[{"data":1,"prerenderedAt":609},["ShallowReactive",2],{"method-iscloam2020":3},{"method":4,"reference":58,"equipment":80,"figures":122,"results":123},{"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":23,"limitations":28,"sensors":33,"platform":36,"estimator":39,"association":40,"timeModel":41,"deskew":42,"loopClosure":43,"globalOptimization":44,"mapRepresentation":42,"prior":45,"outputGeometry":46,"compute":47,"codeUrl":48,"codeLicense":49,"relatedVersions":50},"iscloam2020","Wang et al., 2020","ISC-LOAM (Intensity Scan Context)","Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection",2020,"recent","C04","full_slam_with_global_correction","強度掃描脈絡（Intensity Scan Context，ISC）是一種同時編碼幾何與 LiDAR 強度的全域描述子：先以距離校正強度，再把 50 m 內的點依方位角與半徑分格，每格保留最大強度，形成一張二維矩陣。檢索分兩階段，先用二值化佔用矩陣做 XOR 比對並估計欄位平移，以處理反向重訪；再以餘弦相似度比對強度結構，最後以時間一致性與 FPFH 加 ICP 的幾何檢查確認迴圈。論文本身只提出迴圈偵測；官方 ISC-LOAM 程式碼再把它接上 LOAM 系列前端與後端最佳化，成為完整的 LiDAR SLAM。","Global LiDAR descriptor that bins calibrated intensity by ring and sector (Intensity Scan Context), retrieved in two stages (binary XOR geometry check with column shift, then intensity cosine similarity) and verified by temporal consistency and FPFH plus ICP; the official ISC-LOAM repository combines it with a LOAM-family front end into a full LiDAR SLAM.","full_text_reviewed","peer_reviewed_published","background","論文本身只在倉庫機器人與 KITTI 上測試。施工相關證據來自其他研究：在建築工地的實測評估中，ISC-LOAM 被當成完整 SLAM 基準並報告 APE [feng2025_construction_lidar_eval]；在基礎設施非破壞檢測的回顧中，作者回報它無法完成軌跡估計 [ghadimzadeh2025slamnde]。強度描述子在工地反覆變化的材料與遮蔽下是否穩定，論文沒有驗證。",[20,21,22],"public_benchmark","controlled_experiment","completed_building",[24,25,26,27],"On KITTI 00, 02 and 05 its recall is higher than that of every geometry-only LiDAR descriptor compared (Scan Context, GLAROT3D, Cieslewski et al.) and its precision is higher or equal (100% versus 100% for Scan Context on 00 and 05); the text claims higher precision and recall on all three sequences (Table II; Sec. IV-C)","Handles reverse revisits on KITTI 02 (recall 91% versus 80.6% for DBoW2 and 73% for Scan Context) (Table II)","1.2 ms per query on average (Sec. IV-C; Sec. V)","In the warehouse test it detected a reverse-direction revisit that the DBoW2 visual baseline missed (Sec. IV-B; Fig. 5)",[29,30,31,32],"False positives occur in non-residential stretches with trees on both sides, where geometry and intensity cues are limited (Sec. IV-C)","Intensity must be calibrated with a distance mapping collected in offline experiments (Sec. III-A)","Results of the compared LiDAR descriptors were copied from their papers rather than rerun (Sec. IV-C)","The warehouse experiment is qualitative only (Sec. IV-B; Fig. 5)",[34,35],"3D LiDAR with intensity (Velodyne VLP-16 on the warehouse AGV; Velodyne HDL-64E in KITTI)","wheel odometry fused with LiDAR odometry for the front-end trajectory in the warehouse test (Sec. IV-B)",[37,38],"wheeled UGV (warehouse AGV)","vehicle (KITTI)","paper: loop candidates verified by FPFH-based initial alignment followed by ICP (Sec. III-D); the pose-graph back end is not described in the paper. Repository: front end based on LOAM, A-LOAM and F-LOAM and back end based on ISC, with Ceres and GTSAM listed as dependencies (README; LICENSE)","global descriptor: points within Lmax = 50 m binned into 20 sectors and 60 rings (Table I) with the maximum calibrated intensity per bin; two-stage retrieval: XOR-based binary geometry similarity over column shifts, then column-wise cosine similarity of intensity (Sec. III-B; Sec. III-C)","not described in the paper (descriptor only)","not described in the paper","Intensity Scan Context descriptor