機器人以 2D Hector SLAM 提供粗配準，再以 ICP 精配準停走式掃描。作者以商用地面雷射掃描（TLS）點雲為參考，並比較整體作業時間。","測試地點為 Georgia Tech 附近的地震實驗結構與材料堆置場，屬雜亂戶外場域，非施工中建案（Sec. 1, 5）。",[883,884,885],"RMSE of GRoMI cloud vs commercial TLS 3.91 cm (Sec. 5)","total time for six scans 50 min vs 108 min for TLS workflow including 0 min manual registration (Table 4)","scan planning reached LoC \u003C 4% in all 8 simulations (Table 3)",[887,888,889,890],"UAV use restricted by regulations, weather and GPS interference (Sec. 6)","2D SLAM limits robot to flat or nearly flat ground; 3D SLAM planned (Sec. 6)","commercial TLS reported as slightly more accurate than GRoMI (Sec. 5)","moving-robot point cloud is noisier and used only for navigation; final map relies on stationary scans (Sec. 4.3)",{"id":892,"shortName":893,"title":894,"year":419,"fulltextStatus":10,"publicationStatus":11,"siteTypes":895,"taskLevel":103,"tasks":896,"platforms":897,"sensorTags":898,"reference":24,"referenceNote":899,"engineeringTask":900,"taskRequirement":901,"requirementSource":902,"siteCount":903,"independentValidation":904,"geometricQuality":905,"taskOutcome":906,"applicabilityClaim":907,"taskLimitations":908,"sourceLocator":909,"sensorsRaw":910,"platformRaw":913,"keyIdeaZh":915,"constructionRelevance":916,"strengths":917,"limitations":920},"kim2022scaffoldrobotdog","Robot dog scaffold reconstruction (LIO-SAM)","Deep learning-based 3D reconstruction of scaffolds using a robot dog",[395],[326,238],[290],[21,23],"只有人工點標註，用於分割評估，沒有幾何參考。","scaffold 3D reconstruction for safety monitoring","not_reported (safety regulations mentioned generally)","KOSHA guidelines cited generally (Sec. 1)","3 scaffold sites","manual point labels only","none (no reference geometry)","segmentation precision\u002Frecall\u002FF1; qualitative CAD completeness","authors state method sufficiently effective for monitoring scaffolds","no geometric accuracy; missing members; outdoor only","Sec. 3-5; Tables 1-4",[911,912],"3D LiDAR (Ouster OS1-128)","IMU (MicroStrain)",[914],"legged (Unitree A1)","作者以四足機器狗搭載 128 線光達與 IMU，於鷹架周圍變換橫滾與俯仰姿態擴大掃描範圍，離線以 LIO-SAM 建立點雲地圖。接著以遷移學習微調 RandLA-Net 分割鷹架點，再以 DBSCAN 分群、密度熱圖與 RANSAC 擷取立柱、斜撐與橫桿，自動產生 3D CAD 模型。本研究直接以 SLAM 點雲作為重建輸入，但未以獨立參考量測評估幾何精度。","資料來自 Korea Scaffolding Institution、延世大學與中央大學三處鷹架（Table 1）；測試場址為貼附牆面的代表性鷹架。論文未說明任何場址為施工中工地，故不列為真實施工現場驗證。",[918,919],"scaffold segmentation precision 99.24%, recall 83.96%, F1 90.84% on test site (Table 3)","posture changes increase coverage of upper scaffold members (Sec. 3.1, 4.3)",[921,922,923,924,925],"some bracing and working-platform members undetected; platform at LiDAR height hard to capture (Sec. 4.2)","SLAM could not perform indoors or in confined spaces and registration accuracy can be improved (Sec. 5)","limited LiDAR mounting height on the robot dog restricts the field of view of upper members (Sec. 5)","mobile scaffold absent from training data lowered segmentation recall (Sec. 4.3)","(inference) geometric accuracy of the LIO-SAM map was not validated against TLS or survey",{"id":927,"shortName":928,"title":929,"year":685,"fulltextStatus":10,"publicationStatus":11,"siteTypes":930,"taskLevel":14,"tasks":931,"platforms":933,"sensorTags":934,"reference":27,"referenceNote":110,"engineeringTask":110,"taskRequirement":110,"requirementSource":110,"siteCount":110,"independentValidation":110,"geometricQuality":110,"taskOutcome":110,"applicabilityClaim":110,"taskLimitations":110,"sourceLocator":110,"sensorsRaw":935,"platformRaw":938,"keyIdeaZh":940,"constructionRelevance":941,"strengths":942,"limitations":949},"koide2019_hdlgraphslam","hdl_graph_slam (Koide et al. 2019)","A portable three-dimensional LIDAR-based system for long-term and wide-area people behavior measurement",[102],[932],"method",[398],[21],[936,937],"3D LiDAR (Velodyne HDL-32e, 360 deg range data at 10 Hz); its angular velocity output is used for UKF pose prediction during online localization","GPS (outdoor mapping only, unary position edges in UTM coordinates; model not reported)",[939],"backpack with 3D LiDAR and PC carried by a walking or running human observer (indoor building floors, hospital, outdoor sloped terrain)","此論文提出由觀察者背負 3D LiDAR（Velodyne HDL-32e）的人員行為量測系統，分為兩個階段。離線階段以 Graph SLAM 建立環境點雲地圖：前端以 NDT 掃描配準估計相鄰影格位姿，迴圈候選依距離與軌跡長度挑選並以 NDT 適配分數驗證，再以 g2o 最佳化位姿圖；為抑制累積旋轉誤差，室內加入 RANSAC 偵測的地面平面約束，室外加入 GPS 位置約束。線上階段以 UKF 結合 LiDAR 角速度預測與 NDT 對地圖配準進行定位，同時偵測並追蹤人員。論文中的建圖程式即公開的 hdl_graph_slam；README 中的 GICP 類配準選項與 IMU 約束並未出現在論文。","論文本身為醫院照護行為量測，並在大型室內樓層與室外起伏地形驗證建圖，未涉營建。其室內地面平面約束假設單一平坦樓板，跨樓層時需手動切換地圖（推論：施工中常見的樓板高低差、坡道或未完成樓層可能不符此假設）。Feng 等人 [feng2025_construction_lidar_eval] 以本文作為 HDL-Graph-SLAM 的引用文獻（其 ref. 23），並在施工中醫院門診大樓以預設參數測試；作者報告其實際工地 APE RMSE 為 20.21 m（Table 4），在第一個誤差峰誤差最大，但後端迴圈與地面約束有助抑制大尺度地圖翹曲（Sec. 5.3）。論文未說明實際工地 APE 所用參考軌跡的來源（全文僅描述 Gazebo 模擬的真實軌跡外掛），故實際工地 APE 只能視為作者報告值，不能當作已驗證的幾何精度。",[943,944,945,946,947,948],"Enables long-term, wide-area people tracking with a carried 3D lidar, field-tested with caregivers in a hospital (33 sequences, about 52 min) (Sec. Field test)","With the ground-plane constraint a 45 min, about 2400 m indoor sequence gave a flat, consistent map, whereas BLAM and LeGO-LOAM aborted before the end (Sec. SLAM framework evaluation, Fig. 3)","Offline mapping took 5382 s vs 15,327 s for BLAM on the same sequence (Table 1)","With GPS constraints a 42 min outdoor sequence with large undulations was mapped; without GPS no loop was found and mapping failed (Fig. 8)","Angular-velocity pose prediction kept localization working when the observer ran at about 3.0 m\u002Fs (Table 2, Fig. 13)","People positions agreed with a nine-camera Kinect v2 OpenPTrack setup with mean differences of 0.0768 m (observer) and 0.0990 m (subject) (Table 4)",[950,951,952,953,954,955],"Ground-plane constraint assumes a single flat floor; moving between floors required manually switching maps (Sec. Ground plane constraint; Sec. Field test)","Without the plane constraint the indoor map was warped by accumulated rotation error; outdoors without GPS no loop was found and mapping failed (Sec. SLAM framework evaluation)","No trajectory ground truth: localization was evaluated as the difference between predicted poses and NDT results (Sec. Sensor localization evaluation)","Initial localization pose is given by hand; acceleration-based prediction made results worse because of noise (Sec. Sensor localization)","README parameter-tuning guide advises selecting the registration method (FAST_GICP recommended; FAST_VGICP or NDT_OMP when speed matters) and tuning ndt_resolution (0.5-2.0 m indoor, 2.0-10.0 m outdoor) to obtain good odometry","Independent construction-site evaluation reports limited front-end odometry accuracy compensated by back-end graph optimization with loop and ground constraints (feng2025_construction_lidar_eval, Sec. 5.3)",{"id":957,"shortName":958,"title":959,"year":774,"fulltextStatus":10,"publicationStatus":11,"siteTypes":960,"taskLevel":14,"tasks":961,"platforms":962,"sensorTags":963,"reference":66,"referenceNote":110,"engineeringTask":964,"taskRequirement":27,"requirementSource":27,"siteCount":965,"independentValidation":966,"geometricQuality":967,"taskOutcome":27,"applicabilityClaim":968,"taskLimitations":969,"sourceLocator":970,"sensorsRaw":971,"platformRaw":974,"keyIdeaZh":976,"constructionRelevance":977,"strengths":978,"limitations":982},"kremen2024hovermap750m","Hovermap ST-X over 750 m tunnel","Long-distance SLAM scanning of mine tunnel - testing of precision and accuracy of Emesent Hovermap ST-X",[421],[423,424],[398],[451],"long tunnel geodetic mapping","1 (750 m gallery)","Leica P40 + MS60 geodetic network with spheres","profile-wise systematic shifts X\u002FY\u002FZ and RMSE (Results tables)","adequate for many applications; geodetic precision over long distances needs scaling and path optimization","single device; mining gallery","Abstract; Methods; Results; Conclusion",[972,973],"Emesent Hovermap ST-X (32-channel LiDAR, range 0.5-300 m, FOV 290 x 360 deg, up to 640,000 pts\u002Fs single return)","reference: Leica ScanStation P40 TLS + Leica Nova MS60 robotic total station",[975],"backpack","作者在 Josef 礦坑主坑道 750 m 直線段測試 Emesent Hovermap ST-X，以 Leica P40 與 MS60 建立毫米級測量網與球靶，比較單程（1P）與往返（2P）各五次掃描，逐剖面計算橫向、縱向與垂直方向的系統偏移與 RMSE。結果顯示短段精度高，但全長出現累積誤差與縱向壓縮，需尺度改正，橫向偏差可達數十公分；往返掃描可降低橫向誤差但系統誤差仍存在。","長距離坑道（地下基礎設施類）；證明僅兩端控制時長距離 SLAM 隧道測量會產生系統誤差，對隧道施工測量控制布設具直接意義（推論）。",[979,980,981],"local cloud accuracy ~7 mm average RMSEL, no local deformations (Conclusion; Tab. 2)","double pass roughly halves the transverse deviation: average RMSDOT 206.4 mm (1P) vs 115.4 mm (2P) with S=1 (Tab. 2; Fig. 5)","similarity (scaled) transformation reduces longitudinal error (ØRMSEΔY 24.0 to 10.3 mm for 1P, 30.9 to 4.8 mm for 2P) but leaves transverse and vertical shifts almost unchanged (Tab. 2); the Conclusion states deviations are roughly halved",[983,984,985,986],"cumulative error over 750 m with systematic longitudinal compression (clouds about 10 cm per km too short; scale 1.00007-1.00018) requiring scale correction (Results; Tab. 1; Conclusion)","transverse error up to 430 mm single pass and still > 100 mm with double pass; vertical shifts not reduced by double pass (Conclusion)","vertical shift about 90 mm on average (ØRMSEΔZ 89.3-92.8 mm) regardless of pass type; authors suggest possible sagging from smooth floor versus rough rock ceiling, to be verified (Tab. 2; Conclusion)","sign of lateral error depends on walking direction (Conclusion)",{"id":988,"shortName":989,"title":990,"year":286,"fulltextStatus":10,"publicationStatus":11,"siteTypes":991,"taskLevel":14,"tasks":992,"platforms":993,"sensorTags":994,"reference":66,"referenceNote":110,"engineeringTask":996,"taskRequirement":997,"requirementSource":27,"siteCount":998,"independentValidation":999,"geometricQuality":1000,"taskOutcome":1001,"applicabilityClaim":1002,"taskLimitations":1003,"sourceLocator":1004,"sensorsRaw":1005,"platformRaw":1009,"keyIdeaZh":1010,"constructionRelevance":1011,"strengths":1012,"limitations":1015},"kremen2025earthworks","Hovermap ST-X earthwork monitoring (GCP vs RTK)","Determining the accuracy of SLAM and SLAM GNSS-RTK scanning for monitoring earthworks",[60],[447],[398],[451,995],"gnss","earthwork (soil deposit) monitoring","not_reported (RTK accuracy 10 mm horizontal \u002F 40 mm vertical cited as context)","1 soil deposit","Leica P40 TLS georeferenced via GCPs (target std 6 mm)","RMSD, axis-wise RMSD\u002Fmean\u002Fstd vs TLS (Tables 2-3)","not_reported (no volume result read)","SLAM scanning is a more practical alternative to static TLS for earthwork monitoring","single site, single device; no volume validation read","Abstract; Tables 1-3; Conclusions",[1006,1007,1008],"Emesent Hovermap ST-X (32-channel LiDAR) on a backpack","Trimble R12i GNSS-RTK receiver on the backpack, CZEPOS network RTK (RTK runs only)","reference: Leica ScanStation P40 TLS (12 stations) and Leica Nova TS60 total station",[975],"作者在中波希米亞 D7 高速公路施工產生的土方堆置場（約 150 m × 55 m × 15 m）以背包式 Emesent Hovermap ST-X 沿封閉路線各掃描兩次，比較 8 個高反射標靶地面控制點（GCP）與機載 Trimble R12i GNSS-RTK 兩種地理參考方式，並以 12 站 Leica P40 地面掃描為參考。GCP 方式 RMSD 平均 17.7 mm（平滑後 15.7 mm），RTK 方式 21.6 mm（平滑後 20.0 mm），RTK 第一趟有明顯高程偏移。作者指出堆頂僅以一條窄路與邊坡相連，造成堆頂相對邊坡的系統性高程差，因此未達原廠 15 mm 規格，建議在堆頂四角布設控制點，並讓 RTK 作業至少連接一個已知高程點。","D7 高速公路施工開挖產生的土方堆置場（Slaný 附近），屬實際營建土方情境（Abstract；Experiment Area）。論文未計算體積或土方量，只以點雲對參考 TIN 的 RMSD 評估幾何精度，不能直接推論土方量精度（推論）。",[1013,1014],"GCP-based RMSD ~17 mm close to declared 15 mm mapping accuracy (Abstract)","operationally simpler and faster than static TLS for earthwork monitoring (Abstract)",[1016,1017,1018,1019],"systematic height shifts in RTK datasets (Abstract; Conclusions)","smoothing gave only slight improvement on rough soil surface (Results)","manufacturer's 15 mm mapping accuracy not achieved; the heap top was reached by a single narrow path, leaving top and slopes weakly connected (mean top vs sides height offset -22 vs -3 mm for RTK_1, -8 vs -1 mm for GCP runs) (Discussion)","only two GCPs placed on the heap top, not optimally; walking along the top edge was not feasible for operator safety (Discussion)",{"id":1021,"shortName":1022,"title":1023,"year":1024,"fulltextStatus":10,"publicationStatus":11,"siteTypes":1025,"taskLevel":14,"tasks":1026,"platforms":1027,"sensorTags":1028,"reference":24,"referenceNote":1029,"engineeringTask":1030,"taskRequirement":27,"requirementSource":27,"siteCount":27,"independentValidation":27,"geometricQuality":27,"taskOutcome":27,"applicabilityClaim":27,"taskLimitations":27,"sourceLocator":31,"sensorsRaw":1031,"platformRaw":1033,"keyIdeaZh":1035,"constructionRelevance":1036,"strengths":1037,"limitations":1042},"makkonen2017zebshaft","ZEB1 in mine shaft for maintenance model","Using SLAM-based Handheld Laser Scanning to Gain Information on Difficult-to-Access Areas for Use in Maintenance Model",2017,[421],[423,326],[398],[451],"沒有獨立的參考測量，品質只以下降與上升兩趟點雲的一致性評估（Sec. 4）。","maintenance model of difficult-to-access mine shaft",[1032],"ZEB1 handheld SLAM laser scanner (commercial name; the whole system, developed by CSIRO, is called Zebedee): spring-mounted 2D laser, data logger and IMU; 43,200 points\u002Fs; typical range 15-20 m; noise +-30 mm; 270 deg horizontal FOV, about 120 deg swept vertical FOV",[1034],"handheld, operator standing on the roof of a mine elevator car moving at 1 m\u002Fs and waving the scanner up and down at about 1 Hz; scanner rotated 180 deg between the down and up passes","作者於芬蘭 Pyhäsalmi 礦場長約 1440 m、直徑 5 m 的 Timo 豎井，在例行檢查時站在以 1 m\u002Fs 移動的電梯車廂頂，以 ZEB1 手持 SLAM 掃描器（彈簧式 2D 雷射加 IMU）掃描，下降與上升各約 25 分鐘、各覆蓋約 270°，共取得 1.2 億點。GeoSLAM 雲端處理前兩次完全失敗，第三次才產出結果，且 252 至 434 m 段上下兩個半圓環未能正確疊合；作者以時間戳分離上下行點雲，分割五條纜線自動對齊後套用轉換，半手動修正該段。重疊部分距離差在自動 SLAM 段（130 m 深處）平均 27 mm、標準差 17 mm，修正段平均 37 mm、標準差 19 mm。