[{"data":1,"prerenderedAt":671},["ShallowReactive",2],{"method-gmapping2007":3},{"method":4,"reference":52,"equipment":73,"figures":102,"results":103},{"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":26,"sensors":32,"platform":35,"estimator":37,"association":38,"timeModel":39,"deskew":40,"loopClosure":41,"globalOptimization":42,"mapRepresentation":43,"prior":42,"outputGeometry":44,"compute":45,"codeUrl":46,"codeLicense":47,"relatedVersions":48},"gmapping2007","Grisetti et al., 2007","GMapping","Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters",2007,"classic","C01","full_slam_with_global_correction","GMapping 在 Rao-Blackwellized 粒子濾波（每個粒子攜帶一張佔據網格地圖）上提出兩項改良：以掃描匹配結果與里程計共同計算較準確的提議分布（proposal distribution），以及依有效樣本數選擇性重取樣以減少粒子耗盡。作者報告所需粒子數大約比先前方法少一個數量級。當掃描匹配失敗（如大空曠區域多為最大量程讀值）時，系統退回原始里程計運動模型。","RBPF grid mapping with a scan-matching-informed proposal and adaptive resampling, reducing the particle count needed for correct maps.","full_text_reviewed","peer_reviewed_published","background","未在工地測試。Freiburg 校園資料中，停車場因施工移走車輛導致掃描大多為最大量程讀值，掃描匹配失效一次（Sec. VI.E）；此為資料集中的偶發事件，並非營建場域驗證，但說明空曠、低特徵區域的風險。",[20,21,22],"public_benchmark","completed_building","simulation",[24,25],"Particle count needed for a topologically correct map in at least 60% of runs was about one order of magnitude smaller than Hähnel et al.'s approach (Sec. VI.B).","Never more than 80 particles for datasets up to about 250 m x 250 m (Sec. VI).",[27,28,29,30,31],"Scan matcher can fail in large open spaces (mostly maximum-range readings) or with poor overlap, then only odometry is used (Sec. III-C","Sec. VI-E) | Partly violates the planar-environment assumption outdoors (Sec. VI-A, Freiburg campus) | Only one scan-matcher mode is sampled, so in theory the filter may become overly confident in extremely cluttered environments with very noisy odometry","the authors never met this case with real robots (Sec. III-C) | With 60 particles the MIT Killian Court maps sometimes showed artificial double walls","80 particles were needed for high quality (Sec. VI-A) | Each particle copies its full grid map at resampling, giving a worst-case O(NM) cost","adaptive resampling keeps such steps rare (Sec. V) | (inference) Evaluation targets topological correctness and particle counts rather than metric map error against an independent reference, so it cannot support 3D point-cloud accuracy claims",[33,34],"2D laser range finder","wheel odometry",[36],"wheeled UGV","Rao-Blackwellized particle filter; per-particle Gaussian proposal fitted to K samples around the scan-matcher mode, weighted by observation likelihood and the odometry motion model; raw motion model used when scan matching fails; resampling only when Neff drops below N\u002F2 (Sec. III-B to III-E)","per-particle scan matching with the CARMEN 'vasco' matcher: gradient descent on the beam-endpoint likelihood of the current scan against the particle's own grid map, with the search bounded around the odometry-based initial guess (Sec. III-E, IV)","discrete poses; a filter update after each 0.5 m of travel or 25 deg of rotation (Sec. III-C, VI-F)","not_reported","implicit through particle filter (no explicit loop-closure module)","none","2D occupancy grid per particle","2D occupancy grid map","online on robots; per observation O(N) without resampling and O(NM) with resampling, M the grid size (Sec. V, Table I); Intel Lab log (45 min) corrected in under 30 min with 30 particles, 150 MB, 5 cm grid, 2.8 GHz PC; average execution times 1910 ms for proposal, weights and map update, 41 ms for the resampling test and 244 ms for resampling (Sec. VI-F, Table III)","https:\u002F\u002Fgithub.com\u002FOpenSLAM-org\u002Fopenslam_gmapping","BSD-3-Clause (stated on the OpenSLAM.org GMapping page, which links 'Get the Source Code!' to this repository; no LICENSE file at the repository root)",[49],{"relation":50,"title":51,"doi_or_url":46},"code_release","OpenSLAM GMapping repository (GitHub organisation OpenSLAM-org)",{"id":5,"kind":53,"shortName":7,"title":8,"authors":54,"year":9,"venue":58,"venueType":59,"publisher":60,"volumeIssuePages":61,"doi":62,"arxivId":63,"url":64,"firstPublicDate":65,"publicationStatus":16,"metadataStatus":66,"fulltextStatus":15,"era":10,"classicReason":67,"codeUrl":46,"cluster":11,"topics":68,"mdpi":69,"verification":70,"label":6,"fulltextRoute":71,"versionRead":72,"addedByCensus":69},"method",[55,56,57],"Giorgio Grisetti","Cyrill Stachniss","Wolfram Burgard","IEEE Transactions on Robotics","journal","IEEE","23(1):34-46","10.1109\u002Ftro.2006.889486",null,"http:\u002F\u002Fwww2.informatik.uni-freiburg.de\u002F~stachnis\u002Fpdf\u002Fgrisetti07tro.pdf","2007-02","metadata_verified","reproducible baseline: open-source 2D grid-based RBPF mapper (source linked from the OpenSLAM.org page listing the three authors); illustrates the particle-filter paradigm on laser data. Its use as a common 2D comparison baseline is a reviewer judgement not evidenced in this record.",[11],false,"corrected","author copy","author-hosted manuscript of the T-RO paper (University of Freiburg, grisetti07tro.pdf, 12 pages, generated 2007-01-17); layout differs from the T-RO version of record 23(1):34-46, which was not compared line by line",[74,80,82,84,88,90,96],{"category":75,"model":76,"canonical":76,"role":77,"dataset":63,"specs":78,"locator":79},"platform","ActivMedia Pioneer 2 AT","method input","equipped with SICK LMS or PLS laser range finders","Sec. VI, Fig. 3",{"category":75,"model":81,"canonical":81,"role":77,"dataset":63,"specs":78,"locator":79},"Pioneer 2 DX-8",{"category":75,"model":83,"canonical":83,"role":77,"dataset":63,"specs":78,"locator":79},"iRobot B21r",{"category":85,"model":86,"canonical":86,"role":77,"dataset":63,"specs":40,"locator":87},"lidar","SICK LMS","Sec. VI",{"category":85,"model":89,"canonical":89,"role":77,"dataset":63,"specs":40,"locator":87},"SICK PLS",{"category":75,"model":91,"canonical":91,"role":92,"dataset":93,"specs":94,"locator":95},"Pioneer II robot","dataset sensor","Intel Research Lab","equipped with a SICK sensor","Sec. VI-A",{"category":97,"model":98,"canonical":98,"role":99,"dataset":63,"specs":100,"locator":101},"compute","standard PC with a 2.8 GHz processor","compute for runtime","2.8 GHz","Sec. VI-F",[],{"totalRows":104,"groupCount":105,"groups":106,"others":627},79,12,[107,315,461,572],{"slug":108,"group":109,"sourceId":110,"sourceLabel":111,"table":112,"selfRows":113,"metrics":114,"seqs":131,"entrants":153,"cells":161,"outcomes":279,"locators":280,"hardware":281,"wordings":282,"notes":313},"kuemmerle2009measuring-table-1","kuemmerle2009measuring:Table 1","kuemmerle2009measuring","Kümmerle et al., 2009","Table 1",21,[115,119,122,125,127,129],{"label":116,"unit":117,"statistic":118,"alignment":42},"Translational error, Equation 4 using absolute errors (reported as mean ± std)","m","mean",{"label":120,"unit":121,"statistic":118,"alignment":42},"Translational error, Equation 4 using squared errors (reported as mean ± std)","m^2",{"label":123,"unit":117,"statistic":124,"alignment":42},"Translational error, Maximum absolute error of a relation","max",{"label":126,"unit":117,"statistic":118,"alignment":42},"Translational error, Equation 4 using absolute errors (reported as mean ± std) (footnote a: scan matching applied as a preprocessing step to improve the