with two-stage hierarchical re-identification, temporal consistency over N = 5 neighbouring frames, and FPFH plus ICP geometric verification (Sec. III-C; Sec. III-D; Table I)","not described in the paper; the repository lists GTSAM as a dependency, which implies factor-graph optimization (inference from README)","none; intensity calibration needs a distance-to-intensity mapping collected offline (Sec. III-A)","loop-closure pairs; corrected trajectory and map shown qualitatively for the warehouse test (Fig. 5)","1.2 ms per query on average; binary geometry matching 0.5 ms on a desktop computer; implemented in C++ with ROS on an Intel NUC mini computer (Sec. I; Sec. III-C; Sec. IV-A; Sec. IV-C). The repository README states 20 Hz for the full SLAM (not verified in the paper)","https:\u002F\u002Fgithub.com\u002Fwh200720041\u002Fiscloam","BSD 3-clause style terms in the repository LICENSE file, which also carries LOAM, A-LOAM and F-LOAM notices (LICENSE read; GitHub license metadata not queried because the API was rate limited)",[51,55],{"relation":52,"title":53,"doi_or_url":54},"preprint","Intensity Scan Context (arXiv v1)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2003.05656",{"relation":56,"title":57,"doi_or_url":48},"code_release","wh200720041\u002Fiscloam: Intensity Scan Context based full SLAM implementation (ISC-LOAM)",{"id":5,"kind":59,"shortName":7,"title":8,"authors":60,"year":9,"venue":64,"venueType":65,"publisher":66,"volumeIssuePages":67,"doi":68,"arxivId":69,"url":70,"firstPublicDate":71,"publicationStatus":16,"metadataStatus":72,"fulltextStatus":15,"era":10,"classicReason":73,"codeUrl":48,"cluster":11,"topics":74,"mdpi":75,"verification":76,"label":6,"fulltextRoute":77,"versionRead":78,"addedByCensus":79},"method",[61,62,63],"Han Wang","Chen Wang","Lihua Xie","2020 IEEE International Conference on Robotics and Automation (ICRA)","conference","IEEE","pp. 2095-2101","10.1109\u002Ficra40945.2020.9196764","2003.05656","https:\u002F\u002Fdoi.org\u002F10.1109\u002FICRA40945.2020.9196764","2020-03-12","metadata_verified","not_applicable",[11],false,"corrected","arXiv","arXiv v1 (2020-03-12), the only arXiv version, accepted ICRA 2020 manuscript; IEEE version of record not read",true,[81,88,95,100,105,111,117],{"category":82,"model":83,"canonical":83,"role":84,"dataset":85,"specs":86,"locator":87},"lidar","Velodyne VLP-16","method input",null,"on the warehouse AGV; intensity rescaled to [0, 1]","Sec. III-A; Sec. IV-A",{"category":89,"model":90,"canonical":91,"role":92,"dataset":85,"specs":93,"locator":94},"rgbd","Intel Realsense r200","Intel RealSense R200","compared device","on the same AGV; the DBoW2 baseline used a front-mounted camera (that this is the R200 is an inference)","Sec. IV-A; Sec. IV-B",{"category":96,"model":97,"canonical":97,"role":84,"dataset":85,"specs":98,"locator":99},"wheel_or_leg_odometry","AGV wheel odometer (not described)","fused with PCL feature-based LiDAR odometry for the front-end trajectory","Sec. IV-B",{"category":101,"model":102,"canonical":102,"role":84,"dataset":85,"specs":103,"locator":104},"platform","autonomous guided vehicle for warehouse manipulation","maximum speed 1 m\u002Fs","Sec. IV-A; Fig. 4",{"category":106,"model":107,"canonical":107,"role":108,"dataset":85,"specs":109,"locator":110},"compute","Intel NUC mini computer (model not reported)","compute for runtime","C++ implementation integrated in ROS","Sec. IV-A",{"category":82,"model":112,"canonical":112,"role":113,"dataset":114,"specs":115,"locator":116},"Velodyne HDL-64E","dataset sensor","KITTI","on the KITTI