[{"data":1,"prerenderedAt":940},["ShallowReactive",2],{"method-cartographer2016":3},{"method":4,"reference":59,"equipment":81,"figures":118,"results":119},{"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":36,"platform":40,"estimator":43,"association":44,"timeModel":45,"deskew":46,"loopClosure":47,"globalOptimization":48,"mapRepresentation":49,"prior":50,"outputGeometry":51,"compute":52,"codeUrl":53,"codeLicense":54,"relatedVersions":55},"cartographer2016","Hess et al., 2016","Cartographer","Real-time loop closure in 2D LIDAR SLAM",2016,"classic","C01","full_slam_with_global_correction","Cartographer 以背包式平台即時產生竣工平面圖：局部端把連續掃描以非線性最佳化對齊到小型子地圖（submap），誤差隨時間累積；全域端把已完成的子地圖與所有掃描做迴圈候選，以分支定界（branch-and-bound）加速的逐像素掃描匹配產生迴圈約束，再以稀疏位姿調整（SPA）定期最佳化。作者的貢獻在於降低迴圈約束計算成本，使數萬平方公尺樓層也能即時完成最佳化。","Submap-based 2D LiDAR SLAM whose branch-and-bound scan-to-submap matching makes loop-closure constraint search fast enough for real-time pose optimization on a backpack.","full_text_reviewed","peer_reviewed_published","background","作者明確以竣工平面圖（as-built floor plans）與建築管理為應用動機，並以背包系統在德意志博物館（既有建築）實測（Sec. VI.A，無獨立幾何參考）。另以人推手推車上的低價 Revo LDS 建成 5 cm 平面圖，與雷射捲尺量得的 5 條直線長度比對（Sec. VI.B, Table I）；文中未說明此實驗地點。均為既有建築 2D 平面圖，不是施工中工地，也不是 3D 點雲品質驗證。",[20,21,22],"completed_building","public_benchmark","independent_reference",[24,25,26,27],"Real-time loop closure at 5 cm resolution on modest hardware (abstract; Sec. VII).","Tuning reported as needed only for the sensor configuration, not for the specific surroundings (Sec. VI.C).","Loop-closure precision of 93.4% to 99.8% on five of the six Radish datasets, with enough constraints in all cases and Huber loss in SPA helping robustness to false positives (Sec. VI.C, Table IV).","Branch-and-bound search is exact: it returns the same match as the naive exhaustive search when inner-node scores are upper bounds (Sec. V.B).",[29,30,31,32,33,34,35],"Local scan matching accumulates error that is removed only by the global loop-closure optimization (Sec. III; Sec. IV).","Loop-closure matching must keep pace with incoming scans (soft real-time constraint) (Sec. III).","(inference) Geometric validation in the paper is five line lengths on one floor plan and relative-pose benchmarks, not 3D point-cloud accuracy.","On the Radish benchmarks, the authors report considerably worse results than Graph Mapping on MIT CSAIL and state they cannot be sure that parameters were not fitted to the specific locations of the public datasets (Sec. VI.C, Table II).","Scan-to-submap matching produces false-positive loop constraints; on Freiburg hospital, low resolution and a low minimum score gave 77.3% precision, and raising the score lowers some ground-truth metrics (Sec. VI.C, Table IV).","The submap-scan loop constraints have no ground truth; precision is defined by constraints not violated by more than 20 cm or 1 deg after SPA (Sec. VI.C).","Incorrect constraints can arise in locally symmetric environments such as office cubicles (Sec. V.A).",[37,38,39],"horizontally mounted 2D LIDAR on the backpack (model not reported)","IMU on the backpack (model not reported)","Neato Robotics Revo LDS low-cost laser distance sensor (second experiment)",[41,42],"backpack","manually pushed trolley carrying a vacuum-cleaner Revo LDS","nonlinear least squares (Ceres) for scan-to-submap matching and sparse pose adjustment for global optimization","scan-to-submap matching on probability grids; branch-and-bound pixel-accurate search for loop-closure constraints","discrete poses","not_reported (IMU is used to project scans to the horizontal plane on the unstable backpack)","all finished submaps and scans considered; branch-and-bound scan matching in a search window adds loop constraints in real time","sparse pose adjustment over scan and submap poses every few seconds","2D probability-grid submaps (5 cm resolution in the paper)","none","2D grid floor plan (5 cm resolution) and optimized poses","Deutsches Museum data (1,913 s, 2,253 m): 1,018 s CPU, up to 2.2 GB and up to 4 background loop-closure threads, 360 s wall clock (5.3x real time) on an Intel Xeon E5-1650 at 3.2 GHz (Sec. VI.A); Radish datasets processed in 10 to 190 s wall clock for 424 to 7,678 s of data on the same workstation, with parameters not tuned for CPU performance (Sec. VI.C, Table V)","https:\u002F\u002Fgithub.com\u002Fcartographer-project\u002Fcartographer","Apache-2.0",[56],{"relation":57,"title":58,"doi_or_url":53},"code_release","Cartographer (cartographer-project); the open-source code also contains 3D SLAM, which this 2D paper does not describe",{"id":5,"kind":60,"shortName":7,"title":8,"authors":61,"year":9,"venue":66,"venueType":67,"publisher":68,"volumeIssuePages":69,"doi":70,"arxivId":71,"url":72,"firstPublicDate":73,"publicationStatus":16,"metadataStatus":74,"fulltextStatus":15,"era":10,"classicReason":75,"codeUrl":53,"cluster":11,"topics":76,"mdpi":77,"verification":78,"label":6,"fulltextRoute":79,"versionRead":80,"addedByCensus":77},"method",[62,63,64,65],"Wolfgang Hess","Damon Kohler","Holger Rapp","Daniel Andor","2016 IEEE International Conference on Robotics and Automation (ICRA)","conference","IEEE","pp. 1271-1278","10.1109\u002Ficra.2016.7487258",null,"https:\u002F\u002Fresearch.google.com\u002Fpubs\u002Farchive\u002F45466.pdf","2016-05","metadata_verified","reproducible baseline and necessary technical node: submap-based local SLAM plus branch-and-bound scan-to-submap loop closure with sparse pose adjustment, in an Apache-2.0 open-source system whose code also contains a 3D mode not described in this paper (wide use is a reviewer judgement not evidenced here).",[11],false,"corrected","author copy","Google-hosted camera-ready copy (pdfTeX, created 2016-02-11, 8 pp.) read in full; compared with the IEEE Xplore version of record (arnumber 7487258, ICRA 2016 pp. 1271-1278) fetched through NTU access. The extractor could not extract the VoR page with Sec. VI.A to VI.B and Tables I to III; verifier cross-check (2026-09-25): the complete VoR text layer was extracted with PyMuPDF and Tables I to V and every Sec. VI.A number are identical to the Google copy.",[82,88,93,98,103,106,112],{"category":83,"model":84,"canonical":84,"role":85,"dataset":71,"specs":86,"locator":87},"lidar","Revo LDS","method input","laser distance sensor used in Neato vacuum cleaners, costs under $30; scans taken at approximately 2 Hz over its debug connection","Sec. VI.B",{"category":83,"model":89,"canonical":90,"role":85,"dataset":71,"specs":91,"locator":92},"horizontally mounted LIDAR on the Cartographer backpack (model not reported)","Livox Horizon","provides the laser scans matched to 2D submaps; on the unstable backpack its scans are projected into the 2D world using the IMU gravity estimate; model, range and scan rate not reported","Sec. III, IV, VI",{"category":94,"model":95,"canonical":95,"role":85,"dataset":71,"specs":96,"locator":97},"imu","IMU on the Cartographer backpack (model not reported)","used to estimate the orientation of gravity for projecting scans from the horizontally mounted LIDAR; model and rate not reported","Sec. IV",{"category":99,"model":100,"canonical":100,"role":85,"dataset":71,"specs":101,"locator":102},"platform","Cartographer backpack","sensor-equipped backpack with a horizontally mounted LIDAR and an IMU used to estimate gravity for projecting scans; sensor models not reported; 2D grid maps at 5 cm resolution","Sec. III, IV",{"category":99,"model":104,"canonical":104,"role":85,"dataset":71,"specs":105,"locator":87},"vacuum cleaner pushed on a trolley","Revo LDS data captured by pushing the vacuum cleaner around on a trolley",{"category":107,"model":108,"canonical":108,"role":109,"dataset":71,"specs":110,"locator":111},"compute","Intel Xeon E5-1650","compute for runtime","3.2 GHz, in a workstation used for all timing; up to 4 background threads for loop-closure scan matching on the Deutsches Museum data","Sec. VI.A, VI.C",{"category":113,"model":114,"canonical":114,"role":115,"dataset":71,"specs":116,"locator":117},"other","laser tape measure","reference or ground truth","used for five reference straight-line lengths compared with the Revo LDS floor plan","Sec. VI.B, Table I",[],{"totalRows":120,"groupCount":121,"groups":122,"others":848},199,21,[123,251,416,590],{"slug":124,"group":125,"sourceId":126,"sourceLabel":127,"table":128,"selfRows":129,"metrics":130,"seqs":136,"entrants":161,"cells":169,"outcomes":245,"locators":246,"hardware":247,"wordings":248,"notes":249},"zou2022lidarslam-indoor-table-vii","zou2022lidarslam_indoor:Table VII","zou2022lidarslam_indoor","Zou et al., 2022","Table VII",33,[131],{"label":132,"unit":133,"statistic":134,"alignment":135},"Error (m)","m","not_reported","first-pose",[137,141,143,145,147,149,151,153,155,157,159],{"dataset":138,"sequence":139,"environment":140},"authors' own