odometry)",{"label":128,"unit":121,"statistic":118,"alignment":42},"Translational error, Equation 4 using squared errors (reported as mean ± std) (footnote a: scan matching applied as a preprocessing step to improve the odometry)",{"label":130,"unit":117,"statistic":124,"alignment":42},"Translational error, Maximum absolute error of a relation (footnote a: scan matching applied as a preprocessing step to improve the odometry)",[132,136,139,142,145,148,151],{"dataset":133,"sequence":134,"environment":135},"Aces","local relations provided by the authors","indoor corridors, ACES building, University of Texas at Austin (Sec. 7)",{"dataset":137,"sequence":134,"environment":138},"Intel","indoor office environment with clutter, Intel Research Lab (Sec. 7)",{"dataset":140,"sequence":134,"environment":141},"MIT Killian Court","large indoor corridor environment with nested loops (Sec. 7, 8.2)",{"dataset":143,"sequence":134,"environment":144},"MIT CSAIL","indoor office environment, CSAIL at MIT (Sec. 7)",{"dataset":146,"sequence":134,"environment":147},"Freiburg bldg 79","indoor office building with one corridor, University of Freiburg (Sec. 7, 8.3)",{"dataset":149,"sequence":134,"environment":150},"Freiburg Hospital","outdoor park area of about 500 m by 250 m surrounded by buildings (Sec. 8.4)",{"dataset":149,"sequence":152,"environment":150},"only global relations from satellite images (Sec. 8.4.1)",[154,156,159],{"name":155,"methodId":63,"linkable":69,"proposed":69,"self":69},"Scan Matching",{"name":157,"methodId":5,"linkable":158,"proposed":69,"self":158},"RBPF (50 part.)",true,{"name":160,"methodId":63,"linkable":69,"proposed":69,"self":69},"Graph Mapping",[162,166,169,172,175,178,181,183,185,187,190,193,196,199,202,205,207,209,211,213,216,218,221,224,226,229,231,233,236,239,242,244,247,250,252,254,256,258,261,264,267,270,273,275,277],[163,163,163,164,165,163,165,163,163],0,0.173,-1,[167,163,163,168,165,163,165,167,163],1,0.06,[170,163,163,171,165,163,165,170,163],2,0.044,[163,167,163,173,165,163,165,174,163],0.407,3,[167,167,163,176,165,163,165,177,163],0.006,4,[170,167,163,179,165,163,165,180,163],0.004,5,[163,170,163,182,165,163,165,165,163],4.869,[167,170,163,184,165,163,165,165,163],0.433,[170,170,163,186,165,163,165,165,163],0.347,[163,163,167,188,165,163,165,189,163],0.22,6,[167,163,167,191,165,163,165,192,163],0.07,7,[170,163,167,194,165,163,165,195,163],0.031,8,[163,167,167,197,165,163,165,198,163],0.136,9,[167,167,167,200,165,163,165,201,163],0.011,10,[170,167,167,203,165,163,165,204,163],0.002,11,[163,170,167,206,165,163,165,165,163],1.168,[167,170,167,208,165,163,165,165,163],0.698,[170,170,167,210,165,163,165,165,163],0.229,[167,174,170,212,165,163,165,105,163],0.122,[167,177,170,214,165,163,165,215,163],0.164,13,[167,180,170,217,165,163,165,165,163],2.513,[167,174,174,219,165,163,165,220,163],0.049,14,[167,177,174,222,165,163,165,223,163],0.005,15,[167,180,174,225,165,163,165,165,163],0.508,[167,174,177,227,165,163,165,228,163],0.061,16,[167,177,177,176,165,163,165,230,163],17,[167,180,177,232,165,163,165,165,163],0.856,[163,163,180,234,165,163,165,235,163],0.434,18,[167,163,180,237,165,163,165,238,163],0.637,19,[170,163,180,240,165,163,165,241,163],0.143,20,[163,167,180,243,165,163,165,113,163],2.79,[167,167,180,245,165,163,165,246,163],7.367,22,[170,167,180,248,165,163,165,249,163],0.053,23,[163,170,180,251,165,163,165,165,163],15.584,[167,170,180,253,165,163,165,165,163],15.343,[170,170,180,255,165,163,165,165,163],2.385,[163,163,189,215,165,163,165,257,163],24,[167,163,189,259,165,163,165,260,163],12.3,25,[170,163,189,262,165,163,165,263,163],11.6,26,[163,167,189,265,165,163,165,266,163],305.4,27,[167,167,189,268,165,163,165,269,163],288.8,28,[170,167,189,271,165,163,165,272,163],276.1,29,[163,170,189,274,165,163,165,165,163],70.9,[167,170,189,276,165,163,165,165,163],65.1,[170,170,189,278,165,163,165,165,163],66.1,[],[112],[],[283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312],"Translational