car","Sec. IV-C",{"category":118,"model":119,"canonical":119,"role":120,"dataset":114,"specs":121,"locator":116},"gnss","GPS (KITTI; model not reported)","reference or ground truth","used to count the total number of loop closures",[],{"totalRows":124,"groupCount":125,"groups":126,"others":603},32,5,[127,362,464,541],{"slug":128,"group":129,"sourceId":130,"sourceLabel":131,"table":132,"selfRows":133,"metrics":134,"seqs":144,"entrants":171,"cells":190,"outcomes":356,"locators":357,"hardware":358,"wordings":359,"notes":360},"saloam2021-table-ii","saloam2021:Table II","saloam2021","Li et al., 2021a","Table II",13,[135,139,141],{"label":136,"unit":137,"statistic":138,"alignment":73},"relative translational error (%)","%","mean",{"label":140,"unit":137,"statistic":138,"alignment":73},"relative translational error (%), Average",{"label":142,"unit":143,"statistic":138,"alignment":73},"relative rotational error (deg\u002F100m), Average","deg\u002F100m",[145,149,151,153,155,157,159,161,163,165,167,169],{"dataset":146,"sequence":147,"environment":148},"KITTI odometry","00*","vehicle, road",{"dataset":146,"sequence":150,"environment":148},"01",{"dataset":146,"sequence":152,"environment":148},"02*",{"dataset":146,"sequence":154,"environment":148},"03",{"dataset":146,"sequence":156,"environment":148},"04",{"dataset":146,"sequence":158,"environment":148},"05*",{"dataset":146,"sequence":160,"environment":148},"06*",{"dataset":146,"sequence":162,"environment":148},"07*",{"dataset":146,"sequence":164,"environment":148},"08*",{"dataset":146,"sequence":166,"environment":148},"09*",{"dataset":146,"sequence":168,"environment":148},"10",{"dataset":146,"sequence":170,"environment":148},"Average 00-10",[172,175,178,180,183,186,188],{"name":173,"methodId":174,"linkable":79,"proposed":75,"self":75},"LOAM* (from [19])","loam2017_auro",{"name":176,"methodId":177,"linkable":79,"proposed":75,"self":75},"FLOAM","floam2021",{"name":179,"methodId":5,"linkable":79,"proposed":75,"self":79},"ISC-LOAM",{"name":181,"methodId":182,"linkable":79,"proposed":75,"self":75},"SUMA","suma2018",{"name":184,"methodId":185,"linkable":79,"proposed":75,"self":75},"SUMA++","sumapp2019",{"name":187,"methodId":130,"linkable":79,"proposed":79,"self":75},"Ours-ODOM",{"name":189,"methodId":130,"linkable":79,"proposed":79,"self":75},"Ours-LOOP",[191,195,198,201,204,207,209,212,215,218,221,224,227,228,230,232,234,235,236,238,240,242,244,246,248,250,252,254,256,258,260,262,264,266,268,270,272,274,276,277,279,281,283,284,285,287,289,291,292,294,296,298,299,301,303,305,307,309,310,312,314,316,317,319,321,323,325,326,328,329,331,332,334,336,338,339,340,342,343,344,345,346,347,349,350,351,352,353,354,355],[192,192,192,193,194,192,194,194,192],0,0.78,-1,[192,192,196,197,194,192,194,194,192],1,1.43,[192,192,199,200,194,192,194,194,192],2,0.92,[192,192,202,203,194,192,194,194,192],3,0.86,[192,192,205,206,194,192,194,194,192],4,0.71,[192,192,125,208,194,192,194,194,192],0.57,[192,192,210,211,194,192,194,194,192],6,0.65,[192,192,213,214,194,192,194,194,192],7,0.63,[192,192,216,217,194,192,194,194,192],8,1.12,[192,192,219,220,194,192,194,194,192],9,0.77,[192,192,222,223,194,192,194,194,192],10,0.79,[192,196,225,226,194,192,194,194,192],11,0.84,[196,192,192,200,194,192,194,194,192],[196,192,196,229,194,192,194,194,192],2.8,[196,192,199,231,194,192,194,194,192],1.56,[196,192,202,233,194,192,194,194,192],1.09,[196,192,205,197,194,192,194,194,192],[196,192,125,223,194,192,194,194,192],[196,192,210,237,194,192,194,194,192],0.72,[196,192,213,239,194,192,194,194,192],0.54,[196,192,216,241,194,192,194,194,192],1.11,[196,192,219,243,194,192,194,194,192],1.28,[196,192,222,245