[{"data":1,"prerenderedAt":523},["ShallowReactive",2],{"method-feng2026integratedslam":3},{"method":4,"reference":55,"equipment":79,"figures":108,"results":146},{"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":22,"limitations":28,"sensors":36,"platform":40,"estimator":43,"association":44,"timeModel":45,"deskew":45,"loopClosure":46,"globalOptimization":47,"mapRepresentation":48,"prior":49,"outputGeometry":50,"compute":51,"codeUrl":52,"codeLicense":53,"relatedVersions":54},"feng2026integratedslam","Feng et al., 2026","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",2026,"recent","C11b","full_slam_with_global_correction","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。","System-integration paper: two-stage ground segmentation, FEC clustering of non-ground points, LeGO-LOAM-style two-step LiDAR-only registration and Scan Context++ loop closure with pose-graph optimization, run on a Jetson Xavier NX wheeled robot with an RS-Helios-16P; it beats F-LOAM and LeGO-LOAM in a Gazebo model (ATE RMSE 5.40 m over 1293 m) and on the same building's active site (2.87 m over 1004 m; 0.79% mean dimension error vs drawings), though the real-site ground-truth source is not described.","full_text_reviewed","peer_reviewed_published","supplementary","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）。",[20,21],"simulation","real_construction_site",[23,24,25,26,27],"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)",[29,30,31,32,33,34,35],"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",[37,38,39],"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)",[41,42],"wheeled UGV (Ackermann-steered construction robot base, teleoperated at 0.50 m\u002Fs on site)","simulation (Gazebo, robot moved at 1.00 m\u002Fs)","LiDAR-only feature-based odometry with two-step registration following LeGO-LOAM (ground planar features estimate z, roll and pitch; edge features from FEC-clustered non-ground points estimate x, y and yaw), scan-to-map mapping, and pose-graph optimization with Scan Context++ loop constraints (Sec. 3.1, Algorithm 1)","plane features from two-stage-segmented ground points (RANSAC fit on two low beams, 6 deg coarse threshold, 3 deg normal-consistency check) and edge features from FEC clusters of non-ground points (dth = 0.20 m, 30 to 50 point minimum cluster); scan-to-map feature alignment (Sec. 3.1, 3.3, 3.4)","not_reported","Scan Context++ descriptor on ground-removed clustered points, run in a parallel thread (Sec. 3.5, Algorithm 1)","pose-graph optimization fusing odometry and loop-closure constraints (Sec. 3.1, Algorithm 1)","global point cloud map built by scan-to-map feature alignment, saved as PCD (Sec. 3.1, Sec. 5.2)","none for SLAM; CAD drawings of the building used to build the Gazebo world and as the dimension reference for map evaluation (Sec. 4.1, Sec. 5.2.2)","point cloud map (PCD) and TUM-format trajectory (Sec. 5.2)","NVIDIA Jetson Xavier NX (6-core Carmel ARM v8.2, 384-core Volta GPU, 8 GB): 121.62 ms per frame, 81.75% CPU, 1866.52 MB memory; odometry 10 Hz, mapping 2 Hz (Sec. 3.2, Sec. 5.2.4, Table 