error, Equation 4 using absolute errors (reported as mean ± std; std = 0.614)","Translational error, Equation 4 using absolute errors (reported as mean ± std; std = 0.049)","Translational error, Equation 4 using absolute errors (reported as mean ± std; std = 0.044)","Translational error, Equation 4 using squared errors (reported as mean ± std; std = 2.726)","Translational error, Equation 4 using squared errors (reported as mean ± std; std = 0.011)","Translational error, Equation 4 using squared errors (reported as mean ± std; std = 0.009)","Translational error, Equation 4 using absolute errors (reported as mean ± std; std = 0.296)","Translational error, Equation 4 using absolute errors (reported as mean ± std; std = 0.083)","Translational error, Equation 4 using absolute errors (reported as mean ± std; std = 0.026)","Translational error, Equation 4 using squared errors (reported as mean ± std; std = 0.277)","Translational error, Equation 4 using squared errors (reported as mean ± std; std = 0.034)","Translational error, Equation 4 using squared errors (reported as mean ± std; std = 0.004)","Translational error, Equation 4 using absolute errors (reported as mean ± std; std = 0.386) (footnote a: scan matching applied as a preprocessing step to improve the odometry)","Translational error, Equation 4 using squared errors (reported as mean ± std; std = 0.814) (footnote a: scan matching applied as a preprocessing step to improve the odometry)","Translational error, Equation 4 using absolute errors (reported as mean ± std; std = 0.049) (footnote a: scan matching applied as a preprocessing step to improve the odometry)","Translational error, Equation 4 using squared errors (reported as mean ± std; std = 0.013) (footnote a: scan matching applied as a preprocessing step to improve the odometry)","Translational error, Equation 4 using absolute errors (reported as mean ± std; std = 0.044) (footnote a: scan matching applied as a preprocessing step to improve the odometry)","Translational error, Equation 4 using squared errors (reported as mean ± std; std = 0.020) (footnote a: scan matching applied as a preprocessing step to improve the odometry)","Translational error, Equation 4 using absolute errors (reported as mean ± std; std = 1.615)","Translational error, Equation 4 using absolute errors (reported as mean ± std; std = 2.638)","Translational error, Equation 4 using absolute errors (reported as mean ± std; std = 0.180)","Translational error, Equation 4 using squared errors (reported as mean ± std; std = 18.19)","Translational error, Equation 4 using squared errors (reported as mean ± std; std = 38.496)","Translational error, Equation 4 using squared errors (reported as mean ± std; std = 0.272)","Translational error, Equation 4 using absolute errors (reported as mean ± std; std = 11.6)","Translational error, Equation 4 using absolute errors (reported as mean ± std; std = 11.7)","Translational error, Equation 4 using absolute errors (reported as mean ± std; std = 11.9)","Translational error, Equation 4 using squared errors (reported as mean ± std; std = 518.9)","Translational error, Equation 4 using squared errors (reported as mean ± std; std = 626.3)","Translational error, Equation 4 using squared errors (reported as mean ± std; std = 516.5)",[314],"Translational error of the relative-relation metric (Eq. 4) averaged over all manually verified relations for three mapping