,194,192,194,194,192],1.77,[196,196,225,247,194,192,194,194,192],1.27,[196,199,225,249,194,192,194,194,192],0.49,[199,192,192,251,194,192,194,194,192],1.02,[199,192,196,253,194,192,194,194,192],2.92,[199,192,199,255,194,192,194,194,192],1.67,[199,192,202,257,194,192,194,194,192],1.15,[199,192,205,259,194,192,194,194,192],1.5,[199,192,125,261,194,192,194,194,192],0.81,[199,192,210,263,194,192,194,194,192],0.76,[199,192,213,265,194,192,194,194,192],0.56,[199,192,216,267,194,192,194,194,192],1.2,[199,192,219,269,194,192,194,194,192],1.4,[199,192,222,271,194,192,194,194,192],1.87,[199,196,225,273,194,192,194,194,192],1.35,[199,199,225,275,194,192,194,194,192],0.52,[202,192,192,220,194,192,194,194,192],[202,192,196,278,194,192,194,194,192],11.15,[202,192,199,280,194,192,194,194,192],2.93,[202,192,202,282,194,192,194,194,192],1.25,[202,192,205,203,194,192,194,194,192],[202,192,125,265,194,192,194,194,192],[202,192,210,286,194,192,194,194,192],0.64,[202,192,213,288,194,192,194,194,192],0.47,[202,192,216,290,194,192,194,194,192],1.06,[202,192,219,223,194,192,194,194,192],[202,192,222,293,194,192,194,194,192],0.99,[202,196,225,295,194,192,194,194,192],1.95,[202,199,225,297,194,192,194,194,192],0.48,[205,192,192,211,194,192,194,194,192],[205,192,196,300,194,192,194,194,192],1.63,[205,192,199,302,194,192,194,194,192],3.54,[205,192,202,304,194,192,194,194,192],0.67,[205,192,205,306,194,192,194,194,192],0.34,[205,192,125,308,194,192,194,194,192],0.4,[205,192,210,288,194,192,194,194,192],[205,192,213,311,194,192,194,194,192],0.39,[205,192,216,313,194,192,194,194,192],1.01,[205,192,219,315,194,192,194,194,192],0.58,[205,192,222,304,194,192,194,194,192],[205,196,225,318,194,192,194,194,192],0.94,[205,199,225,320,194,192,194,194,192],0.28,[125,192,192,322,194,192,194,194,192],0.59,[125,192,196,324,194,192,194,194,192],1.89,[125,192,199,220,194,192,194,194,192],[125,192,202,327,194,192,194,194,192],0.87,[125,192,205,322,194,192,194,194,192],[125,192,125,330,194,192,194,194,192],0.45,[125,192,210,275,194,192,194,194,192],[125,192,213,333,194,192,194,194,192],0.41,[125,192,216,335,194,192,194,194,192],0.85,[125,192,219,337,194,192,194,194,192],0.68,[125,192,222,193,194,192,194,194,192],[125,196,225,263,194,192,194,194,192],[125,199,225,341,194,192,194,194,192],0.31,[210,192,192,322,194,192,194,194,192],[210,192,196,324,194,192,194,194,192],[210,192,199,223,194,192,194,194,192],[210,192,202,327,194,192,194,194,192],[210,192,205,322,194,192,194,194,192],[210,192,125,348,194,192,194,194,192],0.37,[210,192,210,275,194,192,194,194,192],[210,192,213,333,194,192,194,194,192],[210,192,216,226,194,192,194,194,192],[210,192,219,220,194,192,194,194,192],[210,192,222,193,194,192,194,194,192],[210,196,225,263,194,192,194,194,192],[210,199,225,320,194,192,194,194,192],[],[132],[],[],[361],"KITTI odometry 00-10; mean relative pose error over 100-800 m trajectories (rotation deg\u002F100m \u002F translation %); * marks sequences with loops; LOAM values quoted from its journal paper [19]; other baselines run with open-source code",{"slug":363,"group":364,"sourceId":130,"sourceLabel":131,"table":365,"selfRows":216,"metrics":366,"seqs":371,"entrants":381,"cells":387,"outcomes":458,"locators":459,"hardware":460,"wordings":461,"notes":462},"saloam2021-table-iii","saloam2021:Table III","Table III",[367],{"label":368,"unit":369,"statistic":370,"alignment":370},"Absolute Trajectory Error (m)","m","not_reported",[372,373,374,375,376,377,378,379],{"dataset":146,"sequence":147,"environment":148},{"dataset":146,"sequence":152,"environment":148},{"dataset":146,"sequence":158,"environment":148},{"dataset":146,"sequence":160,"environment":148},{"dataset":146,"sequence":162,"environment":148},{"dataset":146,"sequence":164,"environment":148},{"dataset":146,"sequence":166,"environment":148},{"dataset":146,"sequence":380,"environment":148},"Average",[382,383,384,385,386],{"name":179,"methodId":5,"linkable":79,"proposed":75,"self":79},{"name":181,"methodId":182,"linkable":79,"proposed":75,"self":75},{"name":184,"methodId":185,"linkable":79,"proposed":75,"self":75},{"name":187,"methodId":130,"linkable":79,"proposed":79,"self":75},{"name":189,"methodId":130,"linkable":79,"proposed":79,"self":75},[388,390,392,394,396,397,399,401,403,405,407,408,409,410,412,414,416,418,420,421,422,423,425,427,429,431,433,435,437,439,441,443,445,446,448,450,451,453,455,456],[192,192,192,389,194,192,194,194,192],1.6,[192,192,196,391,194,192,194,194,192],4.77,[192,192,199,393,194,192,194,194,192],2.49,[192,192,202,395,194,192,194,194,192],1.03,[192,192,205,265,194,192,194,194,192],[192,192,125,398,194,192,194,194,192],4.88,[192,192,210,400,194,192,194,194,192],2.31,[192,192,213,402,194,192,194,194,192],2.52,[196,192,192,404,194,192,194,194,192],1.14,[196,192,196,406,194,192,194,194,192],44.13,[196,192,199,203,194,192,194,194,192],[196,192,202,337,194,192,194,194,192],[196,192,205,308,194,192,194,194,192],[196,192,125,411,194,192,194,194,192],2.09,[196,192,210,413,194,192,194,194,192],3.88,[196,192,213,415,194,192,194,194,192],7.59,[199,192,192,417,194,192,194,194,192],1.17,[199,192,196,419,194,192,194,194,192],12.99,[199,192,199,286,194,192,194,194,192],[199,192,202,265,194,192,194,194,192],[199,192,205,348,194,192,194,194,192],[199,192,125,424,194,192,194,194,192],2.44,[199,192,210,426,194,192,194,194,192],1.19,[199,192,213,428,194,192,194,194,192],2.76,[202,192,192,430,194,192,194,194,192],5.14,[202,192,196,432,194,192,194,194,192],9.71,[202,192,199,434,194,192,194,194,192],3.04,[202,192,202,436,194,192,194,194,192],0.69,[202,192,205,438,194,192,194,194,192],0.53,[202,192,125,440,194,192,194,194,192],3.61,[202,192,210,442,194,192,194,194,192],1.74,[202,192,213,444,194,192,194,194,192],3.49,[205,192,192,293,194,192,194,194,192],[205,192,196,447,194,192,194,194,192],9.24,[205,192,199,449,194,192,194,194,192],0.75,[205,192,202,286,194,192,194,194,192],[205,192,205,452,194,192,194,194,192],0.36,[205,192,125,454,194,192,194,194,192],3.24,[205,192,210,267,194,192,194,194,192],[205,192,213,457,194,192,194,194,192],2.34,[],[365],[],[],[463],"KITTI sequences with loops; absolute trajectory error (m); statistic and alignment not stated",{"slug":465,"group":466,"sourceId":5,"sourceLabel":6,"table":132,"selfRows":210,"metrics":467,"seqs":472,"entrants":480,"cells":492,"outcomes":535,"locators":536,"hardware":537,"wordings":538,"notes":539},"iscloam2020-table-ii","iscloam2020:Table II",[468,470],{"label":469,"unit":137,"statistic":370,"alignment":73},"Precision (%)",{"label":471,"unit":137,"statistic":370,"alignment":73},"Recall Rate (%)",[473,476,478],{"dataset":146,"sequence":474,"environment":475},"sequence 00","vehicle, urban and residential",{"dataset":146,"sequence":477,"environment":475},"sequence 02",{"dataset":146,"sequence":479,"environment":475},"sequence 05",[481,484,486,488,490],{"name":482,"methodId":483,"linkable":79,"proposed":75,"self":75},"Kim [21] (Scan Context)","scancontext2018",{"name":485,"methodId":85,"linkable":75,"proposed":75,"self":75},"GLAROT3D [17]",{"name":487,"methodId":85,"linkable":75,"proposed":75,"self":75},"Cieslewski [24]",{"name":489,"methodId":85,"linkable":75,"proposed":75,"self":75},"Galvez-Lopez [10] (DBoW2)",{"name":491,"methodId":5,"linkable":79,"proposed":79,"self":79},"Proposed (ISC)",[493,495,497,499,501,503,505,506,507,508,510,512,514,515,517,519,521,522,523,525,527,528,529,530,532,533],[192,192,192,494,194,192,194,194,192],100,[192,196,192,496,194,192,194,194,192],87,[196,192,192,498,194,192,194,194,192],86,[196,196,192,500,194,192,194,194,192],40,[199,192,192,502,194,192,194,194,192],92,[199,196,192,504,194,192,194,194,192],80,[202,192,192,494,194,192,194,194,192],[202,196,192,502,194,192,194,194,192],[205,192,192,494,194,192,194,194,192],[205,196,192,509,194,192,194,194,192],90.2,[192,192,196,511,194,192,194,194,192],90,[192,196,196,513,194,192,194,194,192],73,[202,192,196,494,194,192,194,194,192],[202,196,196,516,194,192,194,194,192],80.6,[205,192,196,518,194,192,194,194,192],98,[205,196,196,520,194,192,194,194,192],91,[192,192,199,494,194,192,194,194,192],[192,196,199,511,194,192,194,194,192],[199,192,199,524,194,192,194,194,192],93,[199,196,199,526,194,192,194,194,192],60,[196,192,199,504,194,192,194,194,192],[196,196,199,504,194,192,194,194,192],[202,192,199,494,194,192,194,194,192],[202,196,199,531,194,192,194,194,192],87.6,[205,192,199,494,194,192,194,194,192],[205,196,199,534,194,192,194,194,192],91.2,[],[132],[],[],[540],"KITTI sequences 00, 02 (forward and reverse revisits), 05; loop-closure precision and recall (%); Scan Context, GLAROT3D and Cieslewski results copied from their papers, DBoW2 run by the authors; loop ground truth from GPS",{"slug":542,"group":543,"sourceId":130,"sourceLabel":131,"table":544,"selfRows":202,"metrics":545,"seqs":547,"entrants":555,"cells":562,"outcomes":597,"locators":598,"hardware":599,"wordings":600,"notes":601},"saloam2021-table-iv","saloam2021:Table IV","Table IV",[546],{"label":368,"unit":369,"statistic":370,"alignment":370},[548,552,554],{"dataset":549,"sequence":550,"environment":551},"Ford Campus Vision and Lidar Dataset","Seq01","vehicle, campus (unseen data)",{"dataset":549,"sequence":553,"environment":551},"Seq02",{"dataset":549,"sequence":380,"environment":551},[556,557,558,559,560,561],{"name":176,"methodId":177,"linkable":79,"proposed":75,"self":75},{"name":179,"methodId":5,"linkable":79,"proposed":75,"self":79},{"name":181,"methodId":182,"linkable":79,"proposed":75,"self":75},{"name":184,"methodId":185,"linkable":79,"proposed":75,"self":75},{"name":187,"methodId":130,"linkable":79,"proposed":79,"self":75},{"name":189,"methodId":130,"linkable":79,"proposed":79,"self":75},[563,565,567,569,571,573,575,577,579,581,583,585,587,588,590,592,593,595],[192,192,192,564,194,192,194,194,192],1.61,[192,192,196,566,194,192,194,194,192],7.63,[192,192,199,568,194,192,194,194,192],4.62,[196,192,192,570,194,192,194,194,192],2.3,[196,192,196,572,194,192,194,194,192],10.38,[196,192,199,574,194,192,194,194,192],6.34,[199,192,192,576,194,192,194,194,192],4.45,[199,192,196,578,194,192,194,194,192],75.65,[199,192,199,580,194,192,194,194,192],40.05,[202,192,192,582,194,192,194,194,192],4.22,[202,192,196,584,194,192,194,194,192],63.27,[202,192,199,586,194,192,194,194,192],33.75,[205,192,192,273,194,192,194,194,192],[205,192,196,589,194,192,194,194,192],9.02,[205,192,199,591,194,192,194,194,192],5.18,[125,192,192,273,194,192,194,194,192],[125,192,196,594,194,192,194,194,192],6.97,[125,192,199,596,194,192,194,194,192],4.16,[],[544],[],[],[602],"Ford Campus Vision and Lidar Dataset, models and parameters tuned on KITTI only; absolute trajectory error (m); statistic and alignment not stated",[604],{"group":605,"slug":606,"sourceLabel":6,"table":607,"selfRows":199,"datasets":608},"iscloam2020:Text Sec. IV-C","iscloam2020-text-sec-iv-c","Text Sec. IV-C",[146,370],1790510661515]