全文沒有三腳架 TLS、全測站或標靶作為獨立參考；作者認為系統尚未成熟到能勝任此任務，但速度快，可作為維護模型的潛在資料來源。","礦場人員運輸電梯豎井（長約 1440 m、直徑 5 m），豎井須依國家標準定期檢查，該礦至少每三個月檢查一次；現行檢查需五人在電梯上、一人在地面以無線電聯繫，電梯停用約一小時；屬地下基礎設施維護與檢查情境，非施工中工地，也是早期 ISARC 以手持 SLAM 掃描難以到達區域的例證（Sec. 1、3）。",[1038,1039,1040,1041],"about 120 million points; total measurement time about one hour including preparation (Sec. 3-4)","estimated point density 2,778 points per m2 (about 2 cm spacing) at 1 m\u002Fs (Sec. 4)","automatic SLAM section at 130 m depth: down and up overlap distance error mean 27 mm, SD 17 mm (Sec. 4, Figs. 11-12)","results seemed not strongly affected by rain and condensation (Sec. 5)",[1043,1044,1045,1046,1047,1048],"GeoSLAM SLAM processing failed completely twice before a third attempt gave a result (Sec. 4)","252-434 m section misregistered (two half-cylindrical rings did not overlap); automatic and manual point registration failed; fixed by cable-based alignment with mean 37 mm, SD 19 mm (Sec. 4, Figs. 5-10)","moving platform allowed only about 270 deg per pass; full 360 deg coverage recommended (Sec. 1, 5)","constant rain in the first 1000 m wetted the scanner window and rock walls (Sec. 3)","no independent reference survey; quality assessed only by down and up consistency (Sec. 4)","authors state the system was not yet ready for this demanding task (Sec. 5)",{"id":1050,"shortName":1051,"title":1052,"year":9,"fulltextStatus":10,"publicationStatus":11,"siteTypes":1053,"taskLevel":14,"tasks":1054,"platforms":1055,"sensorTags":1057,"reference":24,"referenceNote":1058,"engineeringTask":1059,"taskRequirement":27,"requirementSource":27,"siteCount":27,"independentValidation":27,"geometricQuality":1060,"taskOutcome":27,"applicabilityClaim":27,"taskLimitations":1061,"sourceLocator":1062,"sensorsRaw":1063,"platformRaw":1069,"keyIdeaZh":1073,"constructionRelevance":1074,"strengths":1075,"limitations":1081},"nikoohemat2020indoor3d","MLS indoor 3D reconstruction for routing","Indoor 3D reconstruction from point clouds for optimal routing in complex buildings to support disaster management",[102],[326],[1056,398,107],"trolley",[451,109],"只以人工標註點與房間、門的數量評估，未與參考量測比較牆位或尺寸等幾何精度（Sec. 7；Tables 1、2）。","indoor as-built 3D modelling for emergency routing","not_verified (Table 2 accuracy not read)","completed buildings; partial reading","Sec. 3-5",[1064,1065,1066,1067,1068],"GeoSLAM ZEB1 handheld MLS (Fire brigade #1, scanner precision e \u003C 0.06 m)","Viametris push-cart MLS (Fire brigade #2, e \u003C 0.04 m)","GeoSLAM ZEB-REVO handheld MLS (TU Delft, e \u003C 0.04 m)","FARO Focus TLS (Penthouse dataset from Mura et al., e \u003C 0.01 m)","NavVis Trolley named only in the Fig. 2 caption example",[1070,1071,1072],"handheld (ZEB1, ZEB-REVO)","push-cart (Viametris)","static terrestrial (FARO Focus, Penthouse)","作者提出從行動雷射掃描點雲重建多樓層建築體積模型並產生導航網路的流程：以掃描軌跡與同步時間戳分離樓層與樓梯，以平面分段鄰接圖加啟發式規則標記牆、地板與天花板（容許斜面與非正交配置），經人工目視修正與自動延伸後重建體積牆與房間多面體，以軌跡與牆面交點偵測門，並以彈性空間分割（FSS）產生可導航空間。在 ZEB1 手持（Fire brigade #1）、Viametris 推車式（Fire brigade #2）、ZEB-REVO 手持（TU Delft）與一組 FARO TLS（Penthouse）資料上，房間正確重建數依序為 25 間中 23 間、27 間中 24 間、18 間中 16 間與 4 間中 4 間；Fire brigade #2 逐點標記的精確率為牆 0.90、地板 0.98、天花板 0.88。論文未評估牆位或尺寸等幾何精度。此研究顯示 SLAM 類行動掃描的軌跡資訊可成為下游建模的語意線索。","於既有複雜建築資料測試：兩棟各兩層的消防隊建築、TU Delft 建築與一組以 TLS 取得、含斜牆與老虎窗的 Penthouse 資料，屬竣工後既有設施的掃描建模與緊急應變導航，非施工中工地；成果只以人工標註點與房間、門的數量評估，未與參考量測比較幾何精度（Sec. 7，Table 1 至 2）。",[1076,1077,1078,1079,1080],"handles slanted walls, ramps, non-horizontal ceilings and non-Manhattan layouts, including dormers in the Penthouse data (Sec. 4.2, 7.1)","trajectory-based door detection finds closed doors crossed by the trajectory; 27 of 30 doors in Fire brigade #2 (Sec. 4.4, Table 1)","Fire brigade #2 labels vs manual labels: wall precision 0.90, recall 0.96; floor 0.98, 1.0; ceiling 0.88, 1.0 (Table 2)","rooms correctly reconstructed: 23 of 25, 24 of 27, 16 of 18, 4 of 4 (Table 1)","visual inspection about 5 min per floor of about 15 rooms, under 6% of segments modified (Sec. 7.2)",[1082,1083,1084,1085,1086,1087,1088,1089],"doors not crossed by trajectory remain undetected (Sec. 4.4, 7.6)","manual visual correction of labels required (Sec. 4.2.2, 7.2)","spaces not fully enclosed are not reconstructed, e.g. the chimney (Sec. 5.1, 7.1)","columns, wall intrusions and window frames are not reconstructed; no perpendicularity constraint, so some walls are slightly skewed (Sec. 7.6)","trajectory-based level separation only works for scanners that can enter staircases; transparent surfaces are problematic (Sec. 7.6)","stair modelling fails if several steps are missing; the push-cart could not scan the stairs (Sec. 7.1, 7.6)","a false-positive wall from ceiling clutter split a corridor and created a false door (Sec. 7.1)","geometric accuracy of the model is not evaluated (Sec. 7)",{"id":1091,"shortName":1092,"title":1093,"year":419,"fulltextStatus":10,"publicationStatus":11,"siteTypes":1094,"taskLevel":103,"tasks":1096,"platforms":1097,"sensorTags":1098,"reference":216,"referenceNote":1099,"engineeringTask":110,"taskRequirement":110,"requirementSource":110,"siteCount":110,"independentValidation":110,"geometricQuality":110,"taskOutcome":110,"applicabilityClaim":110,"taskLimitations":110,"sourceLocator":110,"sensorsRaw":1100,"platformRaw":1105,"keyIdeaZh":1107,"constructionRelevance":1108,"strengths":1109,"limitations":1115},"nubert2022constructionfusion","Graph-MSF for walking excavators","Graph-based Multi-sensor Fusion for Consistent Localization of Autonomous Construction Robots",[1095],"unlabelled",[63],[449],[23,21,995],"位置誤差以 RTK GNSS 比對，但 RTK GNSS 本身是融合輸入，因此不是獨立參考（推論；Table I）；0.58 cm 是作者定義的傳播估計與最佳化估計之間的一致性誤差，不是對外部參考的絕對精度（Sec. V）。Fig. 1 的 Leica RTC360 地面真值地圖只作定性疊合，未報告點雲幾何誤差。",[1101,1102,1103,1104],"IMU at 100 Hz (Ellipse-A at the bottom of the cabin on the first machine; Lord MicroStrain MV5 on the roof of the second)","3D LiDAR Ouster OS0-128 (odometry from CompSLAM at 5 Hz)","dual-antenna RTK GNSS Leica iCON iXE3 at 20 Hz","discrete cabin-rotation joint angle measurements",[1106],"walking excavator (two Menzi Muck machines; HEAP is one of them)","本文為大型步行式挖掘機提出以因子圖為基礎的多感測器融合，結合 IMU、LiDAR 與 RTK-GNSS，以預測更新迴圈同時取得高頻率與全域精度。雙圖設計在 GNSS 暫時失效與恢復之間切換，處理非同步量測與感測器中斷。作者並指出大型機具上的外參校正難以取得精確值，會惡化融合結果。","直接針對營建機具（兩台 Menzi Muck 步行式挖掘機）的實地測試：一為瑞士 Oberglatt 的施工作業展示（Construction Task），一為 Wangen 較都市化環境的導航任務（Sec. V）。原文稱前者為 construction operation showcase，未明確描述為施工中工地，故驗證環境維持 task_level_validation。證據聚焦位姿一致性、延遲與 GNSS 中斷處理，不是點雲幾何精度；外參校正在全文中假設已知（Sec. III）。Fig. 1 將線上建立的地圖疊在 Leica RTC360 掃描所得的地面真值地圖上，但只作定性展示，未報告點雲幾何誤差。",[1110,1111,1112,1113,1114],"Consistent estimates through sensor dropout and recovery on two excavators (abstract, Sec. VI)","Translation consistency error mean 0.58 cm, SD 3.16 cm during a navigation task, defined as deviation between propagated and optimized estimates (Sec. V, Fig. 6)","Relative position error against RTK GNSS in the Construction Task: mean 0.18 cm, SD 1.28 cm, the smallest SD of the three estimators (Table I)","Latency 41 us mean versus 102 us for TSIF and 32.9 us for MSF (Table II)","Map built from the estimates stayed intact near trees where the TSIF-based map was corrupted by GNSS loss (Fig. 5, qualitative)",[1116,1117],"Precise extrinsic calibrations are difficult to obtain on large machines and can degrade fusion (Sec. V)","Joint estimation of extrinsics and sensor time offsets left to future work (Sec. VI)",{"id":1119,"shortName":1120,"title":1121,"year":774,"fulltextStatus":10,"publicationStatus":11,"siteTypes":1122,"taskLevel":14,"tasks":1123,"platforms":1124,"sensorTags":1125,"reference":216,"referenceNote":1126,"engineeringTask":1127,"taskRequirement":27,"requirementSource":27,"siteCount":27,"independentValidation":27,"geometricQuality":27,"taskOutcome":27,"applicabilityClaim":27,"taskLimitations":27,"sourceLocator":31,"sensorsRaw":1128,"platformRaw":1133,"keyIdeaZh":1137,"constructionRelevance":1138,"strengths":1139,"limitations":1143},"prieto2024mars","MARS multi-robot data collection","Multiagent robotic systems and exploration algorithms: Applications for data collection in construction sites",[13],[16],[18,290],[451,21,292],"作者所稱的 ground-truth 地圖是遙控機器人執行 SLAM 的產物（未說明是哪一台），不是獨立參考；文中未報告點雲幾何精度（期刊版 Sec. 4.1、6）。","3D digitization via multi-robot exploration",[1129,1130,1131,1132],"Emesent Hovermap ST LiDAR scanner (data collection; range up to 100 m; frames registered by its LiDAR-based odometry)","Ouster OS1 3D LiDAR (navigation; 150 m maximum range, 45 deg VFOV)","front-facing short-range depth camera (RA1 perception)","RGB-zoom camera and thermal camera on a 3-axis gimbal, five short-range depth cameras (RA2)",[1134,1135,1136],"wheeled UGV (Robotnik SUMMIT-XL, RA1)","legged (Boston Dynamics Spot, RA2)","human agent","作者提出多代理人（機器人與人員）營建資料蒐集方法：以 IFC 網格水平切片得到 2D 地圖，將已探索自由面積占地圖自由面積的比例作為邊界探索的停止準則，未達門檻而已無可達邊界時向其他代理人求助。案例在大學校園約 80 m² 的空間進行，含半完成（清水混凝土與 CMU 牆）與已完工（實驗室及辦公室）區域；因無 BIM，改以遙控機器人執行 SLAM 預先建立的 2D 地圖作為面積基準；RA1（Robotnik SUMMIT-XL，搭載 Emesent Hovermap ST 蒐集點雲、Ouster OS1 供導航）探索到 84.53% 時已無邊界，RA2（Boston Dynamics Spot 加機械手臂）在人員遠端指定抓取點後移除障礙，RA1 再探索至 95.79% 完成。文中未報告點雲幾何精度、密度或執行時間。","案例為大學校園內約 80 m²、以雜物與障礙物分隔為兩區的空間，含半完成（清水混凝土與 CMU 牆）與已完工的實驗室及辦公室，非施工中工地，作者於結論也稱其為雜亂的實驗室環境；所稱 ground-truth 地圖是遙控機器人執行 SLAM 的產物（文中未說明是哪一台機器人），並非獨立參考（VoR Sec. 4.1、6）。",[1140,1141,1142],"exploration stopped with no frontiers at 84.53%; after RA2 removed the obstacle it reached 95.79% and met the criterion (Sec. 4.3)","only one human intervention needed (indicating the grasp point) (Sec. 4.3)","MANET radio up to 120 Mbps with configurable frequency, power, bandwidth, 3 x 3 MIMO and AES-256 encryption (Sec. 4.2.1, Table 2)",[1144,1145,1146,1147,1148,1149,1150],"small case study: two robots, one human, one task; a bigger and more complex scenario is needed (Sec. 5)","ROS 1 master node and centralized CMS are single points of failure (Sec. 3.1.1, 5)","prior information (BIM or pre-mapped ground truth) needed for robust and complete results (Sec. 5)","only one agent performed exploration (Sec. 5)","communication stability depends on space layout and wall materials (Sec. 5)","human needed to indicate the grasp point (Sec. 4.3, 5)","tested in a cluttered lab, not a real construction site (Sec. 6)",{"id":1152,"shortName":1153,"title":1154,"year":234,"fulltextStatus":10,"publicationStatus":11,"siteTypes":1155,"taskLevel":14,"tasks":1156,"platforms":1157,"sensorTags":1158,"reference":216,"referenceNote":1159,"engineeringTask":110,"taskRequirement":110,"requirementSource":110,"siteCount":110,"independentValidation":110,"geometricQuality":110,"taskOutcome":110,"applicabilityClaim":110,"taskLimitations":110,"sourceLocator":110,"sensorsRaw":1160,"platformRaw":1162,"keyIdeaZh":1164,"constructionRelevance":1165,"strengths":1166,"limitations":1171},"qian2026_tunnel2dgs","Depth-aware 2DGS for shield tunnels","Depth-Aware Gaussian Splatting with Dynamic Masking for High-Fidelity Geometric Modeling of Shield Tunnel Digital Twins",[421,236,60],[326],[19],[22],"0.7% 是遮罩版網格對 SfM+MVS 網格（被指定為參考模型）的平均點對點距離除以最大斷面跨度；模型沒有絕對尺度，配準方法未說明，也沒有 TLS 或測量真值（Geometric Accuracy Evaluation）。",[1161],"monocular camera (DJI Mini 4 Pro UAV camera; 1920x1080 video)",[1163],"UAV (DJI Mini 4 Pro) flown longitudinally near the tunnel central axis at 2.0 m\u002Fs","本研究針對浙江省一座施工中盾構隧道（外徑 8.8 m、內徑 8.0 m、環寬 1.6 m），以 DJI Mini 4 Pro 無人機沿隧道中軸縱向飛行，錄製 40 秒 1920×1080 影片涵蓋 80 m 區段，擷取 286 影格後保留 210 張影像。流程先以 COLMAP 進行 SfM 取得相機位姿與稀疏點雲，再以二維高斯潑濺（2DGS）搭配深度感知動態遮罩訓練（依相機深度排序，只讓最近 p% 的面元參與光柵化與損失計算，p 值未報告），最後將各視角渲染深度以 TSDF 融合成網格。論文所稱「0.7% 幾何精度誤差」，是遮罩版網格取樣點雲到 SfM+MVS 網格（被指定為參考模型 No. 1）的平均點對點距離 0.083 u，除以該網格最大斷面跨度 11.8 u；模型沒有絕對尺度，配準方法未說明，也沒有 TLS 或測量真值，因此只代表與傳統攝影測量結果的一致性。建模總時間 1 小時 05 分 33 秒，較 SfM+MVS 的 1 小時 44 分 09 秒少約 37%。此為離線攝影測量式流程，非 SLAM。","資料取自浙江省一座施工中盾構隧道的 80 m 區段（外徑 8.8 m），屬真實施工中隧道場域證據。但幾何評估以 SfM+MVS 網格為參考，且模型無絕對尺度、沒有獨立量測，只能證明與傳統攝影測量的一致性，不能證明工程量測精度；論文僅驗證單一隧道。",[1167,1168,1169,1170],"Rendered depths agree with valid MVS depths for 86.15%, 98.12% and 99.86% of pixels at thresholds 1.25, 1.25^2 and 1.25^3 over 210 viewpoints while filling MVS depth holes (Table 1)","Depth-aware masking lowers the mean point distance to the SfM+MVS mesh from 0.09 u to 0.083 u (7.7%) and the final training loss by about 10% (Geometric Accuracy Evaluation; training-loss text)","Total modeling time 1 h 05 min 33 s versus 1 h 44 min 09 s for MVS plus surface reconstruction, about 37% less (Table 3)","Fewer voids and less blurring and noise in dark segments than the MVS mesh; about 5% higher Brenner sharpness than unmasked 2DGS (Visual Quality Comparison)",[1172,1173,1174,1175,1176,1177,1178],"Geometric accuracy is agreement with the MVS mesh designated as reference (No. 1); models have no absolute scale (unit u), the registration method is not described, and no TLS, total station or other survey reference is used (Geometric Accuracy Evaluation)","2DGS training time (about 49 min) precludes real-time structural updating or inference (Limitations and Future Research Directions; Table 3)","Images give no useful supervision in complete darkness (Limitations and Future Research Directions)","Validated on a single shield tunnel scenario (Limitations and Future Research Directions)","Side-view renderings stay blurred without side-view images, and the Gaussian model alone is not suited to physical measurement (Shield Tunnel Gaussian Model)","In bright regions segment joints are rendered slightly less sharply than in the MVS mesh (Visual Quality Comparison)","Masking percentile p and TSDF voxel size are not reported; data and code available only on request (Methods; Data Availability Statement)",{"id":1180,"shortName":1181,"title":1182,"year":286,"fulltextStatus":10,"publicationStatus":11,"siteTypes":1183,"taskLevel":14,"tasks":1184,"platforms":1185,"sensorTags":1186,"reference":24,"referenceNote":1187,"engineeringTask":1188,"taskRequirement":27,"requirementSource":27,"siteCount":27,"independentValidation":27,"geometricQuality":27,"taskOutcome":27,"applicabilityClaim":27,"taskLimitations":27,"sourceLocator":31,"sensorsRaw":1189,"platformRaw":1191,"keyIdeaZh":1193,"constructionRelevance":1194,"strengths":1195,"limitations":1199},"qin2025backpackmetro","Backpack SLAM metro tunnel quality modelling","Exploring the potential of backpack SLAM LiDAR for metro tunnel inspection: explainable modeling and optimization of point cloud quality",[421],[238,424],[398],[451],"沒有 TLS 或全測站等外部參考，只以粗糙度、密度、平面度與球度描述點雲的內在品質，並未量測幾何精度。","metro tunnel inspection data acquisition",[1190],"OmniSLAM R6 commercial backpack SLAM system: rotating 32-beam LiDAR (maximum range 120-300 m, up to 640,000 points\u002Fs, up to 10,000 points\u002Fm2), inertial navigation fused by LIO, RTK-SLAM positioning module, 360 deg panoramic imaging unit with two 1-inch CMOS sensors; stated absolute accuracy 3 cm, relative better than 1 cm",[1192],"backpack carried by one operator (same operator throughout), walking about 2 to 8 km\u002Fh on flat track or between rails","作者在武漢地鐵 2 號線 Jiangji 隧道（Jianghan Road 站至 Jiyuqiao 站間約 3.322 km 的過江盾構隧道）選四個測段，於非營運夜間以商用背包式 SLAM 光達 OmniSLAM R6 進行 12 組試驗（8 組兩水準正交設計加 4 組只改變步行速度），每段約 50 m，切成 360 個管片環樣本。