approaches; abs values in m, squared values in m^2",{"slug":316,"group":317,"sourceId":110,"sourceLabel":111,"table":318,"selfRows":113,"metrics":319,"seqs":334,"entrants":342,"cells":346,"outcomes":425,"locators":426,"hardware":427,"wordings":428,"notes":459},"kuemmerle2009measuring-table-2","kuemmerle2009measuring:Table 2","Table 2",[320,323,326,328,330,332],{"label":321,"unit":322,"statistic":118,"alignment":42},"Rotational error, Equation 4 using absolute errors (reported as mean ± std)","deg",{"label":324,"unit":325,"statistic":118,"alignment":42},"Rotational error, Equation 4 using squared errors (reported as mean ± std)","deg^2",{"label":327,"unit":322,"statistic":124,"alignment":42},"Rotational error, Maximum absolute error of a relation",{"label":329,"unit":322,"statistic":118,"alignment":42},"Rotational error, Equation 4 using absolute errors (reported as mean ± std) (footnote a: scan matching applied as a preprocessing step to improve the odometry)",{"label":331,"unit":325,"statistic":118,"alignment":42},"Rotational error, Equation 4 using squared errors (reported as mean ± std) (footnote a: scan matching applied as a preprocessing step to improve the odometry)",{"label":333,"unit":322,"statistic":124,"alignment":42},"Rotational error, Maximum absolute error of a relation (footnote a: scan matching applied as a preprocessing step to improve the odometry)",[335,336,337,338,339,340,341],{"dataset":133,"sequence":134,"environment":135},{"dataset":137,"sequence":134,"environment":138},{"dataset":140,"sequence":134,"environment":141},{"dataset":143,"sequence":134,"environment":144},{"dataset":146,"sequence":134,"environment":147},{"dataset":149,"sequence":134,"environment":150},{"dataset":149,"sequence":152,"environment":150},[343,344,345],{"name":155,"methodId":63,"linkable":69,"proposed":69,"self":69},{"name":157,"methodId":5,"linkable":158,"proposed":69,"self":158},{"name":160,"methodId":63,"linkable":69,"proposed":69,"self":69},[347,349,350,352,354,356,358,360,362,364,366,367,369,371,373,374,376,378,380,382,384,386,388,390,392,393,395,396,397,398,399,401,403,405,407,408,410,412,413,414,415,417,419,421,423],[163,163,163,348,165,163,165,163,163],1.2,[167,163,163,348,165,163,165,167,163],[170,163,163,351,165,163,165,170,163],0.4,[163,167,163,353,165,163,165,174,163],3.7,[167,167,163,355,165,163,165,177,163],3.1,[170,167,163,357,165,163,165,180,163],0.3,[163,170,163,359,165,163,165,165,163],12.1,[167,170,163,361,165,163,165,165,163],7.9,[170,170,163,363,165,163,165,165,163],3.5,[163,163,167,365,165,163,165,189,163],1.7,[167,163,167,174,165,163,165,192,163],[170,163,167,368,165,163,165,195,163],1.3,[163,167,167,370,165,163,165,198,163],25.8,[167,167,167,372,165,163,165,201,163],36.7,[170,167,167,257,165,163,165,204,163],[163,170,167,375,165,163,165,165,163],4.5,[167,170,167,377,165,163,165,165,163],34.7,[170,170,167,379,165,163,165,165,163],6.4,[167,174,170,381,165,163,165,105,163],0.8,[167,177,170,383,165,163,165,215,163],0.9,[167,180,170,385,165,163,165,165,163],7.4,[167,174,174,387,165,163,165,220,163],0.6,[167,177,174,389,165,163,165,223,163],1.9,[167,180,174,391,165,163,165,165,163],18.2,[167,174,177,387,165,163,165,228,163],[167,177,177,394,165,163,165,230,163],0.7,[167,180,177,379,165,163,165,165,163],[163,163,180,368,165,163,165,235,163],[167,163,180,368,165,163,165,238,163],[170,163,180,383,165,163,165,241,163],[163,167,180,400,165,163,165,113,163],10.9,[167,167,180,402,165,163,165,246,163],7.1,[170,167,180,404,165,163,165,249,163],5.5,[163,170,180,406,165,163,165,165,163],27.4,[167,170,180,269,165,163,165,165,163],[170,170,180,409,165,163,165,165,163],29.6,[163,163,189,411,165,163,165,257,163],6.3,[167,163,189,404,165,163,165,260,163],[170,163,189,411,165,163,165,263,163],[163,167,189,278,165,163,165,266,163],[167,167,189,416,165,163,165,269,163],64.6,[170,167,189,418,165,163,165,272,163],77.2,[163,170,189,420,165,163,165,165,163],27.3,[167,170,189,422,165,163,165,165,163],35.1,[170,170,189,424,165,163,165,165,163],38.6,[],[318],[],[429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458],"Rotational