8)",null,"not_verified",[],{"id":5,"kind":56,"shortName":7,"title":8,"authors":57,"year":9,"venue":65,"venueType":66,"publisher":67,"volumeIssuePages":68,"doi":69,"arxivId":52,"url":70,"firstPublicDate":71,"publicationStatus":16,"metadataStatus":72,"fulltextStatus":15,"era":10,"classicReason":73,"codeUrl":52,"cluster":11,"topics":74,"mdpi":75,"verification":76,"label":6,"fulltextRoute":77,"versionRead":78,"addedByCensus":75},"method",[58,59,60,61,62,63,64],"Chunyong Feng","Junqi Yu","Wei Quan","Kai Wang","Jugang Guo","Yisheng Chen","Zhenping Dong","Developments in the Built Environment","journal","Elsevier","26, 100913","10.1016\u002Fj.dibe.2026.100913","https:\u002F\u002Fapi.openalex.org\u002Fworks\u002Fdoi:10.1016\u002Fj.dibe.2026.100913","2026-03-26","metadata_verified","not_applicable",[11],false,"confirmed","publisher OA","version of record, Developments in the Built Environment 26 (2026) 100913, ScienceDirect open-access HTML (online 2026-03-26)",[80,86,93,99,103],{"category":81,"model":82,"canonical":82,"role":83,"dataset":52,"specs":84,"locator":85},"lidar","RS-Helios-16P","method input","16 beams; 360 deg horizontal and +-15 deg vertical FOV; 0.4 deg horizontal and 2 deg vertical resolution; 10 Hz; +-2 cm ranging; 0.2 to 150 m; mounted horizontally at 0.88 m","Sec. 3.2, Fig. 2",{"category":87,"model":88,"canonical":88,"role":89,"dataset":90,"specs":91,"locator":92},"imu","nine-axis IMU","dataset sensor","authors' simulation and real-site rosbags","200 Hz; mounted 0.15 m below the LiDAR; recorded but not used by the LiDAR-only method","Sec. 3.2, Sec. 4.2, Sec. 5.1",{"category":94,"model":95,"canonical":95,"role":96,"dataset":52,"specs":97,"locator":98},"compute","NVIDIA Jetson Xavier NX","compute for runtime","6-core Carmel ARM v8.2 CPU, 384-core Volta GPU, 8 GB LPDDR4x; Ubuntu 18.04, ROS Melodic","Sec. 3.2, Sec. 5.2.4, Table 8",{"category":94,"model":100,"canonical":100,"role":96,"dataset":52,"specs":101,"locator":102},"laptop with Intel Core i7-11800H and NVIDIA GeForce RTX 3060","remote-control station connected over local Wi-Fi (Sec. 3.2); Sec. 4.1 states the Gazebo simulation ran on a computer with the same CPU and GPU, without saying it is this laptop; runtime figures in Table 8 are from the Jetson","Sec. 3.2, Sec. 4.1",{"category":104,"model":105,"canonical":105,"role":83,"dataset":52,"specs":106,"locator":107},"platform","construction robot experimental platform (Ackermann-steered wheeled base)","24 V\u002F30 Ah battery; front steering and rear drive via PWM motor controller; max 3.0 m\u002Fs; CAN bus to the Jetson; teleoperated with an Xbox 360 controller at 0.50 m\u002Fs on site","Sec. 3.2, Sec. 5.1, Fig. 2",[109,122,130,138],{"refId":5,"refLabel":6,"fig":110,"whatZh":111,"license":112,"licenseUrl":113,"sourceUrl":114,"src":115,"width":116,"height":117,"thumb":118,"thumbWidth":119,"thumbHeight":120,"modified":121},"Fig. 1","整合式光達 SLAM 系統架構與資料流（地面分割、FEC 分群、兩步配準、Scan Context++ 迴圈偵測）","CC BY-NC 4.0","http:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby-nc\u002F4.0\u002F","https:\u002F\u002Fars.els-cdn.com\u002Fcontent\u002Fimage\u002F1-s2.0-S2666165926000712-gr1_lrg.jpg","\u002Ffigure-files\u002Ffeng2026integratedslam\u002Ffig-1.webp",1400,510,"\u002Ffigure-files\u002Ffeng2026integratedslam\u002Ffig-1.thumb.webp",480,175,"resized to at most 1400 px wide and converted to WebP",{"refId":5,"refLabel":6,"fig":123,"whatZh":124,"license":112,"licenseUrl":113,"sourceUrl":125,"src":126,"width":116,"height":127,"thumb":128,"thumbWidth":119,"thumbHeight":129,"modified":121},"Fig. 