以 CloudCompare 計算粗糙度、密度、平面度與球度四種局部幾何指標，逐樣本擬合 Skew-Normal 分布參數，再以 CatBoost 多輸出迴歸與 SHAP 分析速度、行走穩定性、掃描次數、曲率與坡度五個因子的影響。作者結論是速度為最主要因子，過慢（2 km\u002Fh）與過快（8 km\u002Fh）都使品質劣化。研究沒有 TLS 或全測站等外部參考，只描述點雲內在品質，並未量測幾何精度。","營運中地鐵盾構隧道的檢測資料蒐集：武漢地鐵 2 號線過江隧道四個測段，試驗在非營運夜間進行，隧道內沒有施工或設備作業；屬運維檢測情境，非施工中工地（Sec. 3.1 至 3.2）。",[1196,1197,1198],"CatBoost multi-output regression with five-fold cross-validation: the text states R2 above 0.998 and RMAE below 0.01 for location and scale parameters of sphericity and density (Sec. 3.4); Table 7 column labels appear swapped and, read that way, the density location RMAE is 0.0114, so the claim is not fully consistent with Table 7","speed identified as the most influential factor; very slow (2 km\u002Fh) and very fast (8 km\u002Fh) walking degraded quality (Sec. 3.3, 5)","quantifiable basis for optimizing acquisition strategy (Abstract, Sec. 5)",[1200,1201,1202,1203,1204],"all data from a single metro tunnel system, limiting generalizability (Sec. 4.3)","limited number of experimental samples (Sec. 5)","static regression without SLAM temporal dynamics or error propagation (Sec. 5)","only geometric features, no colour or semantics (Sec. 5)","hyperparameters chosen without extensive tuning (Sec. 3.4)",{"id":1206,"shortName":1207,"title":1208,"year":1024,"fulltextStatus":10,"publicationStatus":11,"siteTypes":1209,"taskLevel":103,"tasks":1210,"platforms":1211,"sensorTags":1212,"reference":27,"referenceNote":1213,"engineeringTask":110,"taskRequirement":110,"requirementSource":110,"siteCount":110,"independentValidation":110,"geometricQuality":110,"taskOutcome":110,"applicabilityClaim":110,"taskLimitations":110,"sourceLocator":110,"sensorsRaw":1214,"platformRaw":1218,"keyIdeaZh":1221,"constructionRelevance":1222,"strengths":1223,"limitations":1227},"rebolj2017pcqualityscanvsbim","Rebolj et al. 2017 (point cloud quality for Scan-vs-BIM)","Point cloud quality requirements for Scan-vs-BIM based automated construction progress monitoring",[211,13],[619],[211,398],[22,292],"以構件辨識正確與否作任務層驗證（實驗 BIM 與 Kinect 2 局部掃描），不是點雲幾何參考。",[1215,1216,1217],"simulated person-carried mobile laser scanner with 360-degree vertical view (HeliOS)","synthetic images of the simulated BIM processed with VisualSFM (photogrammetry and videogrammetry)","Kinect 2 range camera",[1219,1220],"simulation (virtual scanner moved along a modelled person trajectory in HeliOS)","Kinect 2 range camera scanning part of a real building (carrying mode not reported)","本文依構件投影到局部座標三個正交平面的面積總和，將建築構件分為大（至少 5 m²）、中、小、極小（小於 0.25 m²）四級，對應不同施工階段；再以 HeliOS 模擬沿人員行走軌跡移動、具 360 度垂直視野的行動雷射掃描儀，對含 100 個構件的實驗 BIM（刪除 35 個構件作為模擬現況）產生 108 組不同深度精度、光束發散與頻率的點雲，推導每級構件在 Scan-vs-BIM 中正確辨識所需的最小局部密度（每平方公尺 14、70、530、4500 點）、覆蓋率 0.5 準則，以及深度精度須小於缺漏構件最短邊一半的準則。驗證包含以 VisualSFM 由同一模擬 BIM 的合成影像產生的攝影測量與錄影測量點雲，以及以 Kinect 2 掃描真實建物局部（18 個構件）；Kinect 2 實驗合併三段部分掃描，點雲散布指標（LOS）為 12%，依 A 與 LOS 的對應關係約相當於 146 mm 深度精度，遠差於感測器的 21 mm，並有 3 個缺漏構件被誤判為存在。方法假設點雲已與 BIM 對齊，未處理配準誤差，也未涉及 SLAM 軌跡誤差；本文是以工程任務成果反推點雲品質需求的明確前例。","營建進度監測（Scan-vs-BIM）的任務導向點雲品質需求；驗證含一處真實建物局部的 Kinect 2 掃描（Sec. 4），非施工中大型工地。",[1224,1225,1226],"Quantitative, task-linked quality criteria per element size class (Sec. 3, Table 2)","Relation between criteria and scanning parameters for planning (Sec. 5)","Public research data (CC BY 4.0)",[1228,1229,1230,1231,1232,1233],"Assumes clear line of sight; occlusions and construction interference treated as not relevant (Sec. 1.2)","Criteria depend on the identification algorithm (Sec. 4.4)","Future work needed on point cloud positioning and calibration, merging partial clouds and moving objects (Sec. 6)","Point cloud assumed pre-aligned to the BIM; registration and positioning errors not evaluated (Sec. 2.2)","Photogrammetry and videogrammetry validations reuse the simulated BIM that produced the criteria; only the Kinect 2 test uses a real scene with 18 elements (Sec. 4 intro, Sec. 4.3)","dmax is derived for a single frame; overlap from a moving scanner and merging of frames are not modelled (Sec. 5.1)",{"id":1235,"shortName":1236,"title":1237,"year":9,"fulltextStatus":10,"publicationStatus":11,"siteTypes":1238,"taskLevel":14,"tasks":1239,"platforms":1240,"sensorTags":1241,"reference":66,"referenceNote":110,"engineeringTask":1242,"taskRequirement":27,"requirementSource":27,"siteCount":1243,"independentValidation":1244,"geometricQuality":1245,"taskOutcome":27,"applicabilityClaim":27,"taskLimitations":1246,"sourceLocator":1247,"sensorsRaw":1248,"platformRaw":1251,"keyIdeaZh":1252,"constructionRelevance":1253,"strengths":1254,"limitations":1259},"salgues2020mmsindoor","ZEB-REVO RT and LiBackPack C50 indoor","EVALUATION OF MOBILE MAPPING SYSTEMS FOR INDOOR SURVEYS",[102],[424],[398],[451],"indoor building survey","3","FARO Focus3D X330 TLS, target-based georeferencing","M3C2 distances after ICP; plane-fit noise (Table 2)","ICP alignment hides global drift; completed buildings","Sec. 3-7",[1249,1250],"GeoSLAM ZEB-REVO RT handheld: 2D infrared laser profilometer with automatic rotating head plus IMU; no GNSS, no camera (Sec. 3.1, Table 1)","GreenValley LiBackPack C50 backpack: laser profilometer in rotating housing (360 deg H, 30 deg V FOV), panoramic camera, IMU, GNSS for outdoor positioning (Sec. 3.1, Table 1)",[405,975],"作者在史特拉斯堡三處既有室內場址（Ponts Couverts 約 20 m 高的歷史塔樓、五層的動物學博物館、約 850 m² 的 INSA 測量實驗室）評估手持 GeoSLAM ZEB-REVO RT 與背包式 GreenValley LiBackPack C50（後者只用於實驗室），以 FARO Focus3D X330 標靶式地理參考 TLS 點雲為參考，在 CloudCompare 先重取樣 1 cm、以人工選點粗對位再 ICP，最後以 M3C2 計算偏差。塔樓由地面層上到頂層再返回的閉合迴圈，以及博物館地下室的閉合迴圈，在移除離群點後都有 91% 偏差小於 5 cm，但博物館跨兩層並經狹長樓梯間的迴圈只有 55%；實驗室約 1 m³ 柱體樣本有 97% 至 100% 小於 5 cm。平面擬合雜訊標準差為 FARO 3.0 mm、ZEB 3.8 mm、LiBackPack 5.7 mm。作者結論是作業方式對 SLAM 結果影響很大，跨樓層的大迴圈仍是弱點。","評估對象為既有建築室內調查（含文化資產塔樓），非施工中工地。",[1255,1256,1257,1258],"91% of deviations below 5 cm after outlier removal in the tower closed loop (ground floor to top floor and back) and in the museum basement closed loop (Sec. 5.1, 5.2.1)","about 1 m3 pillar sample: 97% (ZEB-REVO RT) and 100% (LiBackPack C50) below 5 cm (Sec. 5.3.1-5.3.2)","ZEB-REVO RT noise 3.8 mm vs LiBackPack C50 5.7 mm vs TLS 3.0 mm (Table 2)","mobile systems capture areas occluded from static stations more easily (Sec. 5.3.4)",[1260,1261,1262,1263,1264,1265],"registration quality lower than static systems; operating mode crucial; large loops over several floors remain a limit (Sec. 7)","alignment by manual point picking + ICP to TLS, so absolute georeferencing error not isolated (Sec. 4)","two-floor loop through a narrow stairwell: 55% of deviations below 5 cm and 80% below 10 cm after outlier removal, 'accuracy' 9 cm (Sec. 5.2.2)","indoor-outdoor loop: ZEB-REVO RT captured only a limited part of the facade; 81% below 5 cm (Sec. 5.2.3)","low MMS point density prevented a proper comparison of the large laboratory floor; dataset 3 compared only on a ~1 m3 pillar (Sec. 5.3)","several percentages computed after outlier removal whose procedure is not described (Sec. 5.1-5.2)",{"id":1267,"shortName":1268,"title":1269,"year":209,"fulltextStatus":10,"publicationStatus":11,"siteTypes":1270,"taskLevel":14,"tasks":1271,"platforms":1272,"sensorTags":1273,"reference":66,"referenceNote":1274,"engineeringTask":1275,"taskRequirement":27,"requirementSource":27,"siteCount":27,"independentValidation":27,"geometricQuality":27,"taskOutcome":27,"applicabilityClaim":27,"taskLimitations":27,"sourceLocator":31,"sensorsRaw":1276,"platformRaw":1280,"keyIdeaZh":1281,"constructionRelevance":1282,"strengths":1283,"limitations":1289},"sammartano2018zeb","ZEB accuracy and geometric content","Point clouds by SLAM-based mobile mapping systems: accuracy and geometric content validation in multisensor survey and stand-alone acquisition",[102,421],[424,326],[398],[451],"城堡中庭與設防村落以 FARO Focus 3D X120 TLS 與 UAV 攝影測量 DSM 為參考，塔樓另與近景攝影測量模型比較；地下冰窖與礦坑只做去程與回程點雲互比（Tables 3、7、8、10、11、14、15）。","architectural\u002Fheritage documentation",[1277,1278,1279],"GeoSLAM ZEB1 (spring-mounted head, 40 Hz, about 43,200 points\u002Fs, 15-30 m range, about 1.5 kg) (Table 1)","GeoSLAM ZEB-REVO (automatically rotating head, 100 Hz, 270 deg HFOV and 100 deg VFOV, about 2 kg) (Table 1)","both: Hokuyo UTM-30LX 2D 905 nm ranging sensor and an IMU with triaxial gyros, accelerometers and magnetometers; no GNSS and no RGB (Sec. ZEB system operational behaviour)",[405],"作者以 Valperga 城堡（Torino）與 San Silvestro 考古礦業園區（Livorno）五組資料評估 GeoSLAM ZEB1 與 ZEB-REVO 手持 SLAM 點雲：塔樓螺旋梯、地下冰窖與中世紀礦坑採單獨使用驗證（去程與回程兩段點雲互比，塔樓另與近景攝影測量模型比較），城堡中庭與設防村落則與 TLS（FARO Focus 3D X120）及 UAV 攝影測量 DSM 整合比較。塔樓全點雲對參考模型平均偏差 2.5 cm、標準差 3.4 cm；中庭 12 m×7 m 牆面樣本對 TLS 平均 1.7 cm；礦坑去回程閉合誤差近 40 cm，經人工清理與濾波後平均 5.5 cm；2016 年約 660 m 的環形路徑在 UAV DSM 上平均偏差達 0.53 m，改用 2017 年去回程路徑的樣本約 6 至 7 cm。作者建議 ZEB 點雲適用 1:100 至 1:200 的建築比例，1:50 以上仍需 TLS。","文化資產文件化（San Silvestro 礦業考古園區的地下礦道與城堡遺構、Valperga 城堡），非施工工地；作為手持 SLAM 精度與製圖比例評估方法背景。",[1284,1285,1286,1287,1288],"tower full raw cloud vs CRP: mean 0.025 m, SD 0.034 m; 67% of points within 2 cm (Table 5)","courtyard 12 x 7 m wall vs TLS DSM: mean 0.017 m, SD 0.023 m, 99% within 5 cm (Table 12)","ice house outward vs return after optimisation: 97% within 5 cm (Table 7)","rapid acquisition, e.g. 19 million points in 10 min in the tower (Sec. Test dataset presentation)","suitable for the 1:100 to 1:200 architectural scale (Conclusion)",[1290,1291,1292,1293,1294,1295],"mine cave loop-closure error of almost 40 cm; raw outward vs return mean 0.214 m, SD 0.313 m (Table 8, Fig. 13)","2016 circular 660 m loop in open, irregular terrain drifted: mean 0.531 m vs UAV DSM, 0.578 m in the church area (Tables 16-17)","ZEB1 deviations increase with height because of the spring-driven ray distribution (Sec. courtyard (C))","edges and architectural details become rounded (Fig. 16)","no radiometric data; not adequate at 1:50 or larger scales (Conclusion)","results depend on manual segmentation, cleaning and filtering (Sec. Metric validation)",{"id":1297,"shortName":1298,"title":1299,"year":419,"fulltextStatus":10,"publicationStatus":11,"siteTypes":1300,"taskLevel":14,"tasks":1301,"platforms":1302,"sensorTags":1303,"reference":241,"referenceNote":1304,"engineeringTask":1305,"taskRequirement":27,"requirementSource":27,"siteCount":27,"independentValidation":27,"geometricQuality":1306,"taskOutcome":27,"applicabilityClaim":27,"taskLimitations":27,"sourceLocator":31,"sensorsRaw":1307,"platformRaw":1309,"keyIdeaZh":1312,"constructionRelevance":1313,"strengths":1314,"limitations":1319},"schaub2022pc2bim","Point cloud to BIM registration (SLAM tracking)","Point cloud to BIM registration for robot localization and Augmented Reality",[102],[62],[398],[21,23],"唯一參考是每段錄製起點以人工量測的初始位置；誤差同時包含配準誤差與 SLAM 漂移，漂移的貢獻未量化（Sec. 4.1、4.3）。評估只用手持錄製，Spot 機器人僅作示範。","AR inspection \u002F remote robot control localization in BIM","localization error (0.03 m; 0.2-0.3 m)",[1308],"Ouster OS0-128 Gen 2 LiDAR (128 channels, 90 deg VFOV; 512x20 in env. 1, 1024x20 in env. 2; up to 131,072 points per frame) with IMU data from the sensor",[1310,1311],"handheld: custom wooden frame with dual hold and 24 V battery (all evaluation recordings)","legged robot Boston Dynamics Spot (demonstration setup, not evaluated)","作者以 Kudan LiDAR SLAM 追蹤 Ouster OS0-128 光達（含感測器 IMU 資料），將關鍵影格累積的點雲配準到以 IfcOpenShell 解析並體素化（0.1 m）的 BIM 點雲：先以法向量角度直方圖做軸向對齊，再以只保留垂直於投影面點的法向過濾樣板匹配（每 1° 測試）粗對位，最後以隨機子取樣 ICP 精對位，得到感測器在 BIM 座標中的位姿，供擴增實境檢查與 Boston Dynamics Spot 遠端操作使用。評估只用手持錄製資料，且以每段錄製起點人工量測的初始位置作為唯一參考：28 m 走廊（只含走廊的 BIM）各關鍵影格中位數平均 XY 誤差 0.03 m、Z 0.035 m，全部成功；TU Wien 圖書館六樓含多個相似房間的環形走廊，前 10 個關鍵影格成功率只有 30%，到第 36 個關鍵影格前平均 XY 0.19 m、Z 0.24 m，之後 XY 低於 0.3 m、Z 低於 0.4 m。