indoor recordings (rosbag)","line, run 1","Exp. I warehouse",{"dataset":138,"sequence":142,"environment":140},"line, run 2",{"dataset":138,"sequence":144,"environment":140},"line, run 3",{"dataset":138,"sequence":146,"environment":140},"L shape, run 1",{"dataset":138,"sequence":148,"environment":140},"L shape, run 2",{"dataset":138,"sequence":150,"environment":140},"rectangle, run 1",{"dataset":138,"sequence":152,"environment":140},"rectangle, run 2",{"dataset":138,"sequence":154,"environment":140},"rectangle, run 3",{"dataset":138,"sequence":156,"environment":140},"random, run 1",{"dataset":138,"sequence":158,"environment":140},"random, run 2",{"dataset":138,"sequence":160,"environment":140},"random, run 3",[162,165,167],{"name":163,"methodId":5,"linkable":164,"proposed":77,"self":164},"Cartographer, LiDAR",true,{"name":166,"methodId":5,"linkable":164,"proposed":77,"self":164},"Cartographer, LiDAR (+IMU)",{"name":168,"methodId":5,"linkable":164,"proposed":77,"self":164},"Cartographer, LiDAR (+IMU+Wheel)",[170,174,177,179,181,183,185,187,189,191,194,197,199,201,203,205,208,211,214,216,218,220,221,223,225,228,231,234,236,237,239,241,243],[171,171,171,172,173,171,173,173,171],0,0.003,-1,[171,171,175,176,173,171,173,173,171],1,0.018,[171,171,178,171,173,171,173,173,171],2,[175,171,171,180,173,171,173,173,171],-0.016,[175,171,175,182,173,171,173,173,171],0.001,[175,171,178,184,173,171,173,173,171],-0.005,[178,171,171,186,173,171,173,173,171],0.002,[178,171,175,188,173,171,173,173,171],0.016,[178,171,178,190,173,171,173,173,171],0.019,[171,171,192,193,173,171,173,173,171],3,0.148,[171,171,195,196,173,171,173,173,171],4,0.126,[175,171,192,198,173,171,173,173,171],0.131,[175,171,195,200,173,171,173,173,171],0.108,[178,171,192,202,173,171,173,173,171],0.124,[178,171,195,204,173,171,173,173,171],0.092,[171,171,206,207,173,171,173,173,171],5,0.013,[171,171,209,210,173,171,173,173,171],6,0.015,[171,171,212,213,173,171,173,173,171],7,0.027,[175,171,206,215,173,171,173,173,171],0.031,[175,171,209,217,173,171,173,173,171],0.032,[175,171,212,219,173,171,173,173,171],0.037,[178,171,206,188,173,171,173,173,171],[178,171,209,222,173,171,173,173,171],0.03,[178,171,212,224,173,171,173,173,171],0.041,[171,171,226,227,173,171,173,173,171],8,0.011,[171,171,229,230,173,171,173,173,171],9,0.044,[171,171,232,233,173,171,173,173,171],10,0.047,[175,171,226,235,173,171,173,173,171],0.014,[175,171,229,215,173,171,173,173,171],[175,171,232,238,173,171,173,173,171],0.054,[178,171,226,240,173,171,173,173,171],0.004,[178,171,229,242,173,171,173,173,171],0.062,[178,171,232,244,173,171,173,173,171],0.052,[],[128],[],[],[250],"Exp. I data from Secs. IV-B.2 and IV-B.3 rerun with Cartographer in three sensor settings; end-point error (m) as listed (signed); GT end points (18.007, 0), (11.985, 5.003), (0, 0), (0, 0)",{"slug":252,"group":253,"sourceId":5,"sourceLabel":6,"table":254,"selfRows":255,"metrics":256,"seqs":317,"entrants":333,"cells":335,"outcomes":408,"locators":409,"hardware":410,"wordings":411,"notes":412},"cartographer2016-table-ii","cartographer2016:Table II","Table II",28,[257,260,263,266,269,271,273,275,277,279,281,283,285,287,289,291,293,295,297,299,301,303,305,307,309,311,313,315],{"label":258,"unit":133,"statistic":259,"alignment":50},"Absolute translational, mean ± std 0.0375 ± 0.0426","mean",{"label":261,"unit":262,"statistic":259,"alignment":50},"Squared translational, mean ± std 0.0032 ± 0.0285","m^2",{"label":264,"unit":265,"statistic":259,"alignment":50},"Absolute rotational, mean ± std 0.373 ± 0.469","deg",{"label":267,"unit":268,"statistic":259,"alignment":50},"Squared rotational, mean ± std 0.359 ± 3.696","deg^2",{"label":270,"unit":133,"statistic":259,"alignment":50},"Absolute translational, mean ± std 0.0229 ± 0.0239",{"label":272,"unit":262,"statistic":259,"alignment":50},"Squared translational, mean ± std 0.0011 ± 0.0040",{"label":274,"unit":265,"statistic":259,"alignment":50},"Absolute rotational, mean ± std 0.453 ± 1.335",{"label":276,"unit":268,"statistic":259,"alignment":50},"Squared rotational, mean ± std 1.986 ± 23.988",{"label":278,"unit":133,"statistic":259,"alignment":50},"Absolute translational, mean ± std 0.0395 ± 0.0488",{"label":280,"unit":262,"statistic":259,"alignment":50},"Squared translational, mean ± std 0.0039 ± 0.0144",{"label":282,"unit":265,"statistic":259,"alignment":50},"Absolute rotational, mean ± std 0.352 ± 0.353",{"label":284,"unit":268,"statistic":259,"alignment":50},"Squared rotational, mean ± std 0.248 ± 0.610",{"label":286,"unit":133,"statistic":259,"alignment":50},"Absolute translational, mean ± std 0.0319 ± 0.0363",{"label":288,"unit":262,"statistic":259,"alignment":50},"Squared translational, mean ± std 0.0023 ± 0.0099",{"label":290,"unit":265,"statistic":259,"alignment":50},"Absolute rotational, mean ± std 0.369 ± 0.365",{"label":292,"unit":268,"statistic":259,"alignment":50},"Squared rotational, mean ± std 0.270 ± 0.637",{"label":294,"unit":133,"statistic":259,"alignment":50},"Absolute translational, mean ± std 0.0452 ± 0.0354",{"label":296,"unit":262,"statistic":259,"alignment":50},"Squared translational, mean ± std 0.0033 ± 0.0055",{"label":298,"unit":265,"statistic":259,"alignment":50},"Absolute rotational, mean ± std 0.538 ± 0.718",{"label":300,"unit":268,"statistic":259,"alignment":50},"Squared rotational, mean ± std 0.804 ± 3.627",{"label":302,"unit":133,"statistic":259,"alignment":50},"Absolute translational, mean ± std 0.1078 ± 0.1943",{"label":304,"unit":262,"statistic":259,"alignment":50},"Squared translational, mean ± std 0.0494 ± 0.2831",{"label":306,"unit":265,"statistic":259,"alignment":50},"Absolute rotational, mean ± std 0.747 ± 2.047",{"label":308,"unit":268,"statistic":259,"alignment":50},"Squared rotational, mean ± std 4.745 ± 40.081",{"label":310,"unit":133,"statistic":259,"alignment":50},"Absolute translational, mean ± std 5.2242 ± 6.6230",{"label":312,"unit":262,"statistic":259,"alignment":50},"Squared translational, mean ± std 71.0288 ± 267.7715",{"label":314,"unit":265,"statistic":259,"alignment":50},"Absolute rotational, mean ± std 3.341 ± 4.797",{"label":316,"unit":268,"statistic":259,"alignment":50},"Squared rotational, mean ± std 34.107 ± 127.227",[318,321,323,325,327,329,331],{"dataset":319,"sequence":320,"environment":134},"Radish","Aces",{"dataset":319,"sequence":322,"environment":134},"Intel",{"dataset":319,"sequence":324,"environment":134},"MIT Killian Court",{"dataset":319,"sequence":326,"environment":134},"MIT CSAIL",{"dataset":319,"sequence":328,"environment":134},"Freiburg bldg 79",{"dataset":319,"sequence":330,"environment":134},"Freiburg hospital (local relations)",{"dataset":319,"sequence":332,"environment":134},"Freiburg hospital (global relations)",[334],{"name":7,"methodId":5,"linkable":164,"proposed":164,"self":164},[336,338,340,342,344,346,348,350,352,354,356,358,361,364,367,370,373,376,379,382,385,388,390,393,396,399,402,405],[171,171,171,337,173,171,173,173,171],0.0375,[171,175,171,339,173,171,173,173,171],0.0032,[171,178,171,341,173,171,173,173,171],0.373,[171,192,171,343,173,171,173,173,171],0.359,[171,195,175,345,173,171,173,173,171],0.0229,[171,206,175,347,173,171,173,173,171],0.0011,[171,209,175,349,173,171,173,173,171],0.453,[171,212,175,351,173,171,173,173,171],1.986,[171,226,178,353,173,171,173,173,171],0.0395,[171,229,178,355,173,171,173,173,171],0.0039,[171,232,178,357,173,171,173,173,171],0.352,[171,359,178,360,173,171,173,173,171],11,0.248,[171,362,192,363,173,171,173,173,171],12,0.0319,[171,365,192,366,173,171,173,173,171],13,0.0023,[171,368,192,369,173,171,173,173,171],14,0.369,[171,371,192,372,173,171,173,173,171],15,0.27,[171,374,195,375,173,171,173,173,171],16,0.0452,[171,377,195,378,173,171,173,173,171],17,0.0033,[171,380,195,381,173,171,173,173,171],18,0.538,[171,383,195,384,173,171,173,173,171],19,0.804,[171,386,206,387,173,171,173,173,175],20,0.1078,[171,121,206,389,173,171,173,173,175],0.0494,[171,391,206,392,173,171,173,173,175],22,0.747,[171,394,206,395,173,171,173,173,175],23,4.745,[171,397,209,398,173,171,173,173,178],24,5.2242,[171,400,209,401,173,171,173,173,178],25,71.0288,[171,403,209,404,173,171,173,173,178],26,3.341,[171,406,209,407,173,171,173,173,178],27,34.107,[],[254],[],[],[413,414,415],"Radish benchmarks, relative-pose error against manually verified relations (metric of Kuemmerle et al. [21]); mean with std; GM values quoted from [21]","Radish benchmarks, relative-pose error against manually verified relations (metric of Kuemmerle et al. [21]); mean with std; GM values quoted from [21]; parameters tuned on the local relations","Radish benchmarks, relative-pose error against manually verified relations (metric of Kuemmerle et al. [21]); mean with std; GM values quoted from [21]; global relations not used for