2","施工機器人硬體平台與主要設備（RS-Helios-16P、IMU、Jetson Xavier NX）","https:\u002F\u002Fars.els-cdn.com\u002Fcontent\u002Fimage\u002F1-s2.0-S2666165926000712-gr2_lrg.jpg","\u002Ffigure-files\u002Ffeng2026integratedslam\u002Ffig-2.webp",785,"\u002Ffigure-files\u002Ffeng2026integratedslam\u002Ffig-2.thumb.webp",269,{"refId":5,"refLabel":6,"fig":131,"whatZh":132,"license":112,"licenseUrl":113,"sourceUrl":133,"src":134,"width":116,"height":135,"thumb":136,"thumbWidth":119,"thumbHeight":137,"modified":121},"Fig. 10","機器人在施工中醫院大樓工地收集資料的情形與軌跡","https:\u002F\u002Fars.els-cdn.com\u002Fcontent\u002Fimage\u002F1-s2.0-S2666165926000712-gr10_lrg.jpg","\u002Ffigure-files\u002Ffeng2026integratedslam\u002Ffig-10.webp",1054,"\u002Ffigure-files\u002Ffeng2026integratedslam\u002Ffig-10.thumb.webp",361,{"refId":5,"refLabel":6,"fig":139,"whatZh":140,"license":112,"licenseUrl":113,"sourceUrl":141,"src":142,"width":116,"height":143,"thumb":144,"thumbWidth":119,"thumbHeight":145,"modified":121},"Fig. 13","實地工地中三種 SLAM 方法點雲地圖的多視角比較（第一部分）","https:\u002F\u002Fars.els-cdn.com\u002Fcontent\u002Fimage\u002F1-s2.0-S2666165926000712-gr13a_lrg.jpg","\u002Ffigure-files\u002Ffeng2026integratedslam\u002Ffig-13.webp",1868,"\u002Ffigure-files\u002Ffeng2026integratedslam\u002Ffig-13.thumb.webp",640,{"totalRows":147,"groupCount":148,"groups":149,"others":497},48,9,[150,285,377,442],{"slug":151,"group":152,"sourceId":5,"sourceLabel":6,"table":153,"selfRows":154,"metrics":155,"seqs":177,"entrants":200,"cells":210,"outcomes":279,"locators":280,"hardware":281,"wordings":282,"notes":283},"feng2026integratedslam-table-2","feng2026integratedslam:Table 2","Table 2",10,[156,159,161,163,165,167,169,171,173,175],{"label":157,"unit":158,"statistic":45,"alignment":45},"Percentage error, site A-B","%",{"label":160,"unit":158,"statistic":45,"alignment":45},"Percentage error, site C-D",{"label":162,"unit":158,"statistic":45,"alignment":45},"Percentage error, site E-F",{"label":164,"unit":158,"statistic":45,"alignment":45},"Percentage error, site G-H",{"label":166,"unit":158,"statistic":45,"alignment":45},"Percentage error, site I-J",{"label":168,"unit":158,"statistic":45,"alignment":45},"Percentage error, site K-L",{"label":170,"unit":158,"statistic":45,"alignment":45},"Percentage error, site M-N",{"label":172,"unit":158,"statistic":45,"alignment":45},"Percentage error, site O-P",{"label":174,"unit":158,"statistic":45,"alignment":45},"Percentage error, site Q-R",{"label":176,"unit":158,"statistic":45,"alignment":45},"Percentage error, site S-T",[178,182,184,186,188,190,192,194,196,198],{"dataset":179,"sequence":180,"environment":181},"authors' Gazebo simulation of a hospital outpatient building (Xi'an)","A-B","Gazebo model of a large public building floor (292 m x 142 m x 6 m), long windowless