誤差同時包含配準誤差與 SLAM 漂移。","應用動機為建築進度控制、數位輔助維護與遠端巡檢；測試在 TU Wien 研究室旁約 28 m 長、2.5 m 寬的走廊，以及 TU Wien 圖書館六樓的環形走廊進行，兩者都是既有建築；圖書館樓層可見 BIM 未記載的家具、植物與牆體、門窗差異，且無法進入個別房間；非施工中工地（Sec. 1、4.2、5）。",[1315,1316,1317,1318],"env. 1 (28 m x 2.5 m hallway, hallway-only BIM): XY error 0.03 m and Z 0.035 m (medians averaged over keyframes); registration successful at all keyframes of 10 recordings (Sec. 4.3)","env. 2: registration virtually always successful from keyframe 28; mean XY 0.19 m and Z 0.24 m up to keyframe 36 (Sec. 4.3)","supports non-perpendicular walls and varying floor levels, unlike Herbers and Konig (Sec. 5)","registration runs in the background without degrading live tracking (Sec. 4.3)",[1320,1321,1322,1323,1324],"self-similar floor plan causes wrong template matches for up to 20 first keyframes; success only 30% in keyframes 2-10 and 50-90% in keyframes 12-26 (Sec. 4.3, 5)","error grows with accumulated SLAM drift, notably after keyframe 38 (about 60.8 m); afterwards XY below 0.3 m and Z below 0.4 m (Sec. 4.3)","ground truth only for the initial sensor position, measured manually; SLAM drift contribution not quantified (Sec. 4.1)","differences between BIM and building (furniture, plants, missing or extra walls, doors, windows) hamper registration (Sec. 5)","only corridors on one floor recorded; rooms not accessible (Sec. 4.2)",{"id":1326,"shortName":1327,"title":1328,"year":286,"fulltextStatus":10,"publicationStatus":11,"siteTypes":1329,"taskLevel":14,"tasks":1330,"platforms":1331,"sensorTags":1332,"reference":66,"referenceNote":110,"engineeringTask":1333,"taskRequirement":27,"requirementSource":27,"siteCount":1334,"independentValidation":1335,"geometricQuality":1336,"taskOutcome":27,"applicabilityClaim":1337,"taskLimitations":1338,"sourceLocator":1339,"sensorsRaw":1340,"platformRaw":1344,"keyIdeaZh":1346,"constructionRelevance":1347,"strengths":1348,"limitations":1353},"schillberg2025quadrupedasbuilt","Quadruped LiDAR-SLAM as-built documentation","Autonomous As-Built Documentation with a Quadruped Robot and LiDAR-SLAM",[102],[424,326],[290],[21,23],"as-built documentation (geometric point cloud capture)","1 building, 3 environments","Leica TS30 total station tracking 360-degree prism; Riegl VZ-400i\u002FVZ-600i TLS","RMS APE and RPE vs total-station trajectory (Table 2); mean C2C vs TLS after point-pair + ICP alignment (Table 3)","authors state Spot + Ouster + open-source SLAM can automate geometric as-built documentation cost-effectively and may be used on construction sites","single building; C2C after ICP alignment; Autowalk-defined routes","Methods; Results; Tables 2-3; Discussion",[1341,1342,1343],"3D LiDAR (Ouster OS1-128) with internal 6-axis IMU","reference: Leica TS30 total station with 360-degree prism","reference: Riegl VZ-400i and VZ-600i TLS",[1345],"legged (Boston Dynamics Spot, Autowalk missions)","作者以搭載 Ouster OS1-128 的 Boston Dynamics Spot，在走廊（44 m 開放路線）、地下室（49 m 封閉迴圈）與連接兩層的樓梯間（32 m）比較 KISS-ICP、FAST-LIO2 與 LIO-SAM-6AXIS。以 Leica TS30 全測站追蹤光達上方稜鏡取得參考軌跡，並提出時間同步與中斷資料拼接方法；另以 RIEGL VZ-400i 與 VZ-600i 地面掃描作為點雲參考。FAST-LIO2 在三種場景中有兩種 APE 最低、三種點雲平均距離皆最低（3.7 至 5.6 cm），點雲也最密；地下室的 RMS APE 為 9 至 35 cm，機器人上下樓梯時誤差明顯增加。","以竣工紀錄為目標，但場景為既有建築空間（走廊、地下室、樓梯間），非施工中工地（Abstract）。",[1349,1350,1351,1352],"FAST-LIO2 lowest mean C2C in all three environments (3.7-5.6 cm) and lowest RMS APE in 2 of 3 (Tables 2-3)","method to synchronize total-station ground truth with SLAM data when line of sight is interrupted (Discussion)","FAST-LIO2 clouds were the densest (6.8-12.1 million points vs 0.27-0.36 million for LIO-SAM-6AXIS) with minimal noise (Table 3, Results)","FAST-LIO2 showed little drift when revisiting the cellar start despite no explicit loop closure, attributed to its large local map (Results, Discussion)",[1354,1355,1356,1357,1358,1359,1360],"cellar RMS APE 9-35 cm; all algorithms struggled after the first curve (Results)","errors increase on stairs, partly from prism shifting with robot tilt (about 3-5 cm) (Results)","point clouds aligned to TLS by point pairs + ICP before C2C, which evaluates global quality but can absorb drift (Discussion)","KISS-ICP double surfaces near the cellar start; KISS-ICP and LIO-SAM-6AXIS strong shearing on the lower stairwell floor (Results)","FAST-LIO2 and KISS-ICP slightly poorer coverage at the hallway start, possibly due to the LiDAR minimum range (Results)","SLAM parameters (voxel and leaf sizes) tuned empirically for indoor density; trajectory alignment uses only the first 1.5 m (Methods)","(inference) single building, single run per environment",{"id":1362,"shortName":1363,"title":1364,"year":209,"fulltextStatus":10,"publicationStatus":11,"siteTypes":1365,"taskLevel":14,"tasks":1366,"platforms":1367,"sensorTags":1368,"reference":241,"referenceNote":1369,"engineeringTask":110,"taskRequirement":110,"requirementSource":110,"siteCount":110,"independentValidation":110,"geometricQuality":110,"taskOutcome":110,"applicabilityClaim":110,"taskLimitations":110,"sourceLocator":110,"sensorsRaw":1370,"platformRaw":1372,"keyIdeaZh":1373,"constructionRelevance":1374,"strengths":1375,"limitations":1377},"shang2018_uav_vslam","Shang & Shen UAV RGB-D SLAM pilot","Real-Time 3D Reconstruction on Construction Site Using Visual SLAM and UAV",[60],[424,619],[19],[292],"以後處理攝影測量模型上 8 個參考點的邊長比較，非測量控制。",[1371],"RGB-D",[333],"本研究為營建現場的先導研究：在 DJI Matrice 600 六旋翼無人機底部朝下安裝 Intel RealSense R200 RGB-D 相機，以機上 Jetson TX1 執行 RTAB-Map 即時重建工地。作者以後處理攝影測量模型為參考，在碎石基礎溝槽角點選取 8 個參考點比較邊長距離，並示範以兩次 SLAM 點雲的 C2C 距離呈現三小時內的土方變化。研究同時指出 RGB-D 深度量測在戶外受照度影響、有效距離有限而造成點雲空洞。","直接於戶外營建現場（碎石基礎溝槽）測試 RGB-D 視覺 SLAM；但參考為攝影測量而非獨立測量控制，只比較 8 點間距離，且未考慮量測誤差，屬先導級證據，不能推論為工程容許差合格。",[1376],"Near real-time model generation with little post-processing compared with photogrammetry (field applications; conclusion)",[1378,1379,1380,1381,1382,1383],"SLAM cloud density lower than photogrammetry (702,089 vs 1,195,326 points) (accuracy evaluation)","Holes in the centre of the model due to limited IR sensing range (accuracy evaluation)","RGB-D camera sensitive to outdoor illumination; parameters had to be tuned; best sensing range 0.5-5 m (experimental setup)","Reference was photogrammetry, not an independent survey, and measurement errors were not considered (accuracy evaluation)","Memory grows with mapping: over 5000 frames produced a map over 3.6 GB, degrading an onboard computer with about 4 GB memory (site asset tracking)","Authors list limited stereo sensing, onboard memory accumulation and UAV manoeuvring in clutter as open challenges (conclusion)",{"id":1385,"shortName":1386,"title":1387,"year":286,"fulltextStatus":10,"publicationStatus":11,"siteTypes":1388,"taskLevel":14,"tasks":1390,"platforms":1391,"sensorTags":1392,"reference":66,"referenceNote":1393,"engineeringTask":1394,"taskRequirement":27,"requirementSource":27,"siteCount":1395,"independentValidation":1396,"geometricQuality":1397,"taskOutcome":27,"applicabilityClaim":27,"taskLimitations":27,"sourceLocator":31,"sensorsRaw":1398,"platformRaw":1403,"keyIdeaZh":1406,"constructionRelevance":1407,"strengths":1408,"limitations":1415},"stuhrenberg2025liobim","LIO-BIM","LIO-BIM - Coupling lidar inertial odometry with building information modeling for robot localization and mapping",[102,1389,60],"public_benchmark",[62,63],[290,398],[21,23,22],"使用 ConSLAM 真值軌跡與 TLS 掃描；ConSLAM 真值軌跡的不確定度未量化；評估 ConSLAM 時使用的 BIM 由序列 2 的 TLS 真值掃描建立（Sec. 4.6）。","robot localization\u002Fmapping relative to BIM for progress monitoring or quality inspection","1 office (113 m trajectory) + ConSLAM sequences 2-5 (225-340 m)","ConSLAM ground-truth trajectories and TLS scans; SLAM2REF ground truth (Tables 5-7)","APE RMSE translation\u002Frotation (Tables 5-6); inlier RMSE and fitness vs TLS (Table 7)",[1399,1400,1401,1402],"3D LiDAR (Velodyne VLP-16)","9-DoF IMU (LORD MicroStrain 3DM-GX5-25; Xsens MTi-610 in ConSLAM)","camera for AprilTag detection (Intel RealSense D435i; Alvium U-319c 3.2 MP in ConSLAM)","reference: Faro Focus S 70 TLS (office); Leica RTC 360 TLS scans of ConSLAM",[1404,1405],"legged (Unitree A1-based 'IDOG')","handheld (ConSLAM dataset)","作者指出僅依 BIM 導出地圖定位需要高發展程度（LOD）模型，且非結構物件常與模型不符。LIO-BIM 以光達慣性里程計持續建立現況地圖，並將機器人周邊的局部地圖與 BIM 做掃描匹配，以同時取得相對 BIM 的定位與現況建圖。系統實作於四足機器人並在辦公室環境與 ConSLAM 工地資料集驗證，程式碼以開源釋出。","以 ConSLAM（施工中建築資料集）驗證（摘要）；另有辦公室實測。",[1409,1410,1411,1412,1413,1414],"ConSLAM translational APE RMSE 10.21-15.68 cm across sequences 2-5 vs ConSLAM ground truth (Table 5)","against SLAM2REF ground truth, 5.97 cm (seq 2) and 12.57 cm vs 18.02 cm for LIO-SAM (seq 5) (Table 6)","point-cloud inlier RMSE (0.3 m threshold) 6.3-7.9 cm vs TLS; office 6.57 cm vs 8.42 cm for LIO-SAM (Table 7)","a wrongly placed wall in the office BIM did not corrupt the map because low-compliance matches were rejected (Sec. 6, Fig. 16)","authors state the accuracy meets the +-10 cm needed for half-cell potential corrosion surveys except for a few outliers (Sec. 6)","open-source code (GitHub)",[1416,1417,1418,1419,1420,1421],"not uniformly better than LIO-SAM: LIO-SAM had lower APE RMSE on ConSLAM sequences 3 and 4 against both ground truths (Tables 5-6) and lower inlier RMSE on sequences 3 and 4 (Table 7)","error grows from 10.21 cm (seq 2) to 15.68 cm (seq 5, 4.5 months later) as the site departs from the BIM (Sec. 6)","AprilTags must be placed manually at matching locations in the building and the BIM; BIM matching starts only after a tag is seen (Sec. 3.2, Sec. 7)","parameters chosen experimentally; lidar reflections in windows create fictitious walls (Sec. 4.4, Sec. 6, Sec. 7, Fig. 15)","BIM scan matching skips 23.6-33.1% of keyframes on ConSLAM because of processing time (Table 8)","(inference) the ConSLAM BIM was derived from the TLS scan of sequence 2, so it is an as-built model and likely favours sequence 2",{"id":1423,"shortName":1424,"title":1425,"year":1426,"fulltextStatus":10,"publicationStatus":11,"siteTypes":1427,"taskLevel":103,"tasks":1428,"platforms":1429,"sensorTags":1430,"reference":27,"referenceNote":1431,"engineeringTask":1432,"taskRequirement":1433,"requirementSource":27,"siteCount":395,"independentValidation":395,"geometricQuality":395,"taskOutcome":1434,"applicabilityClaim":1435,"taskLimitations":395,"sourceLocator":625,"sensorsRaw":1436,"platformRaw":1438,"keyIdeaZh":1439,"constructionRelevance":1440,"strengths":1441,"limitations":1446},"tang2011flatness","TLS flatness-defect detection characterization","Characterization of Laser Scanners and Algorithms for Detecting Flatness Defects on Concrete Surfaces",2011,[13],[105],[107],[109],"以試驗平板上已知尺寸的黏土模擬缺陷作任務層驗證（偵測、定位與誤報），不是點雲幾何參考（Flatness Defect Detection Test Bed；Fig. 3）。","concrete surface flatness defect detection","deviation from a flat reference beyond a specified tolerance (abstract, generic)","defect detectability by scanner and algorithm (abstract)","laser scanners can be effectively used to assess surface flatness (abstract)",[1437],"three terrestrial panoramic laser scanners, models not named in the paper: Scanner 1 AMCW (FoV 360 x 310 deg; 0.036 and 0.018 deg tested), Scanner 2 TOF (about 5,000 points\u002Fs; 0.014 and 0.007 deg), Scanner 3 TOF (0.014 deg)",[124],"作者指出直尺與剖面儀等傳統平整度檢查速度慢、量測稀疏且需接觸表面，因此提出以地面雷射掃描點雲偵測混凝土平整度缺陷。三種演算法（距離影像濾波 RF、偏差濾波 DF、滑動視窗 SW）都先以整體最小平方法擬合參考平面，差別只在降噪與偏差計算步驟。作者以含黏土模擬缺陷（直徑 3 至 50 cm、厚 1 至 7 mm）的水平試驗平板，定義偵測、定位與誤報三項指標，比較一台 AMCW 與兩台 TOF 掃描儀在 3 至 20 m 距離與不同角解析度下的表現。SW 偵測表現最好但有時定位較差；距離增加使表現下降，AMCW 下降較快。摘要所稱 20 m 可偵測 3 cm 寬、1 mm 厚缺陷，對應 Scanner 2 搭配 SW 的「偵測但未定位」結果（表 6）。","以水平放置的試驗平板（黏土模擬缺陷）在受控條件下評估 TLS 掃描儀與演算法偵測混凝土平整度缺陷的能力，情境模擬樓板檢查；非 SLAM，也非實際工地。提供品質檢查任務層框架：掃描距離、角解析度、入射角與演算法如何改變缺陷偵測與定位。",[1442,1443,1444,1445],"Objective evaluation framework (test bed, detection, localization and precision measures, test procedure) for comparing scanners and algorithms (Framework)","SW algorithm shows equal or better detection in all test cases (Conclusions; Fig. 4)","3 cm x 1 mm defect detected at 20 m by SW with Scanner 2 (TOF, 0.014 deg), though not localized (Table 6)","Performance maps support inspection-rate estimates: 0.346, 0.1 and 0.65 m2\u002Fs for Scanners 1-3 in a hypothetical slab task (Inspection Rates section)",[1447,1448,1449,1450,1451,1452],"Only one-time scans of horizontal boards; no probability-of-detection statistics; non-horizontal surfaces untested (Discussion; Conclusions)","Within 6 m, noisy data at board boundaries merge defects with false alarms, especially for SW (Discussion; Fig. 6)","Beyond 6 m, shallow incidence causes occlusion and mixed pixels behind defects of 5 mm or thicker, elongating detections (Discussion)","Sparse, varying data density (4.3 cm spacing at 10 m) hampers the image-based RF and DF algorithms (Discussion)","SW sometimes has worse localization than RF and DF because it produces larger defect regions (Comparison of Algorithms)","Per a citing study, the defect metrics are not compatible with current standard flatness specifications (Straightedge, F-Numbers), making compliance assessment difficult (bosche2014flatness Sec. 2.2)",{"id":1454,"shortName":1455,"title":1456,"year":1457,"fulltextStatus":10,"publicationStatus":11,"siteTypes":1458,"taskLevel":103,"tasks":1459,"platforms":1460,"sensorTags":1461,"reference":66,"referenceNote":110,"engineeringTask":1462,"taskRequirement":1463,"requirementSource":27,"siteCount":1464,"independentValidation":1465,"geometricQuality":1466,"taskOutcome":1467,"applicabilityClaim":1468,"taskLimitations":1469,"sourceLocator":1470,"sensorsRaw":1471,"platformRaw":1475,"keyIdeaZh":1476,"constructionRelevance":1477,"strengths":1478,"limitations":1482},"thomson2013mlsindoor","IMMS fit-for-purpose test for BIM (i-MMS, ZEB1)","Mobile Laser Scanning for Indoor Modelling",2013,[102],[326,424],[1056,398],[451,22],"as-built BIM geometry creation (Scan-to-BIM) of a building corridor","not_reported (authors mention millimetre-level accuracy for survey engineering and monitoring as a benchmark, Sec. 5)","1 (single corridor)","Faro Focus3D TLS (12 scans, 32 tie points) georeferenced with a Leica TS15 total-station network adjusted in LGO; total-station element measurements","ICP RMS and cloud-to-cloud deviation (height function, least-squares planes) after ICP alignment to TLS","door and window width and height differences between Revit models from each data source","suitable for asset capture and facility management; not for millimetre-level survey engineering or monitoring (Sec. 5)","single site; comparison after ICP alignment; manual artefact removal","Sec. 3-5, Tables 1-4",[1472,1473,1474],"Viametris i-MMS trolley: three Hokuyo line scanners (270 deg swath; two produce the point cloud, one upright scanner feeds the 2D SLAM) and a Point Grey Ladybug spherical camera","ZEB1 handheld: the same Hokuyo scanner on a spring with a small IMU, oscillated by the operator, online 6-DoF SLAM","reference: Faro Focus3D phase-shift TLS (12 scans, 32 tie points), Leica Viva TS15 total station network adjusted in LGO 8.1",[1056,405],"作者在倫敦大學學院一段約 39 m × 7 m × 5 m 的走廊，比較台車式 i-MMS（2D SLAM）與手持式 ZEB1（結合 IMU 的 6 自由度 SLAM）兩種室內行動測繪系統，並以 Faro Focus3D 地面雷射掃描（TLS）加全測站控制網作為參考。