[{"data":1,"prerenderedAt":823},["ShallowReactive",2],{"method-ruan2023slamesh":3},{"method":4,"reference":51,"equipment":72,"figures":92,"results":93},{"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":26,"sensors":31,"platform":33,"estimator":35,"association":36,"timeModel":37,"deskew":38,"loopClosure":39,"globalOptimization":40,"mapRepresentation":41,"prior":40,"outputGeometry":42,"compute":43,"codeUrl":44,"codeLicense":45,"relatedVersions":46},"ruan2023slamesh","Ruan et al., 2023","SLAMesh","SLAMesh: Real-time LiDAR Simultaneous Localization and Meshing",2023,"recent","C12","odometry_with_local_mapping","SLAMesh 將掃描點分入體素格，在每格內以高斯過程（Gaussian process）回歸局部表面，於規則分布的位置預測頂點座標與不確定性，再直接連接相鄰頂點形成網格。新掃描同樣重建後，依頂點位置快速建立點對網格（point-to-mesh）對應進行配準；地圖更新只需修正頂點的一維預測值，因此可在 CPU 上即時同時定位與建網格。","Per-cell Gaussian-process surface reconstruction yields regularly placed vertices with uncertainty that are connected into a mesh, enabling fast point-to-mesh registration and incremental mesh updates on a CPU.","full_text_reviewed","peer_reviewed_published","main_body","僅在 Mai City 模擬與 KITTI 驗證，未涉及營建。",[20,21],"simulation","public_benchmark",[23,24,25],"On Mai City (d = 0.3 m, each pipeline's own estimated poses) SLAMesh reached F1 80.14% against 76.83% for Voxblox+A-LOAM and 51.08% for Puma (Sec. IV-B, Table I).","KITTI 00-10 average relative errors 0.676% and 0.291 deg\u002F100 m without loop closure (Sec. IV-C, Table II).","Multithreading reduced processing from 57.1 ms to 23.8 ms per scan (Sec. IV-E, Fig. 8).",[27,28,29,30],"Mesh-LOAM authors state SLAMesh becomes complicated when modeling complicated geometries (Zhu et al., Mesh-LOAM arXiv v1, Sec. I-II).","(inference) Fixed cell size and regular vertex locations bound the smallest representable detail.","(evaluation caveat) KITTI baseline numbers in Table II were imported from the baselines' published papers rather than rerun (Sec. IV-C).","(evaluation caveat) Voxblox in the A-LOAM+Voxblox pipeline used a 50 m sensor range instead of 100 m, and SLAMesh parameters were tuned manually (Sec. IV-A, IV-D).",[32],"3D LiDAR",[34,20],"vehicle","iterative point-to-mesh registration with constraint combination (per-layer residual averaging), solved by Levenberg-Marquardt in Ceres with analytic Jacobians; constant-velocity initial guess","location-based matching of GP-reconstructed vertices to mesh faces in same or adjacent cells, with smoothed face normals","discrete poses","not_reported","no explicit loop closure; optional implicit alignment when registering to revisited areas improves map consistency but was disabled for KITTI odometry metrics (Sec. IV-C)","none","hash map of voxel cells (1.6 m; 1.5 m for the mesh evaluation), each holding up to three Gaussian-process layers of 6 x 6 regularly located vertices with a predicted coordinate and variance, adjacent or diagonal valid vertices connected into triangles","triangle mesh map with vertex uncertainty","CPU only, 3.6 GHz 8-core Intel i7-11700KF with 8 threads; about 40 Hz on KITTI 07; multithreading cut per-scan time from 57.1 ms to 23.8 ms; with 3 m cells about 80 Hz at 0.88% translation error (Sec. IV-A, IV-D, IV-E)","https:\u002F\u002Fgithub.com\u002Flab-sun\u002FSLAMesh","GPL-3.0 (repository