error, Equation 4 using absolute errors (reported as mean ± std; std = 1.5)","Rotational error, Equation 4 using absolute errors (reported as mean ± std; std = 1.3)","Rotational error, Equation 4 using absolute errors (reported as mean ± std; std = 0.4)","Rotational error, Equation 4 using squared errors (reported as mean ± std; std = 10.7)","Rotational error, Equation 4 using squared errors (reported as mean ± std; std = 7.0)","Rotational error, Equation 4 using squared errors (reported as mean ± std; std = 0.8)","Rotational error, Equation 4 using absolute errors (reported as mean ± std; std = 4.8)","Rotational error, Equation 4 using absolute errors (reported as mean ± std; std = 5.3)","Rotational error, Equation 4 using absolute errors (reported as mean ± std; std = 4.7)","Rotational error, Equation 4 using squared errors (reported as mean ± std; std = 170.9)","Rotational error, Equation 4 using squared errors (reported as mean ± std; std = 187.7)","Rotational error, Equation 4 using squared errors (reported as mean ± std; std = 166.1)","Rotational error, Equation 4 using absolute errors (reported as mean ± std; std = 0.8) (footnote a: scan matching applied as a preprocessing step to improve the odometry)","Rotational error, Equation 4 using squared errors (reported as mean ± std; std = 1.7) (footnote a: scan matching applied as a preprocessing step to improve the odometry)","Rotational error, Equation 4 using absolute errors (reported as mean ± std; std = 1.2) (footnote a: scan matching applied as a preprocessing step to improve the odometry)","Rotational error, Equation 4 using squared errors (reported as mean ± std; std = 17.3) (footnote a: scan matching applied as a preprocessing step to improve the odometry)","Rotational error, Equation 4 using absolute errors (reported as mean ± std; std = 0.6) (footnote a: scan matching applied as a preprocessing step to improve the odometry)","Rotational error, Equation 4 using squared errors (reported as mean ± std; std = 2.0) (footnote a: scan matching applied as a preprocessing step to improve the odometry)","Rotational error, Equation 4 using absolute errors (reported as mean ± std; std = 3.0)","Rotational error, Equation 4 using absolute errors (reported as mean ± std; std = 2.3)","Rotational error, Equation 4 using absolute errors (reported as mean ± std; std = 2.2)","Rotational error, Equation 4 using squared errors (reported as mean ± std; std = 50.4)","Rotational error, Equation 4 using squared errors (reported as mean ± std; std = 42.2)","Rotational error, Equation 4 using squared errors (reported as mean ± std; std = 46.2)","Rotational error, Equation 4 using absolute errors (reported as mean ± std; std = 5.2)","Rotational error, Equation 4 using absolute errors (reported as mean ± std; std = 5.9)","Rotational error, Equation 4 using absolute errors (reported as mean ± std; std = 6.2)","Rotational error, Equation 4 using squared errors (reported as mean ± std; std = 101.4)","Rotational error, Equation 4 using squared errors (reported as mean ± std; std = 144.2)","Rotational error, Equation 4 using squared errors (reported as mean ± std; std = 154.8)",[460],"Rotational error of the relative-relation metric (Eq. 4) averaged over all manually verified relations