corridors",{"dataset":179,"sequence":183,"environment":181},"C-D",{"dataset":179,"sequence":185,"environment":181},"E-F",{"dataset":179,"sequence":187,"environment":181},"G-H",{"dataset":179,"sequence":189,"environment":181},"I-J",{"dataset":179,"sequence":191,"environment":181},"K-L",{"dataset":179,"sequence":193,"environment":181},"M-N",{"dataset":179,"sequence":195,"environment":181},"O-P",{"dataset":179,"sequence":197,"environment":181},"Q-R",{"dataset":179,"sequence":199,"environment":181},"S-T",[201,205,208],{"name":202,"methodId":203,"linkable":204,"proposed":75,"self":75},"F-LOAM","floam2021",true,{"name":206,"methodId":207,"linkable":204,"proposed":75,"self":75},"LeGO-LOAM","legoloam2018",{"name":209,"methodId":5,"linkable":204,"proposed":204,"self":204},"Ours",[211,215,218,221,224,227,230,233,236,239,241,243,245,247,249,251,253,255,256,258,260,262,264,266,267,269,271,273,275,277],[212,212,212,213,214,212,214,214,212],0,1.78,-1,[212,216,216,217,214,212,214,214,212],1,3.24,[212,219,219,220,214,212,214,214,212],2,2.14,[212,222,222,223,214,212,214,214,212],3,0.97,[212,225,225,226,214,212,214,214,212],4,0.98,[212,228,228,229,214,212,214,214,212],5,3.86,[212,231,231,232,214,212,214,214,212],6,0.94,[212,234,234,235,214,212,214,214,212],7,4.8,[212,237,237,238,214,212,214,214,212],8,0.39,[212,148,148,240,214,212,214,214,212],3.28,[216,212,212,242,214,212,214,214,212],0.51,[216,216,216,244,214,212,214,214,212],0.23,[216,219,219,246,214,212,214,214,212],1.69,[216,222,222,248,214,212,214,214,212],1.45,[216,225,225,250,214,212,214,214,212],0.36,[216,228,228,252,214,212,214,214,212],1.57,[216,231,231,254,214,212,214,214,212],0.45,[216,234,234,222,214,212,214,214,212],[216,237,237,257,214,212,214,214,212],1.38,[216,148,148,259,214,212,214,214,212],4.52,[219,212,212,261,214,212,214,214,212],0.14,[219,216,216,263,214,212,214,214,212],0.12,[219,219,219,265,214,212,214,214,212],0.55,[219,222,222,265,214,212,214,214,212],[219,225,225,268,214,212,214,214,212],0.3,[219,228,228,270,214,212,214,214,212],0.21,[219,231,231,272,214,212,214,214,212],0.22,[219,234,234,274,214,212,214,214,212],0.77,[219,237,237,276,214,212,214,214,212],0.07,[219,148,148,278,214,212,214,214,212],0.29,[],[153],[],[],[284],"Wall-to-column distances in the SLAM map vs CAD drawing dimensions at 10 reference pairs; percentage error; map sizes omitted",{"slug":286,"group":287,"sourceId":5,"sourceLabel":6,"table":288,"selfRows":154,"metrics":289,"seqs":300,"entrants":313,"cells":317,"outcomes":371,"locators":372,"hardware":373,"wordings":374,"notes":375},"feng2026integratedslam-table-6","feng2026integratedslam:Table 6","Table 6",[290,291,292,293,294,295,296,297,298,299],{"label":157,"unit":158,"statistic":45,"alignment":45},{"label":160,"unit":158,"statistic":45,"alignment":45},{"label":162,"unit":158,"statistic":45,"alignment":45},{"label":164,"unit":158,"statistic":45,"alignment":45},{"label":166,"unit":158,"statistic":45,"alignment":45},{"label":168,"unit":158,"statistic":45,"alignment":45},{"label":170,"unit":158,"statistic":45,"alignment":45},{"label":172,"unit":158,"statistic":45,"alignment":45},{"label":174,"unit":158,"statistic":45,"alignment":45},{"label":176,"unit":158,"statistic":45,"alignment":45},[301,304,305,306,307,308,309,310,311,312],{"dataset":302,"sequence":180,"environment":303},"authors' real-site recording, hospital outpatient building (Xi'an)","active construction site of the same building in MEP installation and finishing stage, with