[{"data":1,"prerenderedAt":580},["ShallowReactive",2],{"method-stuhrenberg2025liobim":3},{"method":4,"reference":62,"equipment":83,"figures":138,"results":178},{"id":5,"label":6,"shortName":7,"title":8,"year":9,"era":10,"cluster":11,"scope":12,"keyIdeaZh":13,"keyIdeaEn":14,"fulltextStatus":15,"publicationStatus":16,"recommendation":17,"constructionRelevance":18,"validationEnvironment":19,"strengths":24,"limitations":31,"sensors":38,"platform":43,"estimator":46,"association":47,"timeModel":48,"deskew":49,"loopClosure":50,"globalOptimization":51,"mapRepresentation":52,"prior":53,"outputGeometry":54,"compute":55,"codeUrl":56,"codeLicense":57,"relatedVersions":58},"stuhrenberg2025liobim","Stührenberg & Smarsly, 2025","LIO-BIM","LIO-BIM - Coupling lidar inertial odometry with building information modeling for robot localization and mapping",2025,"recent","C11b","localization_in_prior_map_or_bim","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。","Couples lidar-inertial odometry with local-map-to-BIM matching to localize and map relative to limited-LOD BIM models despite scan-BIM deviations, validated in an office and on the ConSLAM construction dataset.","full_text_reviewed","peer_reviewed_published","main_body","以 ConSLAM（施工中建築資料集）驗證（摘要）；另有辦公室實測。",[20,21,22,23],"completed_building","public_benchmark","real_construction_site","independent_reference",[25,26,27,28,29,30],"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)",[32,33,34,35,36,37],"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",[39,40,41,42],"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",[44,45],"legged (Unitree A1-based 'IDOG')","handheld (ConSLAM dataset)","LIO-SAM-based factor graph; BIM factors added when local-map-to-BIM scan matching converges with inlier RMSE below and fitness above thresholds, with noise variance set to the inlier RMSE (Sec. 3.4)","feature-based ICP with edge (point-to-line) and planar (point-to-plane) correspondences solved by Levenberg-Marquardt, used both for LIO against preceding scans and for matching a 5 m local keyframe feature map to edge and planar feature clouds sampled from the IFC meshes (500 points\u002Fm2, curvature thresholds 0.6 and 0.1)","not_reported","the latest lidar scan is deskewed in the LIO-SAM-derived front end before feature extraction (Sec. 3.3)","no loop closure module is described; long-term drift is corrected by unary BIM factors from accepted local-map-to-BIM matches","GTSAM factor graph with LIO factors and unary BIM factors (Gaussian noise with variance equal to the inlier RMSE), optimised with the Bayes tree approach of [49]; BIM matches accepted only if converged with inlier RMSE \u003C 0.1 m and fitness > 0.65; 3 keyframes skipped after an accepted match","keyframe-based lidar feature point cloud map in the map frame, aligned to the BIM frame; the BIM is stored as sparse edge and planar feature point clouds (PCD) extracted from IFC geometry after filtering windows, doors and furniture","BIM (IFC via IfcOpenShell); AprilTags placed at identical locations in BIM and building give the initial map-to-BIM transformation (or a supplied initial transform)","keyframe trajectory (TUM format) and point cloud map aligned with the BIM model; evaluated by APE against ConSLAM and SLAM2REF ground truth and by inlier RMSE (0.3 m) against TLS","Intel NUC11TNKV7 (Core i7-1185G7, 32 GB RAM) for both tests; lidar scan matching mean 0.028 s (office) and 0.082-0.095 s (ConSLAM) with 0-14.3% of frames skipped; BIM scan matching mean 0.85 s (office) and 1.48-1.77 s (ConSLAM) with 6.7-33.1% of keyframes skipped","https:\u002F\u002Fgithub.com\u002Fjanstueh\u002FLIO-BIM","BSD-3-Clause (copyright Tixiao Shan 2020 and Jan Stührenberg 2025)",[59],{"relation":60,"title":61,"doi_or_url":56},"code_release","LIO-BIM repository (ROS 2, builds atop LIO-SAM)",{"id":5,"kind":63,"shortName":7,"title":64,"authors":65,"year":9,"venue":68,"venueType":69,"publisher":70,"volumeIssuePages":71,"doi":72,"arxivId":73,"url":74,"firstPublicDate":75,"publicationStatus":16,"metadataStatus":76,"fulltextStatus":15,"era":10,"classicReason":77,"codeUrl":56,"cluster":11,"topics":78,"mdpi":79,"verification":80,"label":6,"fulltextRoute":81,"versionRead":82,"addedByCensus":79},"method","LIO-BIM – Coupling lidar inertial odometry with building information modeling for robot localization and mapping",[66,67],"Jan Stührenberg","Kay Smarsly","Advanced Engineering Informatics","journal","Elsevier","66, 103477","10.1016\u002Fj.aei.2025.103477",null,"https:\u002F\u002Fapi.openalex.org\u002Fworks\u002Fdoi:10.1016\u002Fj.aei.2025.103477","2025-05-24","metadata_verified","not_applicable",[11],false,"corrected","publisher OA","Version of record, Advanced Engineering Informatics 66 (2025) 103477, ScienceDirect HTML, open access under CC BY 4.0",[84,90,95,99,104,110,117,122,127,131,134],{"category":85,"model":86,"canonical":86,"role":87,"dataset":73,"specs":88,"locator":89},"platform","Unitree A1 ('IDOG' Intelligent DOcumentation Gadget)","method input","quadruped carrying lidar, IMU, camera, external computer and extra battery; manually controlled in the office test","Sec. 4.5; Fig. 7; Table 4",{"category":91,"model":92,"canonical":92,"role":87,"dataset":73,"specs":93,"locator":94},"lidar","Velodyne VLP-16","16 channels, 100 m, up to +-3 cm, vertical FoV 30 deg (2.0 deg resolution), 360 deg horizontal, 5-20 Hz; recorded at 10 Hz","Table 4; Sec. 4.5",{"category":96,"model":97,"canonical":97,"role":87,"dataset":73,"specs":98,"locator":94},"imu","LORD MicroStrain 3DM-GX5-25","accelerometer +-8 g, 1 kHz; gyroscope +-300 deg\u002Fs, 4 kHz; magnetometer +-2.5 Gauss, 50 Hz; recorded at 500 Hz",{"category":100,"model":101,"canonical":102,"role":87,"dataset":73,"specs":103,"locator":94},"camera","Intel RealSense D435i","Intel RealSense D435I","RGB up to 1920 x 1080, FoV 69 x 42 deg; recorded at 15 Hz, 640 x 480; used to detect AprilTags",{"category":105,"model":106,"canonical":106,"role":107,"dataset":73,"specs":108,"locator":109},"compute","Intel NUC11TNKV7","compute for runtime","Intel Core i7-1185G7, 32 GB RAM; used for both validation tests","Table 2; Table 4",{"category":111,"model":112,"canonical":113,"role":114,"dataset":73,"specs":115,"locator":116},"tls_scanner","Faro Focus S 70","FARO Focus S70","reference or ground truth","reference scan of the office; maximum registration point error 0.35 cm","Table 2; Sec. 4.5",{"category":118,"model":119,"canonical":119,"role":87,"dataset":73,"specs":120,"locator":121},"other","AprilTag fiducial tags","printed tags placed at identical locations in the building and in the BIM (Revit AprilTag family exported as IfcBuildingElementProxy)","Sec. 3.2",{"category":91,"model":92,"canonical":92,"role":123,"dataset":124,"specs":125,"locator":126},"dataset sensor","ConSLAM","handheld ConSLAM rig","Table 2",{"category":96,"model":128,"canonical":128,"role":123,"dataset":124,"specs":129,"locator":130},"Xsens MTi-610","handheld ConSLAM rig; sequence 1 IMU data faulty","Table 2; Sec. 4.6",{"category":100,"model":132,"canonical":132,"role":123,"dataset":124,"specs":133,"locator":130},"Alvium