tuning",{"slug":417,"group":418,"sourceId":419,"sourceLabel":420,"table":421,"selfRows":121,"metrics":422,"seqs":445,"entrants":460,"cells":473,"outcomes":579,"locators":585,"hardware":586,"wordings":587,"notes":588},"rogers2020subttunnel-table-i","rogers2020subttunnel:Table I","rogers2020subttunnel","Rogers et al., 2020","Table I",[423,427,430,433,435,437,439,441,443],{"label":424,"unit":425,"statistic":134,"alignment":426},"Score (artifacts reported within 5 m of surveyed position): 8\u002F8","%","control points",{"label":428,"unit":133,"statistic":429,"alignment":426},"RMSE of artifact position error against surveyed ground truth","RMSE",{"label":431,"unit":133,"statistic":432,"alignment":426},"Max err (maximum artifact position error)","max",{"label":434,"unit":425,"statistic":134,"alignment":426},"Score (artifacts reported within 5 m of surveyed position): 7\u002F8",{"label":436,"unit":425,"statistic":134,"alignment":426},"Score (artifacts reported within 5 m of surveyed position): 9\u002F9",{"label":438,"unit":425,"statistic":134,"alignment":426},"Score (artifacts reported within 5 m of surveyed position): 1\u002F20",{"label":440,"unit":425,"statistic":134,"alignment":426},"Score (artifacts reported within 5 m of surveyed position): 18\u002F20",{"label":442,"unit":425,"statistic":134,"alignment":426},"Score (artifacts reported within 5 m of surveyed position): 4\u002F13",{"label":444,"unit":425,"statistic":134,"alignment":426},"Score (artifacts reported within 5 m of surveyed position): '-'",[446,450,452,455,457],{"dataset":447,"sequence":448,"environment":449},"SubT-Tunnel","sr_B_route1.bag, run length 909 m","underground mine: NIOSH Bruceton mine, Safety Research (SR) course, configuration B",{"dataset":447,"sequence":451,"environment":449},"sr_B_route2.bag, run length 792 m",{"dataset":447,"sequence":453,"environment":454},"ex_B_route1.bag, run length 1930 m","underground mine: NIOSH Bruceton mine, Experimental (EX) course, configuration B",{"dataset":447,"sequence":456,"environment":454},"ex_B_route2.bag (poor odometry run), run length 1187 m",{"dataset":447,"sequence":458,"environment":459},"stix_mainloop.bag, run length 871 m","underground mine: Edgar mine (STIX event), Idaho Springs, Colorado",[461,463,464,466,468,471],{"name":462,"methodId":71,"linkable":77,"proposed":77,"self":77},"OmniMapper",{"name":7,"methodId":5,"linkable":164,"proposed":77,"self":164},{"name":465,"methodId":5,"linkable":164,"proposed":77,"self":164},"Cartographer 2D (artifacts and detections projected to X-Y plane)",{"name":467,"methodId":71,"linkable":77,"proposed":77,"self":77},"ORB SLAM2+ (modified recovery: continue from last pose when tracking is lost)",{"name":469,"methodId":470,"linkable":164,"proposed":77,"self":77},"ORB SLAM2","orbslam2_2017",{"name":472,"methodId":71,"linkable":77,"proposed":77,"self":77},"Odometry (wheel odometry with IMU orientation, no mapping)",[474,476,478,480,482,484,486,487,489,491,493,495,497,498,500,502,503,504,506,507,509,510,512,514,516,518,520,522,524,526,527,529,531,533,535,537,539,541,543,545,547,549,551,553,555,557,559,560,561,562,564,566,567,569,570,571,572,573,574,575,577],[171,171,171,475,173,171,173,173,171],100,[171,175,171,477,173,171,173,173,171],1.12,[171,178,171,479,173,171,173,173,171],2.22,[175,192,171,481,173,171,173,173,171],87.5,[175,175,171,483,173,171,173,173,171],3.3,[175,178,171,485,173,171,173,173,171],7.2,[178,171,171,475,173,171,173,173,171],[178,175,171,488,173,171,173,173,171],1.5,[178,178,171,490,173,171,173,173,171],2.1,[192,175,171,492,171,171,173,173,171],13.7,[192,178,171,494,171,171,173,173,171],19.2,[195,175,171,496,175,171,173,173,171],1.2,[195,178,171,496,175,171,173,173,171],[206,175,171,499,173,171,173,173,171],13.25,[206,178,171,501,173,171,173,173,171],27.7,[171,195,175,475,173,171,173,173,171],[171,175,175,488,173,171,173,173,171],[171,178,175,505,173,171,173,173,171],2.7,[175,195,175,475,173,171,173,173,171],[175,175,175,508,173,171,173,173,171],2.3,[175,178,175,192,173,171,173,173,171],[192,175,175,511,175,171,173,173,171],28.3,[192,178,175,513,175,171,173,173,171],51.6,[195,175,175,515,175,171,173,173,171],4.8,[195,178,175,517,175,171,173,173,171],6.2,[206,175,175,519,173,171,173,173,171],9.6,[206,178,175,521,173,171,173,173,171],15.3,[171,175,178,523,173,171,173,173,171],2.4,[171,178,178,525,173,171,173,173,171],4.1,[175,206,178,206,173,171,173,173,171],[175,175,178,528,173