workers, machinery, scaffolding",{"dataset":302,"sequence":183,"environment":303},{"dataset":302,"sequence":185,"environment":303},{"dataset":302,"sequence":187,"environment":303},{"dataset":302,"sequence":189,"environment":303},{"dataset":302,"sequence":191,"environment":303},{"dataset":302,"sequence":193,"environment":303},{"dataset":302,"sequence":195,"environment":303},{"dataset":302,"sequence":197,"environment":303},{"dataset":302,"sequence":199,"environment":303},[314,315,316],{"name":202,"methodId":203,"linkable":204,"proposed":75,"self":75},{"name":206,"methodId":207,"linkable":204,"proposed":75,"self":75},{"name":209,"methodId":5,"linkable":204,"proposed":204,"self":204},[318,320,321,323,325,327,329,331,333,335,337,339,341,343,345,347,349,351,353,354,356,358,359,360,362,364,366,367,369,370],[212,212,212,319,214,212,214,214,212],4.45,[212,216,216,319,214,212,214,214,212],[212,219,219,322,214,212,214,214,212],6.87,[212,222,222,324,214,212,214,214,212],2.12,[212,225,225,326,214,212,214,214,212],7.69,[212,228,228,328,214,212,214,214,212],5.93,[212,231,231,330,214,212,214,214,212],1.53,[212,234,234,332,214,212,214,214,212],7.58,[212,237,237,334,214,212,214,214,212],6.12,[212,148,148,336,214,212,214,214,212],5.35,[216,212,212,338,214,212,214,214,212],0.4,[216,216,216,340,214,212,214,214,212],0.69,[216,219,219,342,214,212,214,214,212],1.03,[216,222,222,344,214,212,214,214,212],0.48,[216,225,225,346,214,212,214,214,212],4.27,[216,228,228,348,214,212,214,214,212],4.15,[216,231,231,350,214,212,214,214,212],6.97,[216,234,234,352,214,212,214,214,212],0.92,[216,237,237,330,214,212,214,214,212],[216,148,148,355,214,212,214,214,212],2.87,[219,212,212,357,214,212,214,214,212],0.28,[219,216,216,268,214,212,214,214,212],[219,219,219,270,214,212,214,214,212],[219,222,222,361,214,212,214,214,212],0.24,[219,225,225,363,214,212,214,214,212],2.99,[219,228,228,365,214,212,214,214,212],1.87,[219,231,231,226,214,212,214,214,212],[219,234,234,368,214,212,214,214,212],0.13,[219,237,237,344,214,212,214,214,212],[219,148,148,238,214,212,214,214,212],[],[288],[],[],[376],"Wall-to-column distances in the SLAM map vs construction drawing dimensions at 10 reference pairs (drawings, not an independent survey); percentage error; map sizes omitted",{"slug":378,"group":379,"sourceId":5,"sourceLabel":6,"table":380,"selfRows":228,"metrics":381,"seqs":398,"entrants":401,"cells":405,"outcomes":436,"locators":437,"hardware":438,"wordings":439,"notes":440},"feng2026integratedslam-table-1","feng2026integratedslam:Table 1","Table 1",[382,386,389,392,395],{"label":383,"unit":384,"statistic":385,"alignment":45},"ATE (m) Max","m","max",{"label":387,"unit":384,"statistic":388,"alignment":45},"ATE (m) Mean","mean",{"label":390,"unit":384,"statistic":391,"alignment":45},"ATE (m) Median","median",{"label":393,"unit":384,"statistic":394,"alignment":45},"ATE (m) RMSE","RMSE",{"label":396,"unit":384,"statistic":397,"alignment":45},"ATE (m) STD","std",[399],{"dataset":179,"sequence":400,"environment":181},"simulation run, 1293 m in 1384 s, no loops in similar areas",[402,403,404],{"name":209,"methodId":5,"linkable":204,"proposed":204,"self":204},{"name":202,"methodId":203,"linkable":204,"proposed":75,"self":75},{"name":206,"methodId":207,"linkable":204,"proposed":75,"self":75},[406,408,410,412,414,416,418,420,422,424,426,428,430,432,434],[212,212,212,407,214,212,214,214,212],7.35,[212,216,212,409,214,212,214,214,212],3.48,[212,219,212,411,214,212,214,214,212],3.26,[212,222,212,413,214,212,214,214,212],5.4,[212,225,212,415,214,212,214,214,212],3.58,[216,212,212,417,214,212,214,214,212],47.21,[216,216,212,419,214,212,214,214,212],15.89,[216,219,212,421,214,212,214,214,212],10.35,[216,222,212,423,214,212,214,214,212],20.27,[216,225,212,425,214,212,214,214,212],12.57,[219,212,212,427,214,212,214,214,212],27.48,[219,216,212,429,214,212,214,214,212],9.09,[219,219,212,431,214,212,214,214,212],5.47,[219,222,212,433,214,212,214,214,212],12.33,[219,225,212,435,214,212,214,214,212],8.34,[],[380],[],[],[441],"EVO