比較分兩層：先以 ICP 把行動點雲對齊 TLS 再計算點雲間距離（ICP RMS 約 2.5 cm）；再比較由各點雲在 Revit 建立的 BIM 幾何，特別是 10 個門與 7 個窗的寬高差（平均數公分，最大 42 cm）。作者結論是這類系統適合資產盤點與設施管理，但不適合需要毫米級精度的測量工程與監測。","已完工且使用中的建物（UCL South Cloisters 一樓走廊）之 BIM 幾何建立適用性測試，非施工中工地；屬早期（2013）以 SLAM 行動掃描做任務層比較（門窗尺寸）的非 MDPI 研究，是否為最早之一未經系統檢索確認（推論）。",[1479,1480,1481],"Residuals after ICP registration to TLS were within a few centimetres for both systems (Tables 1-2, Sec. 4.1.2)","Large time savings relative to 12 TLS scans taking about 5 hours including control survey (Sec. 3.2, Sec. 5)","Handheld form factor can reach areas such as stairwells that trolleys cannot (Sec. 2)",[1483,1484,1485,1486,1487,1488,1489],"Not adequate for applications requiring millimetre-level accuracy such as survey engineering and monitoring (Sec. 5)","i-MMS SLAM is 2D only, restricting use to areas without significant height change (Sec. 2.1)","Artefacts from people and glass were removed manually before comparison (Sec. 4.1.1)","Visible noise in the mobile clouds at detail level (Sec. 4.1.3, Fig. 7)","Wall thickness on the office side could not be measured by laser scanning because offices were inaccessible (Sec. 3.1)","Single corridor, single test (Sec. 3.1)","(inference) Point-cloud comparison was done after a best-fit rigid ICP alignment to the TLS cloud, so reported deviations reflect residual distortion and noise, not absolute georeferencing accuracy; drift is captured only insofar as it distorts the cloud relative to the rigid fit",{"id":1491,"shortName":1492,"title":1493,"year":616,"fulltextStatus":10,"publicationStatus":11,"siteTypes":1494,"taskLevel":14,"tasks":1495,"platforms":1496,"sensorTags":1497,"reference":66,"referenceNote":110,"engineeringTask":1498,"taskRequirement":27,"requirementSource":27,"siteCount":1499,"independentValidation":1500,"geometricQuality":1501,"taskOutcome":27,"applicabilityClaim":27,"taskLimitations":1502,"sourceLocator":1503,"sensorsRaw":1504,"platformRaw":1509,"keyIdeaZh":1510,"constructionRelevance":1511,"strengths":1512,"limitations":1518},"trybala2023lowcosttunnel","Low-cost vs ZEB Horizon in collapsed tunnel","COMPARISON OF LOW-COST HANDHELD LIDAR-BASED SLAM SYSTEMS FOR MAPPING UNDERGROUND TUNNELS",[421],[423,424],[398],[451,21,23],"underground tunnel 3D mapping (ventilation modelling context)","1 tunnel","Riegl VZ-400i TLS","M3C2 distances, local std in cross-sections\u002FROIs (Table 2), voxel TP\u002FFN\u002FFP (Table 3)","single site; short paper","Sec. 2-4; Tables 1-3",[1505,1506,1507,1508],"GeoSLAM ZEB Horizon (300,000 pts\u002Fs, 100 m, 30 mm at 100 m)","in-house Velodyne VLP-16 on a Dynamixel servomotor, 3D printed handheld rig, no IMU","in-house Livox Horizon (240,000 pts\u002Fs, 90 m, 20 mm at 25 m) with internal IMU, hand-carried","reference TLS RIEGL VZ-400i (60 stations, plane-patch registration SD 2.6 mm)",[405],"作者在部分坍塌的地下坑道比較兩套自製低成本手持光達 SLAM（致動式 Velodyne、Livox Horizon）與商用 GeoSLAM ZEB Horizon，以 Riegl VZ-400i 地面掃描為參考。除 M3C2 距離與局部剖面標準差外，提出以迭代體素化 ICP 精修後計算體素佔據（TP\u002FFN\u002FFP）的完整度評估，以降低漂移對完整度估計的影響。結果顯示各系統在精度與完整度上各有強弱，需依應用重點選擇。","二戰時期 Riese 計畫開鑿、部分坍塌的 Gontowa 坑道（波蘭 Owl 山區）；研究脈絡為礦業通風模擬（VOT3D 計畫）。提供 SLAM 點雲完整度評估方法，可轉用於隧道與地下工程（推論）。",[1513,1514,1515,1516,1517],"voxel completeness at 50 cm: TP 82% (GeoSLAM), 88% (actuated Velodyne), 74% (Livox) (Table 3)","at 20 cm voxels TP\u002FFN\u002FFP: GeoSLAM 61\u002F3\u002F36%, actuated Velodyne 57\u002F25\u002F18%, Livox 49\u002F30\u002F21% (Table 3)","Livox achieved the best accuracy curve; GeoSLAM and actuated Velodyne completeness quickly approached 100% (Sec. 3, Fig. 14)","IMU-free actuated Velodyne rig may be more robust in high-vibration industrial mines (Sec. 2.3, Sec. 4)","iterative voxelized ICP refinement reduces drift influence on completeness (Sec. 4)",[1519,1520,1521,1522,1523,1524],"GeoSLAM heavy noise near narrow passage; Livox FoV gaps; actuated Velodyne uniformly higher noise (Sec. 3)","M3C2 distance SD to TLS above 200 mm for all systems (364, 281, 232 mm), attributed to drift without global positioning; outliers deliberately kept (Sec. 3, Table 2)","Livox double wall and double floor in cross-section A-A' and ROI 2 (Sec. 3)","drift compensation needs reference data and serves evaluation only, not mapping (Sec. 2.5)","critical metric thresholds are application-dependent (Sec. 4)","(inference) single site and a single pass per system",{"id":1526,"shortName":1527,"title":1528,"year":234,"fulltextStatus":10,"publicationStatus":11,"siteTypes":1529,"taskLevel":103,"tasks":1530,"platforms":1531,"sensorTags":1532,"reference":24,"referenceNote":1533,"engineeringTask":1534,"taskRequirement":27,"requirementSource":27,"siteCount":1535,"independentValidation":27,"geometricQuality":27,"taskOutcome":27,"applicabilityClaim":27,"taskLimitations":27,"sourceLocator":31,"sensorsRaw":1536,"platformRaw":1540,"keyIdeaZh":1543,"constructionRelevance":1544,"strengths":1545,"limitations":1550},"tuomisto2026quadrupedbim","BIM-initialized LiDAR SLAM quadruped inspection","Automating on-site object inspection with a quadruped robot and BIM",[211,102],[62,238],[290,211],[21,292],"未量測定位或地圖誤差，只報告任務執行成功率，屬任務層評估（Sec. 6；Table 1）。","object inspection with BIM-driven navigation","1 real-world trial in one test building + 2 simulated experiments of 20 trials each",[1537,1538,1539],"3D LiDAR (Ouster OS0, 64 beams, 90 deg vertical and 360 deg horizontal FoV) for SLAM and mapping","RGB-D camera (Intel RealSense D415) for inspection images","Spot built-in RGB, five monochrome, six depth and one infrared cameras used by Spot's own obstacle avoidance",[1541,1542],"legged (Boston Dynamics Spot)","simulation (Unreal Engine 5 digital twin)","作者在 Boston Dynamics Spot 上加裝 Ouster OS0 光達與 RealSense D415，以 Kitware LiDAR SLAM 並用 BIM 結構元件點雲初始化平面特徵地圖，使機器人相對 BIM 定位。另建 0.1 m 體素的機率式 3D 地圖，逐體素記錄 BIM 元件是否被光達證實，並在 Nav2 成本地圖中對已證實結構、未證實 BIM 元件與暫時障礙物給予不同代價；再以粒子群最佳化的下一最佳視角規劃拍攝目標物，交由視覺語言模型判斷是否安裝。兩組各 20 次模擬的執行成功率為 97.5% 與 100%，實地單次試驗 6 個目標全數完成；論文未報告定位誤差。","目標是施工中工地與既有建築的 BIM 物件檢查；實測僅在模擬所依據的同一棟既有建築進行一次，施工情境（隨機障礙物、移動工人、未建牆）只在 Unreal Engine 5 數位分身中模擬；論文未量測定位或地圖幾何精度，證據屬任務層。",[1546,1547,1548,1549],"execution success 97.50% (dynamic simulation) and 100% (BIM-discrepancy simulation), 6 of 6 targets in the real trial (Table 1)","found the only viable path through unbuilt BIM walls in all 20 trials of Sim. Experiment 2 (Sec. 4.1)","ablation: Non-Semantic variant fell to 81.67% and 55.00% execution success, No NBV Retries to 80.00% and 76.67% (Table 2)","VLM precision near 100% in simulation and correct inference of a curtain-covered window in the real trial (Table 1; Sec. 4.1)",[1551,1552,1553,1554,1555,1556,1557],"no localization or map error metrics; evaluation is task-level only (Sec. 6)","single real-world trial; simulated experiments lack Spot's proprietary low-level controller (Sec. 3.7.1, Sec. 6)","manual approximate initial pose relative to BIM; no global localization because of structural symmetry (Sec. 3.4.1, Sec. 6)","requires BIM of LoD 300 or higher; tolerance to BIM discrepancies not systematically evaluated (Sec. 6)","robot arm partly occluded the forward LiDAR view in the real trial (Sec. 4.1)","single-floor 2D navigation only; no active search for misplaced objects (Sec. 4.1, Sec. 6)","simulated VLM accuracy depends on rendering fidelity (reception desk 10-15%); real VLM result rests on six images (Sec. 4.2.2, Sec. 6)",{"id":1559,"shortName":1560,"title":1561,"year":616,"fulltextStatus":10,"publicationStatus":11,"siteTypes":1562,"taskLevel":14,"tasks":1563,"platforms":1564,"sensorTags":1565,"reference":24,"referenceNote":1566,"engineeringTask":110,"taskRequirement":110,"requirementSource":110,"siteCount":110,"independentValidation":110,"geometricQuality":110,"taskOutcome":110,"applicabilityClaim":110,"taskLimitations":110,"sourceLocator":110,"sensorsRaw":1567,"platformRaw":1568,"keyIdeaZh":1569,"constructionRelevance":1570,"strengths":1571,"limitations":1574},"bimslam2023","BIM-SLAM","BIM-SLAM: Integrating BIM Models in Multi-session SLAM for Lifelong Mapping using 3D LiDAR",[211,395],[62,619],[290,211],[21],"實測資料只有定性結果；數值來自模擬序列。",[77],[290,211],"BIM-SLAM 從 IFC 模型萃取牆、樓板、天花等永久構件，利用佔據格網轉位姿圖工具與 Gazebo 模擬 3D 光達，產生具真值位姿的「BIM 時段資料」（位姿圖、關鍵影格點雲與 Scan Context 描述子）。真實時段再以錨節點多時段位姿圖對齊到被強先驗固定的 BIM 時段，不需已知初始位姿、也不需從模型範圍內出發；對齊後以點到網格帶號距離找出 BIM 中不存在的新物件，經 DBSCAN 分群後以體素立方體網格呈現。","營建自動化場域（ISARC）研究，直接以 BIM 作為多時段 SLAM 的參考時段，並偵測模型外的新物件（正向差異）。定量結果只來自 Gazebo 模擬序列（Table 1）；真實資料以搭載於 Go1 四足機器人的 Ouster OS1-32 攜帶式建圖系統，在與模擬相同的室內環境蒐集（該系統亦可手持；Sec. 4.2），僅有定性結果（Fig. 5）。文中未說明該建築的類型或是否為施工中工地，也尚未處理 BIM 中已不存在的構件（負向差異列為未來工作；Sec. 6）。",[1572,1573],"No prior initial pose and no need to start inside the prior map (Sec. 5, 6)","Aligned data enables detection of new or missing components relative to BIM (Sec. 3.4, 5)",[1575,1576,1577,1578],"Drift reduction only slightly better than SC-A-LOAM on the simulated sequence (Sec. 5, Table 1)","Large BIM-reality discrepancies, minimal scan-BIM overlap or symmetric environments may prevent correct alignment (Sec. 5)","Only positive differences (new objects) are detected; negative differences (removed BIM elements) are future work (Sec. 6)","(inference) Real-world evaluation is qualitative only (Fig. 5); no independent reference measurement",{"id":1580,"shortName":1581,"title":1582,"year":774,"fulltextStatus":10,"publicationStatus":11,"siteTypes":1583,"taskLevel":14,"tasks":1584,"platforms":1585,"sensorTags":1586,"reference":216,"referenceNote":1587,"engineeringTask":1588,"taskRequirement":1589,"requirementSource":27,"siteCount":1590,"independentValidation":1591,"geometricQuality":1592,"taskOutcome":1593,"applicabilityClaim":1594,"taskLimitations":1595,"sourceLocator":1596,"sensorsRaw":1597,"platformRaw":1600,"keyIdeaZh":1602,"constructionRelevance":1603,"strengths":1604,"limitations":1608},"vegatorres2024slam2ref","SLAM2REF","SLAM2REF: advancing long-term mapping with 3D LiDAR and reference map integration for precise 6-DoF trajectory estimation and map extension",[60,1389],[62,619],[398],[21,23],"評估參考是最終 ICP 所用的同一張 ConSLAM TLS 地圖；與 ConSLAM 提供的真值軌跡僅作差異比較。","long-term map alignment to BIM or TLS, map extension and change detection (construction monitoring)","not_reported (authors state ground-truth poses ideally need about 1 cm accuracy, Sec. 6)","1 construction site (ConSLAM), 4 sequences","not independent: the evaluation reference is the same TLS map used for the final ICP","APE RMSE of trajectories versus TLS-referenced poses; registration RMSE and fitness at 1 cm and 3 cm","qualitative positive and negative difference maps (Figs. 13-14)","construction site monitoring and other domains needing fast-updated 3D maps (abstract)","as-built BIM derived from TLS; reflections; initial-pose sensitivity; offline runtime","Sec. 4.3, 5-8",[1598,1599],"3D LiDAR of the ConSLAM handheld system (model not reported; the ISC descriptor requires a 360-degree horizontal FoV)","9-axis IMU of the ConSLAM handheld system (model not reported; used for DLIO deskewing; LiDAR-IMU extrinsics from OA-LICalib)",[1601],"handheld (ConSLAM sequences)","SLAM2REF 把行動 LiDAR 與 IMU 資料和既有 BIM 或點雲參考圖整合，用於室內無 GPS 環境的長期建圖。流程先由參考圖產生佔據網格與模擬 LiDAR 掃描作為「參考工作段」，再以 DLIO 去除實測掃描的運動畸變，接著用室內版 Scan Context 描述子與 YawGICP 找跨工作段對應，透過多工作段錨定（multi-session anchoring）位姿圖最佳化把漂移的 SLAM 結果對齊參考圖，最後逐幀以點對點 ICP 對齊 1 cm 密度的參考點雲。對齊後以 OctoMap 分析新增與移除的構件並網格化，並允許地圖延伸到參考圖範圍之外。","以 ConSLAM 施工中建物資料集（手持設備、四個序列）驗證，並以 seq 2 的 TLS 建立約半公分精度的 BIM（as-built，非設計模型）。延續 [bimslam2023] 的 BIM-SLAM 路線；作者說明相較 BIM-SLAM 增加大型參考圖、IMU 去畸變、最終 ICP 與容許掃描-地圖差異的能力（Sec. 7）。",[1605,1606,1607],"Automatic alignment without manual initialization of the first keyframe, unlike ground-truth generation in ConSLAM or Newer College (Sec. 7)","Alignment to a clutter-free BIM despite scan-map deviations from clutter, furniture and dynamic objects (Sec. 1, 6)","Not restricted to Manhattan-world layouts; supports extending the reference map (Sec. 7)",[1609,1610,1611,1612,1613,1614,1615,1616,1617,1618],"Sensitive to the initial SLAM or LIO poses; large drift, especially Z-drift in narrow corridors without floor or ceiling points, may not be corrected (Sec. 8, Fig. 15)","Large deviations of permanent walls or columns, low overlap or symmetric environments can defeat alignment; the final ICP may be wrong where the reference map is wrong (Sec. 8)","Not real-time; final ICP can take several dozen minutes (Sec. 8)","Indoor Scan Context needs a 360-degree horizontal FoV, so solid-state LiDARs and depth cameras are not directly supported (Sec. 8)","Window reflections create fictitious elements in change detection (Sec. 8)","Only clutter and dynamic-object deviations are addressed, not alterations of permanent building elements (Sec. 1)","After the KNN loops, rotational APE rises in S3 and S5 (Table 1, Sec. 6); Sec. 7 explains this pattern (naming it for sequences 2 and 5 there) by erroneous KNN loops detected where the ISC-aligned trajectory, before Umeyama alignment, still deviated about 1.5 m in Z and X from the ground truth; the final ICP filters these loops (Sec. 6, Sec. 7, Table 1)","Correct ISC correspondences are very sensitive to the number of top candidates (N_c = 100) (Sec. 5.2.2)","(inference) Final-ICP poses to the TLS map serve as ground truth, so the TLS-referenced final stage is not independently evaluated","(inference) The ConSLAM BIM was modelled from the TLS point cloud of sequence 2 (Sec. 5.1), i.e. an as-built model, so design-versus-as-built discrepancy (brief Sec. 11 risk 2) is not tested; the same caveat is recorded for stuhrenberg2025liobim in C11b",{"id":1620,"shortName":1621,"title":1622,"year":616,"fulltextStatus":10,"publicationStatus":11,"siteTypes":1623,"taskLevel":14,"tasks":1624,"platforms":1625,"sensorTags":1626,"reference":216,"referenceNote":110,"engineeringTask":1627,"taskRequirement":27,"requirementSource":27,"siteCount":1628,"independentValidation":1629,"geometricQuality":1630,"taskOutcome":27,"applicabilityClaim":1631,"taskLimitations":1632,"sourceLocator":1633,"sensorsRaw":1634,"platformRaw":1635,"keyIdeaZh":1637,"constructionRelevance":1638,"strengths":1639,"limitations":1643},"yin2023semanticbimloc","Semantic localization on BIM maps","Semantic localization on BIM-generated maps using a 3D LiDAR sensor",[102],[62],[190],[21],"robot\u002Fsensor localization in BIM frame","1 building, 10 sequences","no (SLAM-derived reference)","trajectory RMSE vs Cartographer reference (2D)","authors limit claims to static environments and call for BIM updating on sites","completed building; 2D evaluation; non-independent GT","Sec. 4.1-4.5; Table 4",[1399],[1636],"not_reported: Fig. 5 shows a Velodyne VLP-16 with portable power and a laptop; the carrier is not stated; data collected with planar motion (Sec. 4.1, 4.4)","作者將 BIM 依樓層拆分，經 IfcOpenShell 轉為網格後取樣成帶有構件類別的語意點雲地圖，免除事先以 SLAM 建圖。