,171,173,173,171],11.9,[175,178,178,530,173,171,173,173,171],18.2,[178,209,178,532,173,171,173,173,171],90,[178,175,178,534,173,171,173,173,171],2.6,[178,178,178,536,173,171,173,173,171],6.7,[192,175,178,538,175,171,173,173,171],15.1,[192,178,178,540,175,171,173,173,171],32.2,[195,175,178,542,175,171,173,173,171],2.62,[195,178,178,544,175,171,173,173,171],3.5,[206,175,178,546,173,171,173,173,171],54.9,[206,178,178,548,173,171,173,173,171],168.1,[171,175,192,550,173,171,173,173,171],9.1,[171,178,192,552,173,171,173,173,171],22.1,[175,212,192,554,173,171,173,173,171],30.8,[175,175,192,556,173,171,173,173,171],19.4,[175,178,192,558,173,171,173,173,171],46.2,[192,175,192,71,178,171,173,173,171],[192,178,192,71,178,171,173,173,171],[206,212,192,554,173,171,173,173,171],[206,175,192,563,173,171,173,173,171],23.8,[206,178,192,565,173,171,173,173,171],44.3,[171,175,195,368,173,171,173,173,171],[171,178,195,568,173,171,173,173,171],37.2,[175,226,195,71,192,171,173,173,171],[175,175,195,71,192,171,173,173,171],[175,178,195,71,192,171,173,173,171],[195,226,195,71,195,171,173,173,171],[195,175,195,71,195,171,173,173,171],[195,178,195,71,195,171,173,173,171],[206,175,195,576,173,171,173,173,171],19.3,[206,178,195,578,173,171,173,173,171],47.8,[580,581,582,583,584],"other: score and RMSE marked with * in the table","other: score marked with * in Table I; the symbol is not defined in the table (Sec. IV only says each ORB-SLAM2 run was terminated once the robot left the lit area)","other: 0\u002F0* in the table (no artifact evaluated; RMSE and errors printed as 0)","not_run (odometry message omitted during STIX data collection, Sec. IV)","not_run (right camera image omitted at STIX, Sec. IV)",[421],[],[],[589],"Artifact-based absolute mapping score on SubT-Tunnel: SLAM map aligned to the surveyed darpa frame by Umeyama on >=3 surveyed AprilTags (stereo depth); hand-coded artifact sightings scored as a point if within 5 m of the surveyed position; RMSE and max error over artifact reports. '*' is not defined in the table (per Sec. IV ORB-SLAM2 runs were stopped once the robot left the lit area); '-' = not run. Score converted to percent from the reported fraction.",{"slug":591,"group":592,"sourceId":593,"sourceLabel":594,"table":254,"selfRows":368,"metrics":595,"seqs":610,"entrants":620,"cells":640,"outcomes":838,"locators":842,"hardware":843,"wordings":845,"notes":846},"locus2021-table-ii","locus2021:Table II","locus2021","Palieri et al., 2021",[596,598,600,603,606,609],{"label":597,"unit":133,"statistic":432,"alignment":134},"APE max",{"label":599,"unit":133,"statistic":259,"alignment":134},"APE mean",{"label":601,"unit":133,"statistic":602,"alignment":134},"APE std","std",{"label":604,"unit":133,"statistic":429,"alignment":605},"ME (map error) RMSE","SE3",{"label":607,"unit":608,"statistic":432,"alignment":134},"CPU load (number of cores)","cores",{"label":607,"unit":608,"statistic":259,"alignment":134},[611,615,617],{"dataset":612,"sequence":613,"environment":614},"DARPA SubT Husky datasets (CoSTAR)","Urban Alpha course","decommissioned power plant, Satsop (Elma, WA): long feature-poor corridors and large open spaces",{"dataset":612,"sequence":616,"environment":614},"Urban Beta course",{"dataset":612,"sequence":618,"environment":619},"Tunnel Safety Research course","Bruceton Research Mine, Pittsburgh: self-similar and self-repetitive tunnels",[621,623,625,627,630,633,634,637],{"name":622,"methodId":593,"linkable":164,"proposed":164,"self":77},"LOCUS",{"name":624,"methodId":593,"linkable":164,"proposed":164,"self":77},"LOCUS FGA",{"name":626,"methodId":71,"linkable":77,"proposed":77,"self":77},"BLAM",{"name":628,"methodId":629,"linkable":164,"proposed":77,"self":77},"ALOAM","aloam_software",{"name":631,"methodId":632,"linkable":164,"proposed":77,"self":77},"FLOAM","floam2021",{"name":7,"methodId":5,"linkable":164,"proposed":77,"self":164},{"name":635,"methodId":636,"linkable":164,"proposed":77,"self":77},"LIO-Mapping","liomapping2019",{"name":638,"methodId":639,"linkable":164,"proposed":77,"self":77},"LIO-SAM","liosam2020",[641,643,645,647,649,651,653,655,657,659,661,663,665,666,668,669,671,673,675,676,678,680,682,683,684,685,686,687,688,690,692,694,696,698,700,702,704,706,708,710,712,714,716,718,720,722,724,726,728,730,732,734,736,738,740,742,744,746,748,750,752,754,756,758,760,762,764,766,768,770,772,774,776,778,780,782,784,786,788,790,792