ATE vs Gazebo ground truth; LiDAR-only methods; Min column omitted",{"slug":443,"group":444,"sourceId":5,"sourceLabel":6,"table":445,"selfRows":228,"metrics":446,"seqs":452,"entrants":456,"cells":460,"outcomes":491,"locators":492,"hardware":493,"wordings":494,"notes":495},"feng2026integratedslam-table-4","feng2026integratedslam:Table 4","Table 4",[447,448,449,450,451],{"label":383,"unit":384,"statistic":385,"alignment":45},{"label":387,"unit":384,"statistic":388,"alignment":45},{"label":390,"unit":384,"statistic":391,"alignment":45},{"label":393,"unit":384,"statistic":394,"alignment":45},{"label":396,"unit":384,"statistic":397,"alignment":45},[453],{"dataset":179,"sequence":454,"environment":455},"simulation run, 1293 m in 1384 s, no loops in similar areas, with dynamic objects","Gazebo model of a large public building floor (292 m x 142 m x 6 m), long windowless corridors, dynamic objects added",[457,458,459],{"name":209,"methodId":5,"linkable":204,"proposed":204,"self":204},{"name":202,"methodId":203,"linkable":204,"proposed":75,"self":75},{"name":206,"methodId":207,"linkable":204,"proposed":75,"self":75},[461,463,465,467,469,471,473,475,477,479,481,483,485,487,489],[212,212,212,462,214,212,214,214,212],8.47,[212,216,212,464,214,212,214,214,212],3.83,[212,219,212,466,214,212,214,214,212],3.59,[212,222,212,468,214,212,214,214,212],5.94,[212,225,212,470,214,212,214,214,212],3.94,[216,212,212,472,214,212,214,214,212],58.92,[216,216,212,474,214,212,214,214,212],19.33,[216,219,212,476,214,212,214,214,212],12.84,[216,222,212,478,214,212,214,214,212],25.13,[216,225,212,480,214,212,214,214,212],15.22,[219,212,212,482,214,212,214,214,212],34.48,[219,216,212,484,214,212,214,214,212],10.9,[219,219,212,486,214,212,214,214,212],6.68,[219,222,212,488,214,212,214,214,212],14.79,[219,225,212,490,214,212,214,214,212],9.98,[],[445],[],[],[496],"EVO ATE in simulation with moving objects (worker cylinder 0.80 m\u002Fs, trolley 0.50 m\u002Fs, lifting platform 0.20 m\u002Fs); Min omitted",[498,503,508,513,518],{"group":499,"slug":500,"sourceLabel":6,"table":501,"selfRows":228,"datasets":502},"feng2026integratedslam:Table 5","feng2026integratedslam-table-5","Table 5",[302],{"group":504,"slug":505,"sourceLabel":6,"table":506,"selfRows":225,"datasets":507},"feng2026integratedslam:Table 8","feng2026integratedslam-table-8","Table 8",[302],{"group":509,"slug":510,"sourceLabel":6,"table":511,"selfRows":222,"datasets":512},"feng2026integratedslam:Table 3","feng2026integratedslam-table-3","Table 3",[179],{"group":514,"slug":515,"sourceLabel":6,"table":516,"selfRows":222,"datasets":517},"feng2026integratedslam:Table 7","feng2026integratedslam-table-7","Table 7",[302],{"group":519,"slug":520,"sourceLabel":6,"table":521,"selfRows":222,"datasets":522},"feng2026integratedslam:Text Sec. 5.2.4","feng2026integratedslam-text-sec-5-2-4","Text Sec. 5.2.4",[302],1790510661490]