定位時先做點對面 ICP，再依語意一致性篩選並加權的 ICP 精化位姿。實驗在新加坡國立大學六層校舍（已完工使用）進行，參考軌跡取自離線 2D Cartographer SLAM，非獨立測量。","測試於 NUS SDE4 已完工六層校舍的 2-5 樓（10 段、總長逾 340 m）；作者明言施工中動態環境需更新 BIM（Sec. 4.1, 4.5）。",[1640,1641,1642],"overall translation RMSE 0.080 m and rotation 0.663 deg; 34% translation improvement over baseline ICP (Table 4)","semantic filtering alone (Sem (ORG)) improved overall translation error by 18% over ICP (ORG) (Sec. 4.3)","Z drift (Delta Z) of the BIM-based localization stayed within 0.084 m on the four tested sequences, against up to -0.968 m (LOAM), -2.675 m (DLO) and -1.547 m (Open3D SLAM) (Table 6)",[1644,1645,1646,1647,1648,1649,1650],"assumes static built environment; BIM updates needed for dynamic construction sites (Sec. 4.5)","as-designed vs as-built deviations can cause failure (Sec. 4.5)","depends on previous pose; no global localization; floor and initial pose set manually at the first frame (Sec. 4.5)","reference trajectory from Cartographer SLAM run in 2D (x, y, yaw) with tuned parameters, not an external survey (Sec. 4.3)","authors state that the BIM-based localization does not show better 2D pose accuracy than the LiDAR-only LOAM, DLO and Open3D SLAM on the four tested sequences; Table 6 is mixed: lowest translation RMSE on Seq. 3-3 but highest yaw RMSE on Seq. 2-3, 4-2 and 5-2 (Table 6, Sec. 4.4)","axis-aligned Dynamo bounding boxes cause mixed or wrongly labelled map points (Fig. 8, Sec. 4.5)","long, narrow corridors with only walls and floors degrade tracking and caused a trajectory discontinuity on the 4th storey (Sec. 4.5)",{"id":1652,"shortName":1653,"title":1654,"year":286,"fulltextStatus":10,"publicationStatus":11,"siteTypes":1655,"taskLevel":14,"tasks":1656,"platforms":1657,"sensorTags":1658,"reference":27,"referenceNote":110,"engineeringTask":110,"taskRequirement":110,"requirementSource":110,"siteCount":110,"independentValidation":110,"geometricQuality":110,"taskOutcome":110,"applicabilityClaim":110,"taskLimitations":110,"sourceLocator":110,"sensorsRaw":1659,"platformRaw":1662,"keyIdeaZh":1665,"constructionRelevance":1666,"strengths":1667,"limitations":1672},"yu2025_3dgs_lidar_heritage","3DGS vs LiDAR workflow (Bouwpub)","From comparison to integration: A workflow evaluation of 3D Gaussian splatting and LiDAR point cloud for modern architectural heritage",[102],[326],[190],[451,22],[1660,1661],"GeoSLAM HORIZON RT mobile SLAM scanner with integrated camera for panoramic images (LiDAR workflow; footnote 12 links the FARO GeoSLAM ZEB Horizon RT page)","iPhone 12 Pro smartphone camera, 124 images at 3024 x 4032 px (3DGS workflow)",[1663,1664],"mobile scanner (GeoSLAM HORIZON RT), carrying mode not reported","smartphone multi-view photography (iPhone 12 Pro)","本研究以位於臺夫特理工大學國定古蹟建築群內的現代建築遺產 Bouwpub 為案例，一方面以 iPhone 12 Pro 拍攝 124 張影像建立 3DGS（比較 Inria、Polycam 與 Postshot 三種流程後選用 Polycam），另一方面以 GeoSLAM ZEB Horizon RT 行動式 SLAM 掃描器取得點雲，從資料擷取與保存、視覺化、語意分割與 VR 傳播四個面向比較。量化指標只有影像清晰度（拉普拉斯變異數）、以獨立 HBIM 為參考的分割精確率與召回率，以及 Unity VR 的載入時間、幀率、記憶體、GPU 負載與延遲，兩者的幾何誤差並未量測。作者據此將 3DGS 定位為建立在 LiDAR 幾何之上的視覺化與傳播層，並提出 Blender 多視角渲染與 LOD3DGS 兩種整合流程。","於既有建物（現代建築遺產）以商用行動式 SLAM 掃描器與手機影像實測比較，支持「高斯表示作為量測點雲之上的視覺化層，而非取代」的論點；但研究未量化任一輸出的幾何誤差，SLAM 掃描的軌跡、閉環與精度也都未報告，因此不能作為 SLAM 點雲或 3DGS 幾何精度的證據，只能支持視覺化與 VR 效能面向的比較。",[1668,1669,1670,1671],"Polycam 3DGS Laplacian variance 2021.88 versus 1181.53 for the GeoSLAM point cloud (HBIM 3493.74) (Sec. 4.2)","In Unity VR, 3DGS loads in 1 s versus 40 s, runs at 60-90 FPS versus 15-60, uses 13.6 MB versus 562 MB memory, 12-25% versus 35-55% GPU overhead, about 10 ms versus about 35 ms latency (Table 3)","LiDAR point cloud with SAM gives higher segmentation recall against HBIM than 3DGS with GMM (Sec. 4.3, Fig. 10)","Smartphone-based 3DGS lowers acquisition cost and technical barrier (Sec. 4.1)",[1673,1674,1675,1676,1677],"3DGS showed reduced geometric precision in complex or occluded areas and lower segmentation recall than LiDAR (Sec. 4.3, Sec. 6)","3DGS depends on well-calibrated, high-resolution images with consistent lighting and full coverage; exposure variation and occlusion may cause local errors (Sec. 5.3)","High GPU demand of converting dense LiDAR clouds to Gaussians forced workaround pipelines with resolution trade-offs (Sec. 5.3)","3DGS lacks standard interoperability with HBIM, GIS and semantic heritage datasets (Sec. 5.3)","LiDAR point clouds in VR showed fragmentation, transparency artifacts and slower response (Sec. 4.4)",{"id":1679,"shortName":1680,"title":1681,"year":774,"fulltextStatus":10,"publicationStatus":11,"siteTypes":1682,"taskLevel":14,"tasks":1683,"platforms":1684,"sensorTags":1685,"reference":241,"referenceNote":1686,"engineeringTask":1687,"taskRequirement":27,"requirementSource":27,"siteCount":1688,"independentValidation":1689,"geometricQuality":1690,"taskOutcome":1691,"applicabilityClaim":27,"taskLimitations":1692,"sourceLocator":1693,"sensorsRaw":1694,"platformRaw":1697,"keyIdeaZh":1698,"constructionRelevance":1699,"strengths":1700,"limitations":1706},"zhang2024globalbimreg","Global BIM-point registration and association","Global BIM-point cloud registration and association for construction progress monitoring",[1389,60],[62,619],[398],[21],"實地的參考轉換，是以工程師量測的每層 3 至 4 個結構特徵點為同名點，在 CloudCompare 人工粗配準後再做點對網格精配準所得，不是直接量測的點位誤差（Sec. 4.2.1、4.2.2；Table 4）。","BIM-point registration and element existence for progress monitoring","5 benchmark models + 1 active construction site (7 floors)","simulation: ISPRS benchmark ground-truth alignment; real site: 3-4 engineer-measured GCP landmarks per floor","rotation\u002Ftranslation errors vs benchmark GT (Tables 1-3)","BIM-point association (qualitative in read part)","registration accuracy only; association assessed qualitatively; GCP count small (3-4 per floor)","Sec. 3-4.1; Tables 1-3",[1695,1696],"real site: handheld sensor suite with Ouster OS0-128 LiDAR (clouds built with FAST-LIO2)","simulation: ISPRS indoor modelling benchmark clouds from stationary, handheld and backpack scanners",[405],"作者把 BIM 構件以構造實體幾何（CSG）拆解並以解析距離場表示，避免取樣造成資訊損失。粗配準以平面基元對 BIM 面在重力軸對齊下搜尋對應，並以剛體動力學模擬驗證幾何一致性；精配準則交替更新位姿與逐點對應權重，並以鄰近性、法向與構件存在與否截斷權重，使臨時材料與未施作構件不誤導配準。模擬採 ISPRS 室內建模基準（含手持與背包掃描）。","實測於香港城市大學賽馬會一健康大樓施工工地 06 至 12 樓（CR Construction 協助，每層約 80 m × 50 m），以手持感測套件上的 Ouster OS0-128 蒐集資料，再以 FAST-LIO2 逐層重建點雲，BIM 為 LOD 300。評估用的參考值不是直接量測的點位誤差，而是以工程師現地量測的 3 至 4 個結構特徵點（GCP）作為同名點，在 CloudCompare v2.13 alpha 人工粗配準後再做點對網格精配準所得的轉換；屬施工中工地且使用 SLAM 點雲。",[1701,1702,1703,1704,1705],"coarse registration median TE 0.053 m and RE 0.272 deg over the successful cases of 250 perturbed samples (Table 1)","fine registration median TE 0.0246 m vs 0.0422 m for point-to-point ICP (Table 2)","100% coarse success for alpha_r >= 0.5 deg and alpha_t >= 0.4 m, unlike all ten baselines (Sec. 4.1.2, Fig. 10)","on seven floors of an active site, fine-registration errors of 0.009-0.203 deg and 0.032-0.107 m relative to the CloudCompare reference, about 0.05 deg and 0.058 m on average (Table 4)","per-point association separates rebar, glass, barriers, temporary materials, pipes and boxes from built structures and flags unbuilt decoration walls (Sec. 4.2.2, Fig. 15)",[1707,1708,1709,1710,1711,1712,1713,1714],"drift errors in the FAST-LIO2 reconstruction caused some wall points to receive low association levels (floors 07 and 12); authors suggest a high-precision laser scanner (Sec. 4.2.2)","geometry-only association: barriers close to walls can be associated with the wall even with normal verification (Sec. 4.2.2)","only regular human-made structures are modelled; MEP objects are not included (Sec. 4.2.2)","offline only; point clouds with few planar segments cannot be registered (Sec. 4.2.2)","coarse registration slower than PLADE and RANSAC in simulation (64.2 s vs 8.85 s and 27.27 s) (Sec. 4.1.2)","coarse precision drops for sparse or partial clouds from early construction stages (Table 3)","(inference) the real-site reference is itself a manual CloudCompare registration seeded by 3-4 GCPs, so centimetre-level differences are not independently verified","(inference) assumes gravity axis known and planar primitives dominant",{"id":1716,"shortName":1717,"title":1718,"year":286,"fulltextStatus":10,"publicationStatus":11,"siteTypes":1719,"taskLevel":14,"tasks":1720,"platforms":1721,"sensorTags":1722,"reference":66,"referenceNote":1723,"engineeringTask":1724,"taskRequirement":1725,"requirementSource":27,"siteCount":1726,"independentValidation":1727,"geometricQuality":1728,"taskOutcome":27,"applicabilityClaim":1729,"taskLimitations":1730,"sourceLocator":1503,"sensorsRaw":1731,"platformRaw":1735,"keyIdeaZh":1737,"constructionRelevance":1738,"strengths":1739,"limitations":1745},"stroner2025minetunnel","4 SLAM vs 2 static scanners in 120 m tunnel","Scanning the underground: Comparison of the accuracies of SLAM and static laser scanners in a mine tunnel",[421],[423,424],[398],[451,109],"參考為 Leica MS60 全測站控制網與 Leica P40 參考點雲（以全測站量測 24 個檢核點驗證，其中 1 點經目視檢查剔除，RMSD 1.4 mm）；受比較者含四款商用 SLAM 掃描儀與兩款靜態掃描儀（Sec. 2-4）。","underground\u002Ftunnel surveying and mapping","not_reported (no project tolerance); cites sub-cm to several-cm precision needs generally","1 (URC Josef tunnel, ~120 m)","Leica MS60 network (SD 1.2 mm XY, 0.6 mm Z) + Leica P40 reference (RMSD 1.4 mm vs 24 check points)","RMSD_GCP, RMSD_PR, RMSD_ICP, central-profile RMSDs, systematic shifts, noise, scale (Table 3)","authors state new-generation SLAM scanners are highly suitable for enclosed spaces","single site; distributor-operated SLAM runs; mining not construction",[1732,1733,1734],"commercial SLAM scanners: GeoSLAM ZEB Horizon RT (Velodyne VLP-16, 16 channels), NavVis VLX 2 (two 16-channel sensors, 4 cameras), Emesent Hovermap ST-X (32 channels), FARO Orbis (32 channels, 360-degree camera)","static TLS: Trimble X7, FARO Focus Premium 70","reference: Leica ScanStation P40 + Leica Nova MS60 total station; four 0.14 m spherical GCP targets; Leica GZT21 black-and-white targets",[405,1736],"wearable","作者在捷克 URC Josef 地下研究中心約 120 m 的不規則岩壁坑道，以 Leica MS60 全測站建立控制網並以 Leica P40 建立參考點雲（以全測站量測 24 個檢核點驗證，其中 1 點經目視檢查剔除，RMSD 1.4 mm），比較四款商用 SLAM 掃描儀與兩款靜態掃描儀。所有點雲僅以兩端四顆球靶做剛體轉換，再計算整體 RMSD、ICP 後 RMSD、中段 3.5 m 區段的系統偏移、雜訊與平滑後誤差。新世代 SLAM 掃描儀整體偏差與靜態掃描同級甚至更好（12 至 24 mm 對 22 至 27 mm），但雜訊較高、需平滑，誤差在兩端控制點中間最大，呈「香蕉形」變形；SLAM 掃描約 2 分鐘，靜態掃描約 2 小時。","礦業坑道（地下工程類環境），以高規格測量控制作獨立參考；可作隧道與地下空間 SLAM 掃描精度的代表證據，但非施工中隧道，作者也提醒牆面平滑的建築物結果可能不同（Sec. 4）。",[1740,1741,1742,1743,1744],"RMSD after GCP transformation 12-24 mm for SLAM (excluding the erroneous Geoslam run) vs 22-27 mm for static scanners (Abstract; Table 3)","Hovermap two-way: RMSD_PR 12 mm, RMSD_CP-XYZ 7 mm, MaxdXYZ 17 mm (Table 3)","SLAM gives more complete coverage of rugged hollows than stop-and-go static scanning (Sec. 3.3, Fig. 5)","SLAM capture about 2 min for the 120 m trail vs about 2 h for each static scanner (Sec. 2.4, Sec. 4)","after simple MLS smoothing, local accuracy of SLAM clouds approaches that of static scanners (Sec. 3.4, Table 3)",[1746,1747,1748,1749,1750,1751,1752],"SLAM clouds noisier; smoothing improves accuracy but may remove sharp edges and cannot recover features at or below the noise length scale (Sec. 4, Sec. 5)","one of three Geoslam runs obviously erroneous (~149 mm transverse deviation) (Sec. 3.4)","largest deviations mid-way between end GCPs (banana-like deformation) (Sec. 3.4, Fig. 6)","SLAM data collected by distributors' technicians; single 120 m test site (Sec. 2.2)","SLAM scanners give no in-method check; authors recommend at least two passes or extra checkpoints (Sec. 4)","findings limited to rugged mine walls; smooth-walled buildings may behave differently; three runs per SLAM scanner do not allow rigorous statistics (Sec. 4)","NavVis clouds are smoothed by the vendor software, flattering its noise profile (Sec. 3.3, Fig. 4)",[1754,1832,1867,1947],{"category":1755,"studies":1756},"tls_scanner",[1757,1762,1769,1774,1779,1784,1788,1792,1796,1801,1808,1812,1818,1823,1827],{"id":283,"items":1758},[1759],{"model":1760,"locator":1761},"Leica BLK360","Sec. 4.1, ref. [81]",{"id":442,"items":1763},[1764,1767],{"model":1765,"locator":1766},"FARO Focus 3D X 330","Sec. 3.2, Sec. 3.3",{"model":1768,"locator":1766},"FARO Focus S70",{"id":738,"items":1770},[1771],{"model":1772,"locator":1773},"Leica RTC360","Sec. 3.1.2",{"id":858,"items":1775},[1776],{"model":1777,"locator":1778},"commercial terrestrial laser scanner (model not reported)","Sec. 5; Table 4",{"id":957,"items":1780},[1781],{"model":1782,"locator":1783},"Leica ScanStation P40","The used devices and software; Measurement and processing of the reference dataset",{"id":988,"items":1785},[1786],{"model":1782,"locator":1787},"The used devices and software; Measurement and processing of the reference dataset; Results",{"id":1091,"items":1789},[1790],{"model":1772,"locator":1791},"Fig. 