U-319c, 3.2 MP camera","colour camera of the ConSLAM rig; AprilTags visible in its images",{"category":111,"model":135,"canonical":136,"role":114,"dataset":124,"specs":137,"locator":130},"Leica RTC 360","Leica RTC360","ConSLAM ground-truth TLS; registration RMSE 0.902-0.994 cm per [51]",[139,152,162,170],{"refId":5,"refLabel":6,"fig":140,"whatZh":141,"license":142,"licenseUrl":143,"sourceUrl":144,"src":145,"width":146,"height":147,"thumb":148,"thumbWidth":149,"thumbHeight":150,"modified":151},"Fig. 1","LIO-BIM 系統架構：光達慣性里程計、AprilTag 初始對齊與 BIM 掃描匹配","CC BY 4.0","http:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby\u002F4.0\u002F","https:\u002F\u002Fars.els-cdn.com\u002Fcontent\u002Fimage\u002F1-s2.0-S1474034625003702-gr1_lrg.jpg","\u002Ffigure-files\u002Fstuhrenberg2025liobim\u002Ffig-1.webp",1400,1254,"\u002Ffigure-files\u002Fstuhrenberg2025liobim\u002Ffig-1.thumb.webp",480,430,"resized to at most 1400 px wide and converted to WebP",{"refId":5,"refLabel":6,"fig":153,"whatZh":154,"license":142,"licenseUrl":143,"sourceUrl":155,"src":156,"width":157,"height":158,"thumb":159,"thumbWidth":149,"thumbHeight":160,"modified":161},"Fig. 7","搭載 VLP-16、IMU 與相機的 IDOG 四足機器人","https:\u002F\u002Fars.els-cdn.com\u002Fcontent\u002Fimage\u002F1-s2.0-S1474034625003702-gr7_lrg.jpg","\u002Ffigure-files\u002Fstuhrenberg2025liobim\u002Ffig-7.webp",1016,803,"\u002Ffigure-files\u002Fstuhrenberg2025liobim\u002Ffig-7.thumb.webp",379,"converted to WebP",{"refId":5,"refLabel":6,"fig":163,"whatZh":164,"license":142,"licenseUrl":143,"sourceUrl":165,"src":166,"width":146,"height":167,"thumb":168,"thumbWidth":149,"thumbHeight":169,"modified":151},"Fig. 11","LIO-BIM 軌跡與點雲對齊 BIM 的結果（辦公室與 ConSLAM 序列 2 至 5）","https:\u002F\u002Fars.els-cdn.com\u002Fcontent\u002Fimage\u002F1-s2.0-S1474034625003702-gr11_lrg.jpg","\u002Ffigure-files\u002Fstuhrenberg2025liobim\u002Ffig-11.webp",1419,"\u002Ffigure-files\u002Fstuhrenberg2025liobim\u002Ffig-11.thumb.webp",487,{"refId":5,"refLabel":6,"fig":171,"whatZh":172,"license":142,"licenseUrl":143,"sourceUrl":173,"src":174,"width":146,"height":175,"thumb":176,"thumbWidth":149,"thumbHeight":177,"modified":151},"Fig. 13","LIO-BIM 點雲與 TLS 掃描對齊，依點對點距離著色","https:\u002F\u002Fars.els-cdn.com\u002Fcontent\u002Fimage\u002F1-s2.0-S1474034625003702-gr13_lrg.jpg","\u002Ffigure-files\u002Fstuhrenberg2025liobim\u002Ffig-13.webp",1352,"\u002Ffigure-files\u002Fstuhrenberg2025liobim\u002Ffig-13.thumb.webp",464,{"totalRows":179,"groupCount":180,"groups":181,"others":566},111,6,[182,296,380,478],{"slug":183,"group":184,"sourceId":5,"sourceLabel":6,"table":185,"selfRows":186,"metrics":187,"seqs":204,"entrants":218,"cells":221,"outcomes":289,"locators":290,"hardware":291,"wordings":293,"notes":294},"stuhrenberg2025liobim-table-8","stuhrenberg2025liobim:Table 8","Table 8",30,[188,193,195,197,199,201],{"label":189,"unit":190,"statistic":191,"alignment":192},"processing time of scan matching with the BIM model per keyframe","s","mean","none",{"label":189,"unit":190,"statistic":194,"alignment":192},"std",{"label":189,"unit":190,"statistic":196,"alignment":192},"median",{"label":198,"unit":190,"statistic":48,"alignment":192},"processing time of scan matching with the BIM model per keyframe (minimum)",{"label":189,"unit":190,"statistic":200,"alignment":192},"max",{"label":202,"unit":203,"statistic":48,"alignment":192},"percentage of keyframes skipped due to processing times","%",[205,209,212,214,216],{"dataset":206,"sequence":207,"environment":208},"own recording (IDOG quadruped)","indoor office environment (39 m x 16 m, 113 m trajectory)","cluttered indoor office, existing building",{"dataset":124,"sequence":210,"environment":211},"Sequence 2 (225 m)","construction site (ConSLAM, handheld recording)",{"dataset":124,"sequence":213,"environment":211},"Sequence 3 (340 m)",{"dataset":124,"sequence":215,"environment":211},"Sequence 4 (275 m)",{"dataset":124,"sequence":217,"environment":211},"Sequence 5 (320 m)",[219],{"name":7,"methodId":5,"linkable":220,"proposed":220,"self":220},true,[222,226,229,232,235,238,240,242,244,246,248,250,252,254,256,258,260,262,264,266,268,270,272,274,276,278,281,283,285,287],[223,223,223,224,225,223,223,225,223],0,0.84947,-1,[223,223,227,228,225,223,223,225,223],1,1.48145,[223,223,230,231,225,223,223,225,223],2,1.71745,[223,223,233,234,225,223,223,225,223],3,1.58183,[223,223,236,237,225,223,223,225,223],4,1.76646,[223,227,223,239,225,223,223,225,223],0.36499,[223,227,227,241,225,223,223,225,223],0.76778,[223,227,230,243,225,223,223,225,223],0.84832,[223,227,233,245,225,223,223,225,223],0.74531,[223,227,236,247,225,223,223,225,223],0.92354,[223,230,223,249,225,223,223,225,223],0.74824,[223,230,227,251,225,223,223,225,223],1.29328,[223,230,230,253,225,223,223,225,223],1.56127,[223,230,233,255,225,223,223,225,223],1.47903,[223,230,236,257,225,223,223,225,223],1.68111,[223,233,223,259,225,223,223,225,223],0.28527,[223,233,227,261,225,223,223,225,223],0.38502,[223,233,230,263,225,223,223,225,223],0.12126,[223,233,233,265,225,223,223,225,223],0.23809,[223,233,236,267,225,223,223,225,223],0.23664,[223,236,223,269,225,223,223,225,223],2.26235,[223,236,227,271,225,223,223,225,223],3.17739,[223,236,230,273,225,223,223,225,223],3.25303,[223,236,233,275,225,223,223,225,223],3.22739,[223,236,236,277,225,223,223,225,223],3.36233,[223,279,223,280,225,223,223,225,223],5,6.667,[223,279,227,282,225,223,223,225,223],24.812,[223,279,230,284,225,223,223,225,223],33.146,[223,279,233,286,225,223,223,225,223],31.191,[223,279,236,288,225,223,223,225,223],23.616,[],[185],[292],"Intel NUC11TNKV7 (Intel Core i7-1185G7, 32 GB RAM)",[],[295],"Processing times recorded on the same Intel NUC11TNKV7 for both tests (office rosbag played back to LIO-BIM; ConSLAM sequences processed on the same computer); LIO-BIM only, no baseline timings",{"slug":297,"group":298,"sourceId":5,"sourceLabel":6,"table":299,"selfRows":186,"metrics":300,"seqs":310,"entrants":316,"cells":318,"outcomes":375,"locators":376,"hardware":377,"wordings":378,"notes":379},"stuhrenberg2025liobim-table-9","stuhrenberg2025liobim:Table 9","Table 9",[301,303,304,305,307,308],{"label":302,"unit":190,"statistic":191,"alignment":192},"processing time of lidar scan matching (LIO) per lidar frame (10 Hz lidar)",{"label":302,"unit":190,"statistic":194,"alignment":192},{"label":302,"unit":190,"statistic":196,"alignment":192},{"label":306,"unit":190,"statistic":48,"alignment":192},"processing time of lidar scan matching (LIO) per lidar frame (10 Hz lidar) (minimum)",{"label":302,"unit":190,"statistic":200,"alignment":192},{"label":309,"unit":203,"statistic":48,"alignment":192},"percentage of lidar frames