for three mapping approaches; abs values in deg, squared values in deg^2",{"slug":462,"group":463,"sourceId":464,"sourceLabel":465,"table":466,"selfRows":195,"metrics":467,"seqs":473,"entrants":480,"cells":503,"outcomes":566,"locators":567,"hardware":568,"wordings":569,"notes":570},"rtabmap2019-table-9","rtabmap2019:Table 9","rtabmap2019","Labbé & Michaud, 2019","Table 9",[468,471],{"label":469,"unit":117,"statistic":470,"alignment":40},"ATEend","RMSE",{"label":472,"unit":117,"statistic":124,"alignment":40},"ATEmax (maximum per-frame ATE during the run)",[474,478],{"dataset":475,"sequence":476,"environment":477},"MIT Stata Center (PR2)","2012-01-25-12-14-25","indoor office building; Long-range lidar",{"dataset":475,"sequence":479,"environment":477},"2012-01-25-12-33-29",[481,483,486,488,490,493,495,497,499,501],{"name":482,"methodId":464,"linkable":158,"proposed":158,"self":69},"RTAB-Map (WheelIMU→S2M) [Long-range lidar]",{"name":484,"methodId":485,"linkable":158,"proposed":69,"self":69},"Cartographer (WheelIMU) [Long-range lidar]","cartographer2016",{"name":487,"methodId":5,"linkable":158,"proposed":69,"self":158},"GMapping (WheelIMU) [Long-range lidar]",{"name":489,"methodId":63,"linkable":69,"proposed":69,"self":69},"Karto SLAM (WheelIMU) [Long-range lidar]",{"name":491,"methodId":492,"linkable":158,"proposed":69,"self":69},"Hector SLAM (no odometry) [Long-range lidar]","hector2011",{"name":494,"methodId":464,"linkable":158,"proposed":158,"self":69},"RTAB-Map (WheelIMU→S2M) [Short-range lidar]",{"name":496,"methodId":485,"linkable":158,"proposed":69,"self":69},"Cartographer (WheelIMU) [Short-range 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Stata Center 2012-01-25 sequences; RTAB-Map WheelIMU→S2M versus other ROS 2D lidar SLAM run with default parameters; Cartographer, GMapping and Karto use WheelIMU odometry, Hector SLAM uses none; GMapping ATE computed on the current best particle path",{"slug":573,"group":574,"sourceId":575,"sourceLabel":576,"table":577,"selfRows":180,"metrics":578,"seqs":582,"entrants":595,"cells":600,"outcomes":621,"locators":622,"hardware":623,"wordings":624,"notes":625},"kissslam2025-table-vii","kissslam2025:Table VII","kissslam2025","Guadagnino et al., 2025a","Table VII",[579],{"label":580,"unit":581,"statistic":470,"alignment":40},"ATE translation RMS [cm] (mean over 10 runs)","cm",[583,587,589,591,593],{"dataset":584,"sequence":585,"environment":586},"authors' office sequences","Static Sequence 1","office (static and dynamic scenes)",{"dataset":584,"sequence":588,"environment":586},"Static Sequence 2",{"dataset":584,"sequence":590,"environment":586},"Static Sequence 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translation RMS, mean of 10 runs; ground truth from ceiling AprilTags seen by an upward camera; success rate and convergence time columns omitted",[628,635,640,645,651,655,660,665],{"group":629,"slug":630,"sourceLabel":631,"table":632,"selfRows":177,"datasets":633},"zou2022lidarslam_indoor:Table III","zou2022lidarslam-indoor-table-iii","Zou et al., 2022","Table III",[634],"authors' own indoor recordings (rosbag)",{"group":636,"slug":637,"sourceLabel":631,"table":638,"selfRows":177,"datasets":639},"zou2022lidarslam_indoor:Table V","zou2022lidarslam-indoor-table-v","Table V",[634],{"group":641,"slug":642,"sourceLabel":631,"table":643,"selfRows":177,"datasets":644},"zou2022lidarslam_indoor:Table VI","zou2022lidarslam-indoor-table-vi","Table VI",[634],{"group":646,"slug":647,"sourceLabel":6,"table":648,"selfRows":174,"datasets":649},"gmapping2007:Table II","gmapping2007-table-ii","Table II",[650,93,140],"Freiburg 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