LICENSE)",[47],{"relation":48,"title":49,"doi_or_url":50},"preprint","arXiv:2303.05252","https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.05252",{"id":5,"kind":52,"shortName":7,"title":8,"authors":53,"year":9,"venue":58,"venueType":59,"publisher":60,"volumeIssuePages":61,"doi":62,"arxivId":63,"url":50,"firstPublicDate":64,"publicationStatus":16,"metadataStatus":65,"fulltextStatus":15,"era":10,"classicReason":66,"codeUrl":44,"cluster":11,"topics":67,"mdpi":68,"verification":69,"label":6,"fulltextRoute":70,"versionRead":71,"addedByCensus":68},"method",[54,55,56,57],"Jianyuan Ruan","Bo Li","Yibo Wang","Yuxiang Sun","2023 IEEE International Conference on Robotics and Automation (ICRA)","conference","IEEE","pp. 3546-3552","10.1109\u002Ficra48891.2023.10161425","2303.05252","2023-03-09","metadata_verified","not_applicable",[11],false,"corrected","arXiv","arXiv v1 (2023-03-09, 'Accepted by ICRA 2023', only version) read in full; ICRA 2023 version of record pp. 3546-3552 obtained via NTU access and Tables I-II plus runtime sentences checked (identical)",[73,81,85],{"category":74,"model":75,"canonical":76,"role":77,"dataset":78,"specs":79,"locator":80},"lidar","Velodyne HDL-64E (simulated)","Velodyne HDL-64E","dataset sensor","Mai City","simulated 64-beam LiDAR in CARLA","Sec. IV-B",{"category":74,"model":76,"canonical":76,"role":77,"dataset":82,"specs":83,"locator":84},"KITTI odometry","not_reported beyond model","Sec. IV-C",{"category":86,"model":87,"canonical":87,"role":88,"dataset":89,"specs":90,"locator":91},"compute","Intel i7-11700KF","compute for runtime",null,"3.6 GHz, 8 cores; 8 threads allocated","Sec. IV-A",[],{"totalRows":94,"groupCount":95,"groups":96,"others":788},82,11,[97,385,525,677],{"slug":98,"group":99,"sourceId":5,"sourceLabel":6,"table":100,"selfRows":101,"metrics":102,"seqs":110,"entrants":136,"cells":161,"outcomes":377,"locators":379,"hardware":380,"wordings":381,"notes":382},"ruan2023slamesh-table-ii","ruan2023slamesh:Table II","Table II",39,[103,107],{"label":104,"unit":105,"statistic":106,"alignment":38},"relative translation error (%)","%","mean",{"label":108,"unit":109,"statistic":106,"alignment":38},"relative rotation error (deg\u002F100m)","deg\u002F100m",[111,114,116,118,120,122,124,126,128,130,132,134],{"dataset":82,"sequence":112,"environment":113},"00","urban, country and highway driving",{"dataset":82,"sequence":115,"environment":113},"01",{"dataset":82,"sequence":117,"environment":113},"02",{"dataset":82,"sequence":119,"environment":113},"03",{"dataset":82,"sequence":121,"environment":113},"04",{"dataset":82,"sequence":123,"environment":113},"05",{"dataset":82,"sequence":125,"environment":113},"06",{"dataset":82,"sequence":127,"environment":113},"07",{"dataset":82,"sequence":129,"environment":113},"08",{"dataset":82,"sequence":131,"environment":113},"09",{"dataset":82,"sequence":133,"environment":113},"10",{"dataset":82,"sequence":135,"environment":113},"Mean",[137,141,144,147,150,152,155,157,159],{"name":138,"methodId":139,"linkable":140,"proposed":68,"self":68},"LOAM","loam2014",true,{"name":142,"methodId":143,"linkable":140,"proposed":68,"self":68},"A-LOAM","aloam_software",{"name":145,"methodId":146,"linkable":140,"proposed":68,"self":68},"Suma","suma2018",{"name":148,"methodId":149,"linkable":140,"proposed":68,"self":68},"Suma++","sumapp2019",{"name":151,"methodId":89,"linkable":68,"proposed":68,"self":68},"Litamin2",{"name":153,"methodId":154,"linkable":140,"proposed":68,"self":68},"Puma","vizzo2021puma",{"name":156,"methodId":5,"linkable":140,"proposed":140,"self":140},"SLAMesh (Ours) Full",{"name":158,"methodId":5,"linkable":140,"proposed":140,"self":140},"SLAMesh w\u002Fo Comb.",{"name":160,"methodId":5,"linkable":140,"proposed":140,"self":140},"SLAMesh w\u002Fo P2Mesh",[162,166,169,172,175,178,181,184,187,190,193,196,198,199,201,203,205,207,209,210,212,214,216,218,220,222,224,226,228,230,231,233,235,236,237,238,239,240,242,244,246,248,249,251,253,254,256,258,259,261,263,265,267,268,270,272,274,276,278,280,282,284,286,288,290,292,294,296,298,299,301,303,305,307,309,311,313,315,317,318,320,321,322,323,325,327,329,331,332,333,335,337,338,339,341,343,344,346,347,348,349,351,353,355,357,358,360,361,362,363,365,366,368,370,371,373,375],[163,163,163,164,165,163,165,165,163],0,0.78,-1,[163,163,167,168,165,163,165,165,163],1,1.43,[163,163,170,171,165,163,165,165,163],2,0.92,[163,163,173,174,165,163,165,165,163],3,0.86,[163,163,176,177,165,163,165,165,163],4,0.71,[163,163,179,180,165,163,165,165,163],5,0.57,[163,163,182,183,165,163,165,165,163],6,0.65,[163,163,185,186,165,163,165,165,163],7,0.63,[163,163,188,189,165,163,165,165,163],8,1.12,[163,163,191,192,165,163,165,165,163],9,0.77,[163,163,194,195,165,163,165,165,163],10,0.79,[163,163,95,197,165,163,165,165,163],0.839,[163,167,95,89,163,163,165,165,167],[167,163,163,200,165,163,165,165,163],0.97,[167,163,167,202,165,163,165,165,163],2.75,[167,163,170,204,165,163,165,165,163],4.91,[167,163,173,206,165,163,165,165,163],1.22,[167,163,176,208,165,163,165,165,163],1.35,[167,163,179,186,165,163,165,165,163],[167,163,182,211,165,163,165,165,163],0.61,[167,163,185,213,165,163,165,165,163],0.48,[167,163,188,215,165,163,165,165,163],1.17,[167,163,191,217,165,163,165,165,163],1.11,[167,163,194,219,165,163,165,165,163],1.58,[167,163,95,221,165,163,165,165,163],1.525,[167,167,95,223,165,163,165,165,167],0.928,[170,163,163,225,165,163,165,165,163],0.7,[170,163,167,227,165,163,165,165,163],1.7,[170,163,170,229,165,163,165,165,163],1.1,[170,163,173,225,165,163,165,165,163],[170,163,176,232,165,163,165,165,163],0.4,[170,163,179,234,165,163,165,165,163],0.5,[170,163,182,232,165,163,165,165,163],[170,163,185,232,165,163,165,165,163],[170,163,188,167,165,163,165,165,163],[170,163,191,234,165,163,165,165,163],[170,163,194,225,165,163,165,165,163],[170,163,95,241,165,163,165,165,163],0.736,[170,167,95,243,165,163,165,165,167],0.318,[173,163,163,245,165,163,165,165,163],0.64,[173,163,167,247,165,163,165,165,163],1.6,[173,163,170,167,165,163,165,165,163],[173,163,173,250,165,163,165,165,163],0.67,[173,163,176,252,165,163,165,165,163],0.37,[173,163,179,232,165,163,165,165,163],[173,163,182,255,165,163,165,165,163],0.46,[173,163,185,257,165,163,165,165,163],0.34,[173,163,188,229,165,163,165,165,163],[173,163,191,260,165,163,165,165,163],0.47,[173,163,194,262,165,163,165,165,163],0.66,[173,163,95,264,165,163,165,165,163],0.701,[173,167,95,266,165,163,165,165,167],0.294,[176,163,163,225,165,163,165,165,163],[176,163,167,269,165,163,165,165,163],2.1,[176,163,170,271,165,163,165,165,163],0.98,[176,163,173,273,165,163,165,165,163],0.96,[176,163,176,275,165,163,165,165,163],1.05,[176,163,179,277,165,163,165,165,163],0.45,[176,163,182,279,165,163,165,165,163],0.59,[176,163,185,281,165,163,165,165,163],0.44,[176,163,188,283,165,163,165,165,163],0.95,[176,163,191,285,165,163,165,165,163],0.69,[176,163,194,287,165,163,165,165,163],0.8,[176,163,95,289,165,163,165,165,163],