1",{"id":1235,"items":1793},[1794],{"model":1765,"locator":1795},"Sec. 3.2, Table 2",{"id":1267,"items":1797},[1798],{"model":1799,"locator":1800},"FARO Focus 3D X120","Sec. Courtyard (C), Fortified village (E); Tables 11, 15",{"id":1326,"items":1802},[1803,1806],{"model":1804,"locator":1805},"RIEGL VZ-400i","Hardware Setup; Metrics for Evaluation",{"model":1807,"locator":1805},"RIEGL VZ-600i",{"id":1716,"items":1809},[1810],{"model":1782,"locator":1811},"Sec. 2.2; Sec. 2.3; Sec. 3.2; Table 1",{"id":1385,"items":1813},[1814,1816],{"model":1772,"locator":1815},"Table 2; Sec. 4.6",{"model":1768,"locator":1817},"Table 2; Sec. 4.5",{"id":1454,"items":1819},[1820],{"model":1821,"locator":1822},"FARO Focus 3D","Sec. 3.2",{"id":1491,"items":1824},[1825],{"model":1804,"locator":1826},"Sec. 2.4; Table 1",{"id":1580,"items":1828},[1829],{"model":1830,"locator":1831},"not_reported (TLS point clouds supplied with ConSLAM)","Sec. 1, 5.1, 6",{"category":1833,"studies":1834},"total_station",[1835,1840,1845,1849,1853,1858,1862],{"id":55,"items":1836},[1837],{"model":1838,"locator":1839},"not_reported (total station; model not named)","Sec. IV-A, IV-C",{"id":771,"items":1841},[1842],{"model":1843,"locator":1844},"Trimble SX10","Sec. 3.2; Sec. 3.3",{"id":957,"items":1846},[1847],{"model":1848,"locator":1783},"Leica Nova MS60",{"id":988,"items":1850},[1851],{"model":1852,"locator":1783},"Leica Nova TS60",{"id":1326,"items":1854},[1855],{"model":1856,"locator":1857},"Leica TS30","Hardware Setup; Data Collection",{"id":1716,"items":1859},[1860],{"model":1848,"locator":1861},"Sec. 2.2; Sec. 2.3; Sec. 3.2",{"id":1454,"items":1863},[1864],{"model":1865,"locator":1866},"Leica Viva TS15","Sec. 3.3",{"category":1868,"studies":1869},"other",[1870,1875,1881,1886,1891,1896,1901,1906,1911,1916,1924,1929,1937,1942],{"id":97,"items":1871},[1872],{"model":1873,"locator":1874},"2 m long straightedge and precision steel rule on a chalk-line 2 m grid","Sec. 7.2",{"id":442,"items":1876},[1877,1879],{"model":1878,"locator":1866},"spherical targets",{"model":1880,"locator":1866},"target board with removable targets and a flat reference surface",{"id":518,"items":1882},[1883],{"model":1884,"locator":1885},"laser rangefinder (model not reported)","Sec. 4.2, Table 2",{"id":548,"items":1887},[1888],{"model":1889,"locator":1890},"ruler","Sec. 4.4",{"id":578,"items":1892},[1893],{"model":1894,"locator":1895},"laboratory targets: 24 MDF colour boards (300 x 300 mm, smooth and 40-grit rough) and cast concrete targets (dolomite and granite mixes, three roughness levels)","Sec. 3.1, 3.2, 4.2",{"id":613,"items":1897},[1898],{"model":1899,"locator":1900},"physical cylinder test object with pre-marked scale for the movement-rate test","Sec. 4.2.2, Figs. 6, 7",{"id":771,"items":1902},[1903],{"model":1904,"locator":1905},"hand-held thermometer","Sec. 3.2; Sec. 4.1",{"id":957,"items":1907},[1908],{"model":1909,"locator":1910},"spherical targets (diameter 0.145 m) and black-and-white targets","Materials and methods; Measurement and processing of the reference dataset",{"id":988,"items":1912},[1913],{"model":1914,"locator":1915},"Leica GZT21 black-and-white targets and Leica GMP111 mini prism","Measurement and processing of the reference dataset",{"id":1267,"items":1917},[1918,1921],{"model":1919,"locator":1920},"UAV photogrammetry DSM (nadir and oblique images; platform and camera not reported)","Tables 10, 14",{"model":1922,"locator":1923},"close-range photogrammetry (SfM) model of the tower (camera not reported)","Table 3; Sec. Tower (A)",{"id":1326,"items":1925},[1926],{"model":1927,"locator":1928},"360-degree prism (model not_reported)","Hardware Setup; Results",{"id":1716,"items":1930},[1931,1934],{"model":1932,"locator":1933},"Leica GZT21 4.5 inch black-and-white targets","Sec. 2.2",{"model":1935,"locator":1936},"spherical targets, diameter 0.14 m (4 GCPs)","Sec. 2; Sec. 2.5",{"id":1423,"items":1938},[1939],{"model":1940,"locator":1941},"flatness defect test-bed boards (3 boards)","Framework, Flatness Defect Detection Test Bed; Fig. 3",{"id":1679,"items":1943},[1944],{"model":1945,"locator":1946},"not_reported (engineers' on-site measurement of 3-4 structural landmarks as GCPs)","Sec. 4.2.1",{"category":22,"studies":1948},[1949],{"id":578,"items":1950},[1951],{"model":1952,"locator":1953},"RGB camera for visual validation images (model not reported)","Sec. 5.2, Fig. 18",[1955,1975,1994,2011,2030,2045,2062,2079,2093,2111,2128,2144,2160,2176,2192,2209,2223,2239,2255,2270,2286],{"id":1956,"shortName":1957,"title":1958,"year":1959,"fulltextStatus":10,"publicationStatus":11,"construction":24,"dataType":1960,"refTypes":1961,"sensorTags":1963,"licence":1964,"environment":1965,"stage":1966,"sensors":1967,"refTrajectory":1968,"refAccuracy":1969,"refMap":1970,"access":1971,"limitation":1972,"loc":1973,"cite":1974},"burri2016euroc","EuRoC MAV","The EuRoC micro aerial vehicle datasets",2016,"sequence",[1833,1962],"motion_capture",[22,23],"not_found","ETH 機械廠房（第一批 5 個序列）與約 8 m × 8.4 m × 4 m 的 Vicon 動作捕捉室（第二批 6 個序列），共 11 個序列。","非營建場域（工業廠房與實驗室）。","AscTec Firefly 六旋翼無人機搭載兩部全域快門單色相機（20 Hz）與 ADIS16448 IMU（200 Hz），硬體同步（Sec. 2；Table 1）。","廠房以 Leica Nova MS50 追蹤機上稜鏡提供 3D 位置（約 20 Hz）；動作捕捉室以 Vicon 提供 6D 位姿（100 Hz）。真值與感測資料以離線批次最大概似估計器做時空對齊，同時估計稜鏡或標記偏移與隨時間變化的時間差（Sec. 2、5.2）。","MS50 位置真值約 1 mm（Table 1）；作者指出劇烈運動時追蹤儀的精度可能變差，原廠數值對這些時段可能偏樂觀（Sec. 6）。","以 MS50 從七個站位掃描融合的 Vicon 室點雲，可用於重建評估（Sec. 3.2；Fig. 4）。","資料集網頁未載明（未查證）。","劇烈運動可能降低雷射追蹤的精度；真值與感測器分屬不同記錄系統，且沒有 Vicon 裝置時間戳，時間同步有限，作者以把時間差當成狀態估計來緩解（Sec. 5.2、6）。兩部相機各自自動曝光，影像亮度不一（Sec. 6）。","Sec. 2-6；Table 1；Fig. 4",[],{"id":1976,"shortName":1977,"title":1978,"year":286,"fulltextStatus":10,"publicationStatus":11,"construction":1979,"dataType":1960,"refTypes":1980,"sensorTags":1983,"licence":1964,"environment":1984,"stage":1985,"sensors":1986,"refTrajectory":1987,"refAccuracy":1988,"refMap":1989,"access":1990,"limitation":1991,"loc":1992,"cite":1993},"chen2025geode","GEODE","Heterogeneous LiDAR dataset for benchmarking robust localization in diverse degenerate scenarios","unreported",[1962,1981,1833,1982],"tls_registration","gnss_ins",[21,22,23],"64 條軌跡、逾 64 km，涵蓋七種退化情境：平地、樓梯、地鐵隧道（礦山法與盾構法）、越野、內河航道、都市隧道與橋梁。","地鐵隧道以工法描述；是否在施工中或已營運未報告，不能視為施工工地資料。","三組設備分別搭載 Velodyne VLP-16、Ouster OS1-64 與 Livox Avia，共用 HikRobot 立體相機（10 Hz）與 Xsens MTi-30 IMU，另有光達內建 IMU；以 GNSS 時間訊號與 PPS 做 FPGA 硬體同步，量得 IMU 與相機的同步誤差約 4 ms（Sec. 3.1-3.2）。","依情境不同：平地用 Vicon；樓梯以 PALoc 在 Leica RTC360 地圖中定位；地鐵隧道以 Leica MS60 追蹤稜鏡（只有 3 自由度位置）；越野、航道、都市隧道與橋梁用 GNSS-RTK 與 INS。","列出的是儀器規格：Vicon 1 mm、MS60 1 mm、RTK 1 cm、RTC360 1 mm（Table 2）。樓梯段的真值是 PALoc 在 RTC360 地圖中的定位結果（inlier RMSE 0.07 與 0.08 m），不是外部追蹤儀器的量測，而且 PALoc 無法為 Livox Avia 產生準確真值（Sec. 4.4.2）。","部分室內序列有 RTC360 地圖。","GitHub；未找到授權聲明。","IMU 與相機的時間戳由主機指定，未完全同步，作者建議使用者線上估計時間偏移（Sec. 6.3）；RTK-INS 真值在衛星少時劣化，都市隧道只保留固定解並人工剔除大誤差點（Sec. 4.4.2）；各設備只校正一次，但蒐集期間約一週（Sec. 6.2）。期刊版評估的八種 LIO、LIVO 與立體視覺 VIO 方法，在所有盾構隧道序列都失效（Sec. 5.2；Table 5）。儀器規格不等於真值軌跡的精度（推論）。","Sec. 1、3.1-3.2、4.1.3、4.4.2、5.2、6；Table 2-3、5",[],{"id":1995,"shortName":1996,"title":1997,"year":1024,"fulltextStatus":10,"publicationStatus":11,"construction":24,"dataType":1960,"refTypes":1998,"sensorTags":2000,"licence":1964,"environment":2001,"stage":2002,"sensors":2003,"refTrajectory":2004,"refAccuracy":2005,"refMap":2006,"access":2007,"limitation":2008,"loc":2009,"cite":2010},"dai2017scannet","ScanNet","ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes",[1999],"slam_derived",[292,23],"1,513 個室內場景、250 萬個視角。","非營建場域。","裝在 iPad Air2 上的 Structure 深度感測器：深度 640 × 480、彩色 1296 × 968，30 Hz，硬體同步；另記錄 iPad 內建 IMU 資料，其重力向量用於對齊網格方向（Sec. 3.1、3.2）。","BundleFusion 位姿，即一套 RGB-D SLAM 的輸出。","未報告。","以 VoxelHashing 做 TSDF 融合（4 mm 體素）並以 marching cubes 產生網格；屬 SLAM 衍生網格，不是獨立測量。","未查證。","過短、重建殘差高或對齊影格比例低的掃描會被自動剔除，明顯錯位的重建再由人工剔除（Sec. 3.2）；因此釋出的場景偏向 BundleFusion 成功的案例（推論）。參考位姿與網格都是 SLAM 輸出，以此計算的幾何分數不是獨立的精度量測（推論）。","Sec. 3.1-3.2",[],{"id":2012,"shortName":2013,"title":2014,"year":286,"fulltextStatus":10,"publicationStatus":11,"construction":2015,"dataType":1960,"refTypes":2016,"sensorTags":2019,"licence":1964,"environment":2020,"stage":2021,"sensors":2022,"refTrajectory":2023,"refAccuracy":2024,"refMap":2025,"access":2026,"limitation":2027,"loc":2028,"cite":2029},"feng2025_construction_lidar_eval","Feng et al. 2025 construction-site LiDAR SLAM evaluation","Evaluation of LiDAR SLAM algorithms for construction robots in large public construction sites","all_active",[2017,2018],"not_described","synthetic",[21,23,22],"西安某醫院門診大樓標準層，實測軌跡約 1,004 m；另依施工圖建立 Gazebo 模擬工地，軌跡約 1,293 m 且沒有迴圈重訪。","主體結構已封頂，機電安裝與室內裝修並行。","自建輪式地面施工機器人搭載 RS-Helios-16P 3D 光達（10 Hz，論文未寫製造商）與九軸 IMU（500 Hz）；另錄有 30 Hz 相機影像，受評方法未使用。","模擬：Gazebo 外掛提供真實位姿。實測工地：全文未說明參考軌跡的來源。","實測參考未說明，因此無從評估。","無量測參考；地圖品質以高程著色的三視圖目視評估，未報告數值。","依請求提供（Data availability）；授權未查證。","所有演算法都使用官方預設參數；實測 APE 的參考來源未說明，兩組資料計算 APE 前的軌跡對齊方式也未說明，其公尺級數值不能解讀為絕對精度。只有單一工地與單一感測器配置（推論）。","Sec. 3-5；Table 4",[],{"id":2031,"shortName":2032,"title":2033,"year":186,"fulltextStatus":10,"publicationStatus":11,"construction":24,"dataType":1960,"refTypes":2034,"sensorTags":2035,"licence":1964,"environment":2036,"stage":2037,"sensors":2038,"refTrajectory":2039,"refAccuracy":2040,"refMap":2041,"access":2007,"limitation":2042,"loc":2043,"cite":2044},"geiger2012kitti","KITTI","Are we ready for autonomous driving? The KITTI vision benchmark suite",[1982],[21,22,23,995],"中型城市、鄉村與高速公路的戶外駕駛；里程計基準含 22 個雙目序列、39.2 km。","非營建場域（道路駕駛）。","兩組立體相機（灰階與彩色，10 Hz）、Velodyne HDL-64E、OXTS RT 3003 GPS\u002FIMU（RTK）。","GPS\u002FIMU 定位單元的輸出。","開闊天空下該單元的定位誤差低於 5 cm（Sec. 2.1）；無逐序列不確定度。","里程計基準沒有參考地圖。","道路幾何與車輛運動和室內工地差異很大（推論）；本站只引用其子序列漂移指標的定義。","Sec. 2.1-2.5",[],{"id":2046,"shortName":2047,"title":2048,"year":100,"fulltextStatus":10,"publicationStatus":11,"construction":24,"dataType":2018,"refTypes":2049,"sensorTags":2050,"licence":2051,"environment":2052,"stage":2053,"sensors":2054,"refTrajectory":2055,"refAccuracy":2056,"refMap":2057,"access":2058,"limitation":2059,"loc":2060,"cite":2061},"handa2014iclnuim","ICL-NUIM","A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM",[2018],[292],"data_licence","以 POV-Ray 光線追蹤產生的合成客廳與辦公室場景。","非營建場域（合成住宅場景）。","合成 RGB-D，加入類 Kinect 的深度雜訊與 RGB 雜訊模型。","取自 Kintinuous 在真實客廳資料上估計的軌跡，旋轉並等比縮放後放入虛擬場景，作為真值。","不適用（合成資料）。","只有客廳場景附多邊形模型，可量化表面重建誤差；辦公室場景只適合評估軌跡。","資料為 CC BY 3.0（論文 Sec. I 與資料集網頁）。","只有合成資料，未模擬動態模糊與滾動快門；軌跡為房間尺度（2.05 至 11.32 m）。表面誤差在人工粗對齊加 ICP 之後計算，不含絕對錯位，也不量測完整度（推論）。","Sec. III、VI-B、VII；Table I",[],{"id":2063,"shortName":2064,"title":2065,"year":419,"fulltextStatus":10,"publicationStatus":11,"construction":2066,"dataType":1960,"refTypes":2067,"sensorTags":2068,"licence":2051,"environment":2069,"stage":2070,"sensors":2071,"refTrajectory":2072,"refAccuracy":2073,"refMap":2074,"access":2075,"limitation":2076,"loc":2077,"cite":2078},"helmberger2022hilti","Hilti SLAM Challenge 2021","The Hilti SLAM Challenge Dataset","partial_active",[1833,1962],[21,22,23],"八個地點：Basement、Campus 中庭、Construction Site（多為戶外、部分有遮蔽，約 40 × 80 m，表面未完成）、IC Office、Lab、Office Mitte（完工辦公建物）、Parking 停車場與 RPG 追蹤區。","逐地點描述中只有 Construction Site 一處明確為營建工地，且未說明施工階段；其他地點是否施工中未說明，不應標為工地。","Alphasense 五相機模組（10 Hz）、Ouster OS0-64、Livox MID70，以及 ADIS16445、BMI085、ICM-20948 三具 IMU；以 PTP 同步，觀測到的時間對齊優於 1 ms。","稀疏真值：Hilti PLT 300 自動全測站以停走方式追蹤稜鏡；兩個室內房間改用光學動作捕捉。","靜態稜鏡測距 3 mm；動作捕捉位置優於 1 mm（200 Hz）。","無。","官方資料頁（hilti-challenge.com\u002Fdataset-2021.html）載明 CC BY-NC-SA 3.0。","動作捕捉與記錄器時鐘未硬體同步（相差 1 至 3 ms）；高負載時部分幀遺失。真值只公開一半序列，另一半保留供挑戰賽計分（Sec. V）。稀疏的位置真值只在少數時刻評估軌跡，不評估點雲幾何（推論）。","Sec. III-VI",[],{"id":2080,"shortName":2081,"title":2082,"year":419,"fulltextStatus":10,"publicationStatus":11,"construction":24,"dataType":1960,"refTypes":2083,"sensorTags":2084,"licence":1964,"environment":2085,"stage":2002,"sensors":2086,"refTrajectory":2087,"refAccuracy":2088,"refMap":2089,"access":2007,"limitation":2090,"loc":2091,"cite":2092},"jiao2022fusionportable","FusionPortable","FusionPortable: A Multi-Sensor Campus-Scene Dataset for Evaluation of Localization and Mapping Accuracy on Diverse Platforms",[1962,1981,1982],[21,22,23,995],"香港科技大學校園：實驗室、花園、餐廳、走廊、手扶梯與戶外道路，共 17 個序列。","Ouster OS1-128 光達（10 Hz）、雙目影像相機（20 Hz）、雙目事件相機、IMU（200 Hz）與 GPS（10 Hz）；以 GPS PPS 觸發的 FPGA 硬體同步，但兩具事件相機的影像擷取無法同步，約有 10 至 20 ms 偏差（Sec. III-A3、III-B1）。","動作捕捉室用 OptiTrack（120 Hz，毫米級）；中尺度區域以 NDT 在 BLK360 先驗地圖中定位；戶外以 RTK GPS 融合光達慣性資料（LIO-SAM）。","依來源而異；論文沒有量化地圖內 NDT 定位與 RTK-GPS 加 LIO-SAM 真值的精度（推論）。","數個室內場景的 Leica BLK360 彩色稠密地圖，毫米級精度；戶外校園道路序列沒有真值地圖。","真值品質隨來源（動作捕捉、地圖內 NDT 定位、RTK-GPS 加 LIO-SAM）而不同，且未統一量化（推論）。戶外校園道路序列的真值由 RTK GPS 融合 LIO-SAM 產生，而 LIO-SAM 也在該序列受評，因此其分數不獨立（推論；Table III）。GPS 可能因建物遮蔽而不穩定（Sec. III-A5）。","Sec. III、IV-B、V；Table III",[],{"id":2094,"shortName":2095,"title":2096,"year":1024,"fulltextStatus":10,"publicationStatus":11,"construction":24,"dataType":2097,"refTypes":2098,"sensorTags":2100,"licence":1964,"environment":2101,"stage":2102,"sensors":2103,"refTrajectory":2104,"refAccuracy":2105,"refMap":2106,"access":2107,"limitation":2108,"loc":2109,"cite":2110},"khoshelham2017isprsindoor","ISPRS Indoor Modelling Benchmark","THE ISPRS BENCHMARK ON INDOOR MODELLING","static",[2099],"manual_model",[21,109],"五組室內點雲：TUB1、TUB2（兩層樓）、Fire Brigade（Delft 消防隊辦公室）、UVigo 與 UoM（大學建築）；各組的雜物程度從低到高不等。","已完工建物，非施工中工地。","推車式 Viametris iMS3D、手持 ZEB-REVO 與 ZEB1、UVigo 背包原型、Leica C10 地面光達。","未提供軌跡真值。","Table 2 的感測器精度是規格值。","專家在 CloudCompare 前處理後，於 Revit 以水平與垂直切面人工建立的參考模型，只涵蓋點雲中可見的構件。","論文未載明資料授權；論文所給的下載網址在 2026-09-25 回傳 HTTP 404。","參考模型由同一批點雲人工建立，不是獨立的幾何量測（推論）；量化評估只含牆、樓板、天花板與門窗，不含樓梯與柱。","Sec. 2-5",[],{"id":2112,"shortName":2113,"title":2114,"year":1024,"fulltextStatus":10,"publicationStatus":11,"construction":24,"dataType":2115,"refTypes":2116,"sensorTags":2118,"licence":1964,"environment":2119,"stage":2002,"sensors":2120,"refTrajectory":2121,"refAccuracy":2122,"refMap":2123,"access":2124,"limitation":2125,"loc":2126,"cite":2127},"knapitsch2017tnt","Tanks and Temples","Tanks and temples: benchmarking large-scale scene reconstruction","video",[2117],"tls_reference",[22],"雕塑、大型車輛、房屋尺度的建物（中級組），以及大型室內外場景（進階組）。","高解析度影片（全域快門與滾動快門相機）。","不適用（重建基準）。","不適用。","FARO Focus 3D X330 HDR 掃描；校正時測距雜訊在 10.2 m 為 0.1 mm、22.7 m 為 0.3 mm；以 τ\u002F2 的體素格網重取樣。","未查證（網站可連線）。","門檻 τ 依場景尺度與取樣密度而不同。