,794,796,798,799,801,802,803,805,806,808,810,812,814,816,818,819,821,823,824,825,826,827,828,829,830,831,833,835,836],[171,171,171,642,173,171,173,173,171],1.69,[171,175,171,644,173,171,173,173,171],0.62,[171,178,171,646,173,171,173,173,171],0.57,[171,192,171,648,173,171,173,173,171],0.29,[171,171,175,650,173,171,173,173,171],1.51,[171,175,175,652,173,171,173,173,171],0.88,[171,178,175,654,173,171,173,173,171],0.51,[171,192,175,656,173,171,173,173,171],0.69,[171,171,178,658,173,171,173,173,171],3.39,[171,175,178,660,173,171,173,173,171],1.67,[171,178,178,662,173,171,173,173,171],0.76,[171,192,178,664,173,171,173,173,171],0.63,[171,195,175,658,173,171,171,173,171],[171,206,175,667,173,171,171,173,171],2.72,[175,171,171,664,173,171,173,173,171],[175,175,171,670,173,171,173,173,171],0.26,[175,178,171,672,173,171,173,173,171],0.18,[175,192,171,674,173,171,173,173,171],0.28,[175,171,175,496,173,171,173,173,171],[175,175,175,677,173,171,173,173,171],0.58,[175,178,175,679,173,171,173,173,171],0.39,[175,192,175,681,173,171,173,173,171],0.48,[175,171,178,71,171,171,173,173,171],[175,175,178,71,171,171,173,173,171],[175,178,178,71,171,171,173,173,171],[175,192,178,71,171,171,173,173,171],[175,195,175,658,173,171,171,173,171],[175,206,175,667,173,171,171,173,171],[178,171,171,689,173,171,173,173,171],3.44,[178,175,171,691,173,171,173,173,171],1.01,[178,178,171,693,173,171,173,173,171],0.94,[178,192,171,695,173,171,173,173,171],0.43,[178,171,175,697,173,171,173,173,171],3.89,[178,175,175,699,173,171,173,173,171],2.27,[178,178,175,701,173,171,173,173,171],0.89,[178,192,175,703,173,171,173,173,171],1.27,[178,171,178,705,173,171,173,173,171],171.34,[178,175,178,707,173,171,173,173,171],35.45,[178,178,178,709,173,171,173,173,171],51.91,[178,192,178,711,173,171,173,173,171],5.37,[178,195,175,713,173,171,171,173,171],1.14,[178,206,175,715,173,171,171,173,171],0.93,[192,171,171,717,173,171,173,173,171],4.33,[192,175,171,719,173,171,173,173,171],1.38,[192,178,171,721,173,171,173,173,171],1.19,[192,192,171,723,173,171,173,173,171],0.6,[192,171,175,725,173,171,173,173,171],2.58,[192,175,175,727,173,171,173,173,171],2.11,[192,178,175,729,173,171,173,173,171],0.44,[192,192,175,731,173,171,173,173,171],0.99,[192,171,178,733,173,171,173,173,171],18.61,[192,175,178,735,173,171,173,173,171],10.01,[192,178,178,737,173,171,173,173,171],6.01,[192,192,178,739,173,171,173,173,171],6.11,[192,195,175,741,173,171,171,173,171],1.65,[192,206,175,743,173,171,171,173,171],1.41,[195,171,171,745,173,171,173,173,171],29.49,[195,175,171,747,173,171,173,173,171],9.19,[195,178,171,749,173,171,173,173,171],8.96,[195,192,171,751,175,171,173,173,171],1.73,[195,171,175,753,173,171,173,173,171],40.64,[195,175,175,755,173,171,173,173,171],3.94,[195,178,175,757,173,171,173,173,171],8.42,[195,192,175,759,175,171,173,173,171],3.73,[195,171,178,761,173,171,173,173,171],85.31,[195,175,178,763,173,171,173,173,171],32.49,[195,178,178,765,173,171,173,173,171],25.73,[195,192,178,767,173,171,173,173,171],20.16,[195,195,175,769,173,171,171,173,171],1.76,[195,206,175,771,173,171,171,173,171],1.44,[206,171,171,773,173,171,173,173,171],5.84,[206,175,171,775,173,171,173,173,171],2.91,[206,178,171,777,173,171,173,173,171],1.6,[206,192,171,779,173,171,173,173,171],1.05,[206,171,175,781,173,171,173,173,171],2.64,[206,175,175,783,173,171,173,173,171],1.37,[206,178,175,785,173,171,173,173,171],0.67,[206,192,175,787,173,171,173,173,171],0.31,[206,171,178,789,173,171,173,173,171],50.05,[206,175,178,791,173,171,173,173,171],14.31,[206,178,178,793,173,171,173,173,171],13.45,[206,192,178,795,173,171,173,173,171],14.25,[206,195,175,797,173,171,171,173,171],1.75,[206,206,175,652,173,171,171,173,171],[209,171,171,800,173,171,173,173,171],2.12,[209,175,171,731,173,171,173,173,171],[209,178,171,654,173,171,173,173,171],[209,192,171,804,173,171,173,173,171],0.45,[209,171,175,777,173,171,173,173,171],[209,175,175,807,173,171,173,173,171],1.18,[209,178,175,809,173,171,173,173,171],0.22,[209,192,175,811,173,171,173,173,171],0.61,[209,171,178,813,173,171,173,173,171],3.31,[209,175,178,815,173,171,173,173,171],1.99,[209,178,178,817,173,171,173,173,171],0.55,[209,192,178,662,173,171,173,173,171],[209,195,175,820,173,171,171,173,171],1.8,[209,206,175,822,173,171,171,173,171],1.53,[212,171,171,71,178,171,173,173,171],[212,175,171,71,178,171,173,173,171],[212,178,171,71,178,171,173,173,171],[212,192,171,71,178,171,173,173,171],[212,171,175,71,178,171,173,173,171],[212,175,175,71,178,171,173,173,171],[212,178,175,71,178,171,173,173,171],[212,192,175,71,178,171,173,173,171],[212,171,178,832,173,171,173,173,171],2.45,[212,175,178,834,173,171,173,173,171],1.26,[212,178,178,677,173,171,173,173,171],[212,192,178,837,173,171,173,173,171],0.52,[839,840,841],"not_run (FGA variant not reported on the Tunnel dataset)","value marked * in Table II: failure leads to a low map error","failed (authors could not get LIO-SAM working on the Urban datasets, likely because the 50 Hz IMU rate is below the recommended 200 Hz)",[254],[844],"Intel Hades Canyon NUC8i7HVKVA (4 x 1.9 GHz, 32 GB RAM, Ubuntu 18.04)",[],[847],"Husky field datasets from the SubT Urban (Alpha, Beta courses at the Satsop power plant) and Tunnel (Safety Research course, Bruceton mine) circuits; APE via evo against a reference from LOCUS scan matching on the DARPA ground-truth map; ME = RMSE of cloud-to-cloud error after ICP alignment of the map to the DARPA ground-truth map; loop closures disabled; FLOAM and LIO-Mapping ran with one LiDAR in Urban Alpha, LIO-SAM with one LiDAR; CPU loads from Urban Beta (LIO-SAM from Tunnel)",[849,854,861,866,873,877,884,889,894,900,906,910,914,919,923,927,933],{"group":850,"slug":851,"sourceLabel":6,"table":852,"selfRows":362,"datasets":853},"cartographer2016:Table IV","cartographer2016-table-iv","Table IV",[319],{"group":855,"slug":856,"sourceLabel":857,"table":858,"selfRows":362,"datasets":859},"vegatorres2023ogm2pgbm:Table 1","vegatorres2023ogm2pgbm-table-1","Torres et al., 2023","Table 1",[860],"Gazebo simulation from an as-designed IFC model, three scenarios (building not named in the paper)",{"group":862,"slug":863,"sourceLabel":6,"table":421,"selfRows":232,"datasets":864},"cartographer2016:Table I","cartographer2016-table-i",[865],"own Revo LDS capture",{"group":867,"slug":868,"sourceLabel":869,"table":870,"selfRows":232,"datasets":871},"dlo2022:Table III","dlo2022-table-iii","Chen et al., 2022a","Table III",[872],"DARPA SubT Urban Circuit (Alpha and Beta courses)",{"group":874,"slug":875,"sourceLabel":6,"table":870,"selfRows":226,"datasets":876},"cartographer2016:Table III","cartographer2016-table-iii",[319],{"group":878,"slug":879,"sourceLabel":880,"table":881,"selfRows":226,"datasets":882},"rtabmap2019:Table 9","rtabmap2019-table-9","Labbé & Michaud, 2019","Table 9",[883],"MIT Stata Center (PR2)",{"group":885,"slug":886,"sourceLabel":127,"table":887,"selfRows":226,"datasets":888},"zou2022lidarslam_indoor:Table IX","zou2022lidarslam-indoor-table-ix","Table IX",[138],{"group":890,"slug":891,"sourceLabel":6,"table":892,"selfRows":209,"datasets":893},"cartographer2016:Table V","cartographer2016-table-v","Table V",[319],{"group":895,"slug":896,"sourceLabel":6,"table":897,"selfRows":206,"datasets":898},"cartographer2016:Text Sec. VI.A","cartographer2016-text-sec-vi-a","Text Sec. VI.A",[899],"Deutsches Museum (own backpack data)",{"group":901,"slug":902,"sourceLabel":903,"table":852,"selfRows":195,"datasets":904},"dliom2023:Table IV","dliom2023-table-iv","Wang et al., 2023b",[905],"TONGJI dataset",{"group":907,"slug":908,"sourceLabel":127,"table":870,"selfRows":195,"datasets":909},"zou2022lidarslam_indoor:Table III","zou2022lidarslam-indoor-table-iii",[138],{"group":911,"slug":912,"sourceLabel":127,"table":892,"selfRows":195,"datasets":913},"zou2022lidarslam_indoor:Table V","zou2022lidarslam-indoor-table-v",[138],{"group":915,"slug":916,"sourceLabel":127,"table":917,"selfRows":195,"datasets":918},"zou2022lidarslam_indoor:Table VI","zou2022lidarslam-indoor-table-vi","Table VI",[138],{"group":920,"slug":921,"sourceLabel":594,"table":870,"selfRows":192,"datasets":922},"locus2021:Table III","locus2021-table-iii",[612],{"group":924,"slug":925,"sourceLabel":127,"table":852,"selfRows":192,"datasets":926},"zou2022lidarslam_indoor:Table IV","zou2022lidarslam-indoor-table-iv",[138],{"group":928,"slug":929,"sourceLabel":930,"table":421,"selfRows":175,"datasets":931},"droeschel2018ctslam:Table I","droeschel2018ctslam-table-i","Droeschel & Behnke, 2018",[932],"Deutsches Museum (Cartographer dataset)",{"group":934,"slug":935,"sourceLabel":936,"table":937,"selfRows":175,"datasets":938},"ghadimzadeh2025slamnde:Table 3","ghadimzadeh2025slamnde-table-3","Ghadimzadeh Alamdari et al., 2025","Table 3",[939],"Luleå SubT tunnel dataset (Koval et al. 2022)",1790510655587]