skipped",[311,312,313,314,315],{"dataset":206,"sequence":207,"environment":208},{"dataset":124,"sequence":210,"environment":211},{"dataset":124,"sequence":213,"environment":211},{"dataset":124,"sequence":215,"environment":211},{"dataset":124,"sequence":217,"environment":211},[317],{"name":7,"methodId":5,"linkable":220,"proposed":220,"self":220},[319,321,323,325,327,329,331,333,335,337,339,341,343,345,347,349,351,353,354,355,356,358,360,362,364,366,367,369,371,373],[223,223,223,320,225,223,223,225,223],0.02836,[223,223,227,322,225,223,223,225,223],0.0824,[223,223,230,324,225,223,223,225,223],0.0943,[223,223,233,326,225,223,223,225,223],0.09174,[223,223,236,328,225,223,223,225,223],0.09549,[223,227,223,330,225,223,223,225,223],0.01407,[223,227,227,332,225,223,223,225,223],0.04119,[223,227,230,334,225,223,223,225,223],0.05252,[223,227,233,336,225,223,223,225,223],0.05406,[223,227,236,338,225,223,223,225,223],0.05451,[223,230,223,340,225,223,223,225,223],0.02648,[223,230,227,342,225,223,223,225,223],0.0756,[223,230,230,344,225,223,223,225,223],0.08682,[223,230,233,346,225,223,223,225,223],0.08134,[223,230,236,348,225,223,223,225,223],0.09241,[223,233,223,350,225,223,223,225,223],0.00005,[223,233,227,352,225,223,223,225,223],0.00003,[223,233,230,350,225,223,223,225,223],[223,233,233,350,225,223,223,225,223],[223,233,236,350,225,223,223,225,223],[223,236,223,357,225,223,223,225,223],0.15316,[223,236,227,359,225,223,223,225,223],0.48707,[223,236,230,361,225,223,223,225,223],0.51106,[223,236,233,363,225,223,223,225,223],0.83815,[223,236,236,365,225,223,223,225,223],0.44883,[223,279,223,223,225,223,223,225,223],[223,279,227,368,225,223,223,225,223],6.125,[223,279,230,370,225,223,223,225,223],13.709,[223,279,233,372,225,223,223,225,223],13.327,[223,279,236,374,225,223,223,225,223],14.327,[],[299],[292],[],[295],{"slug":381,"group":382,"sourceId":5,"sourceLabel":6,"table":383,"selfRows":384,"metrics":385,"seqs":397,"entrants":402,"cells":407,"outcomes":472,"locators":473,"hardware":474,"wordings":475,"notes":476},"stuhrenberg2025liobim-table-5","stuhrenberg2025liobim:Table 5","Table 5",16,[386,390,392,395],{"label":387,"unit":388,"statistic":389,"alignment":48},"translational APE RMSE","cm","RMSE",{"label":391,"unit":388,"statistic":200,"alignment":48},"translational APE max",{"label":393,"unit":394,"statistic":389,"alignment":48},"rotational APE RMSE","deg",{"label":396,"unit":394,"statistic":200,"alignment":48},"rotational APE max",[398,399,400,401],{"dataset":124,"sequence":210,"environment":211},{"dataset":124,"sequence":213,"environment":211},{"dataset":124,"sequence":215,"environment":211},{"dataset":124,"sequence":217,"environment":211},[403,404],{"name":7,"methodId":5,"linkable":220,"proposed":220,"self":220},{"name":405,"methodId":406,"linkable":220,"proposed":79,"self":79},"LIO-SAM","liosam2020",[408,410,412,414,416,418,420,422,424,426,428,430,432,434,436,438,440,442,444,446,448,450,452,454,456,458,460,462,464,466,468,470],[223,223,223,409,225,223,225,225,223],10.21,[223,227,223,411,225,223,225,225,223],103.84,[223,230,223,413,225,223,225,225,223],1.3665,[223,233,223,415,225,223,225,225,223],16.8973,[223,223,227,417,225,223,225,225,223],10.4,[223,227,227,419,225,223,225,225,223],32.06,[223,230,227,421,225,223,225,225,223],1.3791,[223,233,227,423,225,223,225,225,223],7.5847,[223,223,230,425,225,223,225,225,223],12.85,[223,227,230,427,225,223,225,225,223],37.29,[223,230,230,429,225,223,225,225,223],1.1199,[223,233,230,431,225,223,225,225,223],4.2863,[223,223,233,433,225,223,225,225,223],15.68,[223,227,233,435,225,223,225,225,223],73.66,[223,230,233,437,225,223,225,225,223],1.4438,[223,233,233,439,225,223,225,225,223],7.1668,[227,223,223,441,225,223,225,225,223],27.28,[227,227,223,443,225,223,225,225,223],112.84,[227,230,223,445,225,223,225,225,223],1.5796,[227,233,223,447,225,223,225,225,223],12.07,[227,223,227,449,225,223,225,225,223],9.94,[227,227,227,451,225,223,225,225,223],33.48,[227,230,227,453,225,223,225,225,223],1.3275,[227,233,227,455,225,223,225,225,223],6.4213,[227,223,230,457,225,223,225,225,223],10.68,[227,227,230,459,225,223,225,225,223],29.15,[227,230,230,461,225,223,225,225,223],1.2403,[227,233,230,463,225,223,225,225,223],5.8163,[227,223,233,465,225,223,225,225,223],16.09,[227,227,233,467,225,223,225,225,223],54.83,[227,230,233,469,225,223,225,225,223],1.4824,[227,233,233,471,225,223,225,225,223],6.9557,[],[383],[],[],[477],"APE