評估前先做含尺度的相似轉換對齊，分數只反映形狀一致性，不含尺度與絕對位置誤差（推論）。","Sec. 4.1；評估章節",[],{"id":2129,"shortName":2130,"title":2131,"year":286,"fulltextStatus":10,"publicationStatus":2132,"construction":2015,"dataType":1960,"refTypes":2133,"sensorTags":2134,"licence":1964,"environment":2135,"stage":2136,"sensors":2137,"refTrajectory":2138,"refAccuracy":2005,"refMap":2139,"access":2140,"limitation":2141,"loc":2142,"cite":2143},"li2025hcic","HCIC Construction VSLAM dataset","A Real World Visual SLAM Dataset for Indoor Construction Sites","status_uncertain",[1999],[21,292,23],"Bright Dream Robotics（佛山）提供的室內施工現場：未完成的結構、外露管線與散置材料。","序列呈現不同材料階段的表面（混凝土、磚、粉刷、批土）；未對應工程進度表，也未特指裝修階段。","Intel RealSense L515 RGB-D（30 Hz）與 Ouster OS0-128（10 Hz），皆含內建 IMU，裝在人工操作的平台上；使用 ROS 預設同步，光達與影像未時間對齊。","修改版 FAST-LIO2 光達慣性里程計的輸出。","無；光達地圖來自同一個里程計。","GitHub 儲存庫；未找到授權檔。","光達與相機時間戳未完全對齊（Sec. 2.4）；合併多次執行時地圖出現錯位（Sec. 3）。論文沒有報告任何軌跡或地圖誤差數值，視覺前端的比較只以軌跡圖呈現（Sec. 3；Fig. 8a）。參考軌跡不是獨立量測，其漂移未量化，不能用來驗證光達方法（推論）。","Sec. 2-3",[],{"id":2145,"shortName":2146,"title":2147,"year":774,"fulltextStatus":10,"publicationStatus":11,"construction":2066,"dataType":1960,"refTypes":2148,"sensorTags":2150,"licence":2051,"environment":2151,"stage":2152,"sensors":2153,"refTrajectory":2154,"refAccuracy":2155,"refMap":2156,"access":2157,"limitation":2158,"loc":2077,"cite":2159},"nair2024hilti2023","Hilti SLAM Challenge 2023","Hilti SLAM Challenge 2023: Benchmarking Single + Multi-Session SLAM Across Sensor Constellations in Construction",[2149],"gcp_sparse",[21,22,23],"Site 1：多層新建工地（樓地板面積逾 4000 m²）；Site 2：改建中的三層地下停車場（逾 7500 m²，牆面不平行、樓板有坡度）；Site 3：部分作倉庫使用的隧道走廊網。每處的所有序列在 48 小時內蒐集。","Site 1 為新建工地（未說明階段），Site 2 為改建中；Site 3 是既有設施，不屬施工工地。","手持 Phasma（與 2022 年相同硬體）；Trailblazer 700 kg 鑽孔機器人原型搭載 Robosense BPearl 半球光達（雜訊 ±3 cm）、四組 OAK-D 立體相機與 XSens MTi-670 IMU；以 PTP 與觸發訊號同步。","由 TLS 萃取的稀疏地面控制點：手持設備以尖端放置；機器人以光達強度偵測圓形標靶並以 Hough 投票定位。","機器人的控制點偵測器在預先測設的 6 × 6 格網上，相對誤差中位數 2.3 mm、最大 4.7 mm；這項評估只涵蓋控制點之間的相對精度，不含隨距離變化的測距偏差等絕對效應，作者認為控制點位於機器人所在的地面，此效應可忽略（Sec. III-D）。","每處以 Trimble X7 掃描；三處分別有 92%、84%、90% 的已配準掃描在 3 mm 配準不確定度內。","官方資料頁（hilti-challenge.com\u002Fdataset-2023.html）載明 CC BY-NC-SA 3.0，真值檔可依該頁下載。","只有稀疏真值、沒有稠密軌跡；校正精度未經外部驗證。回饋圖可推得控制點座標，作者因此加入防護措施，並以加倍權重的隱藏 Site 3 計分。",[],{"id":2161,"shortName":2162,"title":2163,"year":419,"fulltextStatus":10,"publicationStatus":11,"construction":24,"dataType":1960,"refTypes":2164,"sensorTags":2165,"licence":2051,"environment":2166,"stage":2167,"sensors":2168,"refTrajectory":2169,"refAccuracy":2170,"refMap":2171,"access":2172,"limitation":2173,"loc":2174,"cite":2175},"nguyen2022ntuviral","NTU VIRAL","NTU VIRAL: A visual-inertial-ranging-lidar dataset, from an aerial vehicle viewpoint",[1833],[21,22,23],"南洋理工大學校園：停車場（EEE）、玻璃建物旁廣場（SBS）與禮堂室內（NYA）；原有九條序列，資料集網頁記載 2022-09-26 另增九條。","非營建場域；作者以建物、起重機等檢測情境為動機，但資料都在大學校園錄製。","DJI M600 Pro 六旋翼無人機搭載兩具 16 線 Ouster OS1（水平與垂直）、VectorNav VN100 IMU、硬體觸發的雙目全域快門相機與 UWB 測距。","Leica Nova MS60 追蹤機上稜鏡，只有位置沒有姿態；稜鏡距機體原點約 0.4 m，計算 ATE 前必須換算。","稜鏡並非剛性固定，傾斜時最大位移約 2 cm；真值與機載感測器的時間對齊只界定在 0.1 s 內。","無參考點雲。","資料集網頁載明 CC BY-NC-SA 4.0（非商業學術使用）。","只有位置真值、沒有參考點雲，可評估軌跡但不能評估地圖幾何（推論）；部分序列全測站短暫失鎖、UWB 訊號遺失。","Sec. 2.5、5.3、6-7",[],{"id":2177,"shortName":2178,"title":2179,"year":9,"fulltextStatus":10,"publicationStatus":11,"construction":24,"dataType":1960,"refTypes":2180,"sensorTags":2181,"licence":1964,"environment":2182,"stage":2183,"sensors":2184,"refTrajectory":2185,"refAccuracy":2186,"refMap":2187,"access":2188,"limitation":2189,"loc":2190,"cite":2191},"ramezani2020newercollege","Newer College","The Newer College Dataset: Handheld LiDAR, Inertial and Vision with Ground Truth",[1981],[21,22,23],"牛津 New College 的中庭、中段與公園綠地（有建物與植被的戶外校園），步行約 2.2 km。","非營建場域（歷史學院的既有建築）。","Ouster OS-1 64 線光達（含 IMU）與 Intel RealSense D435i 雙目慣性相機；視覺與慣性資料為軟體同步。","每一光達掃描以 ICP（10 Hz）配準到 TLS 先驗地圖。","約 3 cm（摘要）；此值是裝置靜止 10 秒期間 ICP 位置的標準差（x 2 cm、y 1.6 cm、z 2 cm），移動中的精度沒有量化，作者預期會較低（Sec. V；Fig. 5）。","Leica BLK360 測量級 TLS 架站 47 次建立的地圖，約 2.9 億點；規格為 10 m 處 6 mm、20 m 處 8 mm，作者稱多數點優於 1 cm。","資料網頁未載明（未查證）。","真值軌跡精度（約 3 cm）明顯粗於 TLS 地圖本身；原始資料的視覺與慣性僅軟體同步，裝置未硬體同步，RealSense 與 Ouster 的 IMU 每小時相對漂移約 58 ms（Sec. III）。","Sec. III、V",[],{"id":2193,"shortName":2194,"title":2195,"year":286,"fulltextStatus":10,"publicationStatus":11,"construction":2015,"dataType":2097,"refTypes":2196,"sensorTags":2198,"licence":2051,"environment":2199,"stage":2200,"sensors":2201,"refTrajectory":2202,"refAccuracy":2203,"refMap":2204,"access":2205,"limitation":2206,"loc":2207,"cite":2208},"rauch2025rohbau3d","Rohbau3D","Rohbau3D: A Shell Construction Site 3D Point Cloud Dataset",[2197],"not_applicable",[109],"德國慕尼黑周邊 14 個工地的 504 站掃描，涵蓋集合住宅、學校、辦公大樓、地下停車場與歷史地窖。","作者載明所有工地在擷取時都處於結構體（Rohbau）施工或翻修階段。","FARO Focus M70 地面光達；RGB 取自掃描儀的 HDR 影像。","不適用；單站掃描沒有軌跡。","只有原廠規格：10 m 處 2 mm、25 m 處 3 mm，更遠每公尺再加 0.1 mm。","沒有獨立參考量測；各站未註冊到共同座標系。","資料為 CC BY 4.0（Dataverse），程式碼為 MIT。","單站 TLS，沒有序列、軌跡或同一空間的重複期次，無法評估 SLAM 軌跡或地圖一致性（推論）；法向量品質只做定性評估。彩色取自光達掃描之後拍攝的 HDR 影像，與幾何並非同時取得，動態場景可能出現色彩瑕疵，作者認為多數情況可忽略（Methods）。","Methods；Technical Validation",[],{"id":2210,"shortName":2211,"title":2212,"year":286,"fulltextStatus":10,"publicationStatus":11,"construction":2066,"dataType":2097,"refTypes":2213,"sensorTags":2214,"licence":1964,"environment":2215,"stage":2216,"sensors":2217,"refTrajectory":2218,"refAccuracy":2122,"refMap":2219,"access":2007,"limitation":2220,"loc":2221,"cite":2222},"sun2025nss","Nothing Stands Still (NSS)","Nothing Stands Still: A spatiotemporal benchmark on 3D point cloud registration under large geometric and temporal change",[2197],[292],"六個施工或改建中建物內的室內區域（A 至 F），平均每區約 2,500 m²；每區 2 至 6 個時期，間隔數週至數月。","五個區域呈現施工中階段；Area D 只有改建前後兩期，沒有可見的施工。擷取時間與專案經理協調，安排在管線被覆蓋之前。","Matterport Camera v1（三腳架式 RGB-D，專有配準；規格幾何誤差約 1 英吋）。","不適用；片段位姿由對齊後的各期掃描推得。","Matterport 掃描；各期以粗對齊加 ICP 對齊，ICP 後跨期位移中位數每區 0.117 至 0.141 m，其中包含真實的施工變化。","ICP 對齊假設場景靜止，無法事先排除變化點；原始 Matterport 片段無法取得，片段為合成產生。這是定點掃描的跨期配準基準，不是 SLAM 序列。","Sec. 4.1-4.2",[],{"id":2224,"shortName":2225,"title":2226,"year":286,"fulltextStatus":10,"publicationStatus":11,"construction":24,"dataType":1960,"refTypes":2227,"sensorTags":2228,"licence":2051,"environment":2229,"stage":2230,"sensors":2231,"refTrajectory":2232,"refAccuracy":2233,"refMap":2234,"access":2235,"limitation":2236,"loc":2237,"cite":2238},"tao2025oxfordspires","Oxford Spires","The Oxford Spires Dataset: Benchmarking large-scale LiDAR-visual localisation, reconstruction and radiance field methods",[1981],[21,22,23],"牛津及周邊六處歷史場域（如 Christ Church、Keble College、Radcliffe Observatory Quarter、Blenheim Palace），每處約 10,000 m²；24 個序列，平均長度逾 400 m。","非營建場域（歷史建築）。","背負式 Frontier 裝置：三具 1.6 MP 全域快門彩色魚眼相機、Hesai QT64 光達（垂直視野 104°）與 IMU；以 PTP 與硬體同步並精密校正（Sec. 3.1、5.2）。","以 VILENS ICP 將去畸變光達掃描離線配準到合併的 TLS 地圖。","約 1 至 2 cm（Sec. 5.1.7）。","每處以 Leica RTC360 掃描並附 Leica 配準報告；合併彩色地圖的解析度為 1 cm。","儲存庫 LICENSE.md 載明 CC BY-NC-SA 4.0，並明確涵蓋資料集；商業使用需聯絡作者。","真值軌跡精度（1 至 2 cm）粗於 TLS 地圖；自動曝光使合併的彩色點雲顏色不一致。","Sec. 5.1.6-5.1.7、7",[],{"id":2240,"shortName":2241,"title":2242,"year":616,"fulltextStatus":10,"publicationStatus":11,"construction":2015,"dataType":1960,"refTypes":2243,"sensorTags":2244,"licence":2051,"environment":2245,"stage":2246,"sensors":2247,"refTrajectory":2248,"refAccuracy":2249,"refMap":2250,"access":2251,"limitation":2252,"cite":2253,"loc":2254},"trzeciak2023conslam","ConSLAM","ConSLAM: Construction Data Set for SLAM",[1981],[21,22,23],"倫敦 Whiteley's 改建案：保留歷史立面，其後新建六至九層建物；資料只涵蓋其中一層樓。","施工中工地；約四個月內每月蒐集一次，共五個序列。本站所讀的作者稿全文未說明各月的工種或施工階段。","手持原型 PointPix：Velodyne VLP-16（測距上限設為 60 m）、Alvium RGB 與近紅外相機、Xsens MTi-610 IMU（約 400 Hz）；以 ROS 訊息配對做軟體同步，整體誤差略高於 10 ms。","以邊緣特徵 ICP 將關鍵光達掃描配準到 TLS 真值掃描（以 SLAM 估計初始化）；序列 2 至 5 約 98% 的關鍵掃描配準成功，序列 1 因資料錯誤沒有真值軌跡。","未報告；重建軌跡的不確定度沒有量化，作者也承認若要用於更嚴謹的實驗，真值配準需要更嚴格的不確定度分析。","測量團隊以 Leica RTC360 掃描，多視角配準並做光束法平差，其中許多掃描對經緯儀測設的控制點定位；報告以 1 cm 距離門檻計算的重疊 RMSE（Table 2）。","GitHub README 提供下載連結；僅供學術使用，保留所有權利。","軟體時間同步誤差略高於 10 ms；LiDAR 與 IMU 之間的外參只提供旋轉。Hilti 2023 的作者指出，掃描對地圖的真值會限制整體 ATE 的精度。",[2145],"Methodology；Ground-truth trajectories；Table 2",{"id":2256,"shortName":2257,"title":2258,"year":286,"fulltextStatus":10,"publicationStatus":11,"construction":24,"dataType":1960,"refTypes":2259,"sensorTags":2260,"licence":1964,"environment":2261,"stage":2262,"sensors":2263,"refTrajectory":2264,"refAccuracy":2265,"refMap":2266,"access":2007,"limitation":2267,"loc":2268,"cite":2269},"wei2025fusionportablev2","FusionPortableV2","FusionPortableV2: A unified multi-sensor dataset for generalized SLAM across diverse platforms and scalable environments",[1833,1982],[21,22,23,995],"27 個序列、2.5 小時、38.7 km：建物、校園、地下停車場、草地、隧道、山路與都市道路、高速公路。","非營建場域；地下停車場與隧道序列不是施工場域。","手持多感測器設備（光達、影像與事件立體相機、IMU、INS），另有平台感測器（輪速編碼器、腿式感測器）。","可視時以 Leica MS60 全測站追蹤稜鏡（3 自由度、5 至 8 Hz，以三次樣條重取樣到 20 Hz）；戶外用 3DM-GQ7 雙天線 RTK-GNSS\u002FINS（6 自由度）；vehicle_street00 與 vehicle_tunnel00 只有原始 2 Hz 的 GNSS 位置。MS60 的時間偏移以最小化 ATE 估計。","MS60 標稱 1 mm（Sec. 3.3.2）；本站讀過全文，未見逐序列的真值不確定度。INS 真值只採用濾波穩定、雙天線皆為 RTK 固定解且至少 20 顆衛星、共變異數收斂的時段（Sec. 3.3.3）。","Leica RTC360（40 m 內 5.3 mm 以下）與 BLK360 掃描在 Cyclone 合併；校園彩色地圖約 0.36 km²、解析度 8 cm；地下停車場地圖約 0.037 km²、解析度 4 mm；成對掃描平均誤差 3.4 mm。","稜鏡被遮蔽或超出範圍時全測站會失鎖；部分車載序列只有 2 Hz、3 自由度的 GNSS 真值；vehicle_multilayer00 的參考軌跡是 FAST-LIO2 的輸出（以車頂 GNSS 作迴圈閉合參考），不是獨立真值（Sec. 6.2.2；Table 4）；部分外參取自 CAD。","Sec. 3.3、6.2、7.4；Table 4",[],{"id":2271,"shortName":2272,"title":2273,"year":616,"fulltextStatus":10,"publicationStatus":11,"construction":2066,"dataType":1960,"refTypes":2274,"sensorTags":2275,"licence":2051,"environment":2276,"stage":2277,"sensors":2278,"refTrajectory":2279,"refAccuracy":2280,"refMap":2281,"access":2282,"limitation":2283,"loc":2284,"cite":2285},"zhang2023hiltioxford","Hilti-Oxford (Hilti 2022)","Hilti-Oxford Dataset: A Millimeter-Accurate Benchmark for Simultaneous Localization and Mapping",[2149,1981],[21,22,23],"列支敦斯登 Schaan 的施工中工地（100 m × 30 m，含地下室共四層，紋理有限）；牛津 Sheldonian Theatre（1664 年、列入法定保護的歷史建築，六層、窄樓梯）；Hilti 總部 100 m 的辦公走廊。","只有 Exp01 至 Exp06 位於施工中工地，論文未說明施工階段；其餘序列在歷史建築與辦公走廊，但 Exp14 Basement 2 的地點文中未明示（Sec. IV）。","Hesai PandarXT-32、Alphasense 五具魚眼全域快門相機（40 Hz）、Bosch BMI085 IMU（400 Hz）；硬體同步，外參配置以 GOM ATOS Q 掃描儀核對。","稀疏：每個序列 5 至 10 個控制點，把設備尖端放在經測量定位的地面十字標記上（人工放置誤差低於 1 mm）。另有部分序列提供以 VILENS ICP 對 TLS 地圖離線配準的稠密軌跡。","稀疏控制點為毫米級；稠密軌跡 1 至 2 cm；缺少 LiDAR 與 IMU 之間的外參校正可能在控制點再增加數毫米（Sec. VI-C）。","Z+F Imager 5016 TLS，以標靶、平面對平面匹配與區塊平差配準；Sheldonian 91%、工地 95% 的掃描位置不確定度在 3 mm 內。","官方資料頁（hilti-challenge.com\u002Fdataset-2022.html）載明 CC BY-NC-SA 3.0。","每個序列只有 5 至 10 個時刻有真值；計分不考慮延遲與運算量，部分隊伍離線融合多個序列。分數大致跟隨平均 ATE，但誤差落在高分區間外或軌跡不完整時會偏離。","Sec. IV-VI",[],{"id":2287,"shortName":2288,"title":2289,"year":774,"fulltextStatus":10,"publicationStatus":11,"construction":24,"dataType":1960,"refTypes":2290,"sensorTags":2291,"licence":1964,"environment":2292,"stage":2293,"sensors":2294,"refTrajectory":2295,"refAccuracy":2296,"refMap":2297,"access":2007,"limitation":2298,"cite":2299,"loc":2300},"zhao2024subtmrs","SubT-MRS","SubT-MRS Dataset: Pushing SLAM Towards All-weather Environments",[1981],[21,22,292,23],"30 多個退化場景：都市地下、洞穴、隧道、長走廊、多樓層建物與越野，含煙、塵與雪。","非營建工地。","Velodyne VLP-16 光達、四具 RGB 魚眼相機、FLIR Boson 熱像相機、選配的 L515 深度相機與 Epson M-G365 IMU；以 PPS 硬體同步，感測器間時間差至多 3 ms（Sec. 3.1.1）。","融合掃描對真值地圖的點、面、線約束與視覺、光達、IMU 里程計；以該軌跡重建地圖並與真值地圖比對來檢驗，但未報告比對數值（Sec. 3.2）。","正文只描述為公分級（Sec. 2）；補充材料稱 FARO 真值模型的精度保證在 10 cm 內（Supp. B.3）；Hilti 2023 論文也稱以此資料舉辦的 ICCV 2023 挑戰賽真值約在 ±10 cm 內。","FARO Focus 3D S120 掃描（測距誤差 ±2 mm）；迴圈閉合後 96% 的掃描位置不確定度低於 2 mm（Sec. 3.2）。補充材料所稱的 10 cm 遠寬於這些數字。","軌跡真值依賴掃描對地圖的融合。光達與 IMU 的外參取自 CAD 模型（Sec. 3.1.1）；基準混合實測序列與由 TartanAir 衍生的模擬無人機序列（Sec. 4.1）。Hilti 2023 的作者指出，以 SubT-MRS 舉辦的 ICCV 2023 SLAM 挑戰賽真值約在 ±10 cm 內，且容易受光達退化影響。",[2145],"Sec. 2、3.1.1、3.2、4.1；Supp. B.3",{"task":2302,"platform":2315,"sensor":2325,"reference":2333},{"bim_alignment":2303,"asbuilt_modelling":2304,"progress":2305,"dimensional_qc":2306,"deformation":2307,"inspection":2308,"underground_survey":2309,"robotic_capture":2310,"registration":2311,"robot_localization":2312,"accuracy_eval":2313,"method":2314},"以 BIM 或參考地圖定位與對齊","現況與竣工建模","進度與跨期變更","尺寸與平整度檢核","變形、地工與土方監測","巡檢與缺陷檢測","地下與坑道測繪","機器人資料蒐集與掃描規劃","多站掃描配準","工地機器人與機具定位","SLAM 點雲精度評估","SLAM 方法（被工地研究採用）",{"portable":2316,"trolley":2317,"wheeled":2318,"legged":2319,"uav":2320,"vehicle":2321,"tripod":2322,"simulation":2323,"not_stated":2324},"人員攜帶（手持、背包、穿戴）","推車","輪式地面機器人","四足機器人","無人機","車輛或工程機具","固定式（三腳架）","模擬","未說明或未查證",{"lidar":2326,"commercial_slam":2327,"tls":2328,"imu":79,"camera":2329,"rgbd":2330,"gnss":2331,"unverified":2332},"光達（研究系統）","商用 SLAM 掃描儀","地面光達（TLS）","相機","RGB-D 或深度相機","GNSS","掃描器型式未說明或未查證",{"independent":2334,"partial":2335,"not_independent":2336,"none":2337,"not_reported":2338},"有獨立參考量測","部分或稀疏參考","參考非獨立","無參考量測","未報告或未擷取",{"construction":2340,"dataType":2346,"refType":2351,"sensor":2363,"licence":2365},{"all_active":2341,"active_periphery":2342,"partial_active":2343,"unreported":2344,"none":2345},"全部資料來自施工中工地","施工中工地周邊","僅部分序列或區域在施工中工地","施工狀態未報告","非營建場域",{"sequence":2347,"static":2348,"synthetic":2349,"video":2350},"感測器序列（可執行 SLAM）","定點掃描或既成點雲","合成或渲染資料","影像重建基準",{"total_station":2352,"motion_capture":2353,"gcp_sparse":2354,"tls_registration":2355,"gnss_ins":2356,"slam_derived":2357,"manual_model":2358,"synthetic":2359,"not_described":2360,"not_applicable":2361,"tls_reference":2362},"全測站或雷射追蹤儀","動作捕捉","稀疏地面控制點","掃描對 TLS 地圖配準","GNSS 與 INS","由 SLAM 或里程計產生","人工建立的參考模型","合成真值","原文未說明","無獨立參考（非軌跡資料）","TLS 參考點雲（非軌跡資料）",{"lidar":2364,"tls":2328,"camera":2329,"rgbd":2330,"imu":79,"gnss":2331},"光達",{"data_licence":2366,"unclear":2367,"not_found":2368},"已查到資料授權聲明","找到授權但適用範圍不明","未查證或未找到",[62,326,619,105,447,238,423,16,801,63,424,932],6,3,1790510652870]