of keyframe trajectories vs ConSLAM ground truth; trajectories aligned with Umeyama alignment (evo), scale handling not stated; ConSLAM sequence 1 excluded (faulty IMU)",{"slug":479,"group":480,"sourceId":5,"sourceLabel":6,"table":481,"selfRows":384,"metrics":482,"seqs":487,"entrants":492,"cells":495,"outcomes":560,"locators":561,"hardware":562,"wordings":563,"notes":564},"stuhrenberg2025liobim-table-6","stuhrenberg2025liobim:Table 6","Table 6",[483,484,485,486],{"label":387,"unit":388,"statistic":389,"alignment":48},{"label":391,"unit":388,"statistic":200,"alignment":48},{"label":393,"unit":394,"statistic":389,"alignment":48},{"label":396,"unit":394,"statistic":200,"alignment":48},[488,489,490,491],{"dataset":124,"sequence":210,"environment":211},{"dataset":124,"sequence":213,"environment":211},{"dataset":124,"sequence":215,"environment":211},{"dataset":124,"sequence":217,"environment":211},[493,494],{"name":7,"methodId":5,"linkable":220,"proposed":220,"self":220},{"name":405,"methodId":406,"linkable":220,"proposed":79,"self":79},[496,498,500,502,504,506,508,510,512,514,516,518,520,522,524,526,528,530,532,534,536,538,540,542,544,546,548,550,552,554,556,558],[223,223,223,497,225,223,225,225,223],5.97,[223,227,223,499,225,223,225,225,223],20.67,[223,230,223,501,225,223,225,225,223],0.7992,[223,233,223,503,225,223,225,225,223],4.4042,[223,223,227,505,225,223,225,225,223],26.68,[223,227,227,507,225,223,225,225,223],195.7,[223,230,227,509,225,223,225,225,223],1.473,[223,233,227,511,225,223,225,225,223],7.7004,[223,223,230,513,225,223,225,225,223],10.25,[223,227,230,515,225,223,225,225,223],23.2,[223,230,230,517,225,223,225,225,223],1.091,[223,233,230,519,225,223,225,225,223],4.5813,[223,223,233,521,225,223,225,225,223],12.57,[223,227,233,523,225,223,225,225,223],34.88,[223,230,233,525,225,223,225,225,223],1.2362,[223,233,233,527,225,223,225,225,223],6.8999,[227,223,223,529,225,223,225,225,223],6.58,[227,227,223,531,225,223,225,225,223],28.62,[227,230,223,533,225,223,225,225,223],0.8347,[227,233,223,535,225,223,225,225,223],4.4082,[227,223,227,537,225,223,225,225,223],26.23,[227,227,227,539,225,223,225,225,223],190.18,[227,230,227,541,225,223,225,225,223],1.469,[227,233,227,543,225,223,225,225,223],6.2511,[227,223,230,545,225,223,225,225,223],10.09,[227,227,230,547,225,223,225,225,223],27.5,[227,230,230,549,225,223,225,225,223],1.0193,[227,233,230,551,225,223,225,225,223],4.904,[227,223,233,553,225,223,225,225,223],18.02,[227,227,233,555,225,223,225,225,223],44.3,[227,230,233,557,225,223,225,225,223],1.3595,[227,233,233,559,225,223,225,225,223],5.3831,[],[481],[],[],[565],"APE vs the ground-truth trajectories of SLAM2REF [17]; same runs as Table 5; alignment procedure for this comparison not separately stated",[567,573],{"group":568,"slug":569,"sourceLabel":6,"table":570,"selfRows":571,"datasets":572},"stuhrenberg2025liobim:Table 7","stuhrenberg2025liobim-table-7","Table 7",10,[124,206],{"group":574,"slug":575,"sourceLabel":576,"table":383,"selfRows":577,"datasets":578},"bimloc2026:Table 5","bimloc2026-table-5","Zhang et al., 2026",9,[579],"CityU construction benchmark",1790510655474]