0.883,[176,167,95,291,165,163,165,165,167],0.375,[179,163,163,293,165,163,165,165,163],1.46,[179,163,167,295,165,163,165,165,163],3.38,[179,163,170,297,165,163,165,165,163],1.86,[179,163,173,247,165,163,165,165,163],[179,163,176,300,165,163,165,165,163],1.63,[179,163,179,302,165,163,165,165,163],1.2,[179,163,182,304,165,163,165,165,163],0.88,[179,163,185,306,165,163,165,165,163],0.72,[179,163,188,308,165,163,165,165,163],1.44,[179,163,191,310,165,163,165,165,163],1.51,[179,163,194,312,165,163,165,165,163],1.38,[179,163,95,314,165,163,165,165,163],1.551,[179,167,95,316,165,163,165,165,167],0.737,[182,163,163,192,165,163,165,165,163],[182,163,167,319,165,163,165,165,163],1.25,[182,163,170,192,165,163,165,165,163],[182,163,173,245,165,163,165,165,163],[182,163,176,234,165,163,165,165,163],[182,163,179,324,165,163,165,165,163],0.52,[182,163,182,326,165,163,165,165,163],0.53,[182,163,185,328,165,163,165,165,163],0.36,[182,163,188,330,165,163,165,165,163],0.87,[182,163,191,180,165,163,165,165,163],[182,163,194,183,165,163,165,165,163],[182,163,95,334,165,163,165,165,163],0.676,[182,167,95,336,165,163,165,165,167],0.291,[185,163,163,273,165,163,165,165,163],[185,163,167,227,165,163,165,165,163],[185,163,170,340,165,163,165,165,163],1.04,[185,163,173,342,165,163,165,165,163],0.81,[185,163,176,250,165,163,165,165,163],[185,163,179,345,165,163,165,165,163],0.51,[185,163,182,245,165,163,165,165,163],[185,163,185,255,165,163,165,165,163],[185,163,188,174,165,163,165,165,163],[185,163,191,350,165,163,165,165,163],0.74,[185,163,194,352,165,163,165,165,163],1.02,[185,163,95,354,165,163,165,165,163],0.857,[185,167,95,356,165,163,165,165,167],0.363,[188,163,163,225,165,163,165,165,163],[188,163,167,359,165,163,165,165,163],1.33,[188,163,170,174,165,163,165,165,163],[188,163,173,279,165,163,165,165,163],[188,163,176,285,165,163,165,165,163],[188,163,179,364,165,163,165,165,163],0.49,[188,163,182,252,165,163,165,165,163],[188,163,185,367,165,163,165,165,163],0.43,[188,163,188,369,165,163,165,165,163],1.14,[188,163,191,192,165,163,165,165,163],[188,163,194,372,165,163,165,165,163],0.93,[188,163,95,374,165,163,165,165,163],0.756,[188,167,95,376,165,163,165,165,167],0.366,[378],"not_reported (dash in table)",[100],[],[],[383,384],"KITTI odometry 00-10, relative translation error; baseline values imported from their published papers (not rerun); SLAMesh without loop closure; w\u002Fo Comb. and w\u002Fo P2Mesh are ablations","KITTI odometry 00-10, relative translation error; baseline values imported from their published papers (not rerun); SLAMesh without loop closure; w\u002Fo Comb. and w\u002Fo P2Mesh are ablations; only the Mean column of the rotation row is extracted",{"slug":386,"group":387,"sourceId":388,"sourceLabel":389,"table":390,"selfRows":194,"metrics":391,"seqs":405,"entrants":411,"cells":424,"outcomes":518,"locators":519,"hardware":521,"wordings":522,"notes":523},"zhu2025meshloam-table-v","zhu2025meshloam:Table V","zhu2025meshloam","Zhu et al., 2025","Table V",[392,395,397,399,401,403],{"label":393,"unit":394,"statistic":38,"alignment":40},"Comp. (cm), completion","cm",{"label":396,"unit":394,"statistic":38,"alignment":40},"Acc. (cm), accuracy",{"label":398,"unit":394,"statistic":38,"alignment":40},"C-L1 (cm), Chamfer-L1 distance",{"label":400,"unit":105,"statistic":38,"alignment":40},"Comp.Ratio (%)",{"label":402,"unit":105,"statistic":38,"alignment":40},"F-score (10cm) (%)",{"label":404,"unit":105,"statistic":38,"alignment":40},"F-score (20cm) (%)",[406,408],{"dataset":78,"sequence":78,"environment":407},"simulated urban street",{"dataset":409,"sequence":38,"environment":410},"Newer College","outdoor college (handheld)",[412,415,417,420,422],{"name":413,"methodId":414,"linkable":140,"proposed":68,"self":68},"VDB Fusion [28]","vizzo2022vdbfusion",{"name":416,"methodId":154,"linkable":140,"proposed":68,"self":68},"Puma [13]",{"name":418,"methodId":419,"linkable":140,"proposed":68,"self":68},"SHINE-Mapping [30]","shinemapping2023",{"name":421,"methodId":5,"linkable":140,"proposed":68,"self":140},"SLAMesh [14]",{"name":423,"methodId":388,"linkable":140,"proposed":140,"self":68},"Ours",[425,427,429,431,433,435,437,438,440,442,444,446,447,449,451,453,455,457,459,461,463,465,466,468,470,472,473,475,477,479,481,483,485,487,489,491,493,495,497,499,501,503,504,506,508,510,512,513,515,517],[163,163,163,426,165,163,165,165,163],6.9,[163,167,163,428,165,163,165,165,163],1.3,[163,170,163,430,165,163,165,165,163],4.5,[163,173,163,432,165,163,165,165,163],90.2,[163,176,163,434,165,163,165,165,163],94.1,[163,163,167,436,165,163,165,165,163],12,[163,167,167,426,165,163,165,165,163],[163,170,167,439,165,163,165,165,163],9.4,[163,173,167,441,165,163,165,165,163],91.3,[163,179,167,443,165,163,165,165,163],92.6,[167,163,163,445,165,163,165,165,163],32,[167,167,163,302,165,163,165,165,163],[167,170,163,448,165,163,165,165,163],16.9,[167,173,163,450,165,163,165,165,163],78.8,[167,176,163,452,165,163,165,165,163],87.3,[167,163,167,454,165,163,165,165,163],15.4,[167,167,167,456,165,163,165,165,163],7.7,[167,170,167,458,165,163,165,165,163],11.5,[167,173,167,460,165,163,165,165,163],89.9,[167,179,167,462,165,163,165,165,163],91.9,[170,163,163,464,165,163,165,165,163],3.2,[170,167,163,229,165,163,165,165,163],[170,170,163,467,165,163,165,165,163],2.9,[170,173,163,469,165,163,165,165,163],95.2,[170,176,163,471,165,163,165,165,163],95.9,[170,163,167,194,165,163,165,165,163],[170,167,167,474,165,163,165,165,163],6.7,[170,170,167,476,165,163,165,165,163],8.4,[170,173,167,478,165,163,165,165,163],93.6,[170,179,167,480,165,163,165,165,163],93.7,[173,163,163,482,165,163,165,165,163],7.5,[173,167,163,484,165,163,165,165,163],3.7,[173,170,163,486,165,163,165,165,163],6.1,[173,173,163,488,165,163,165,165,163],89.2,[173,176,163,490,165,163,165,165,163],90.6,[173,163,167,492,165,163,165,165,163],13.7,[173,167,167,494,165,163,165,165,163],11.4,[173,170,167,496,165,163,165,165,163],12.6,[173,173,167,498,165,163,165,165,163],83.5,[173,179,167,500,165,163,165,165,163],82.3,[176,163,163,502,165,163,165,165,163],2.5,[176,167,163,302,165,163,165,165,163],[176,170,163,505,165,163,165,165,163],2.4,[176,173,163,507,165,163,165,165,163],96.3,[176,176,163,509,165,163,165,165,163],97.4,[176,163,167,511,165,163,165,165,163],9.6,[176,167,167,474,165,163,165,165,163],[176,170,167,514,165,163,165,165,163],8.2,[176,173,167,516,165,163,165,165,163],94.2,[176,179,167,434,165,163,165,165,163],[],[520],"Table V (VoR)",[],[],[524],"Mesh quality with ground-truth poses for all methods, voxel size 0.1 m, settings of SHINE-Mapping; distances in cm; completion ratio and F-score in % at 10 cm (Mai City) and 20 cm (Newer College)",{"slug":526,"group":527,"sourceId":528,"sourceLabel":529,"table":530,"selfRows":188,"metrics":531,"seqs":540,"entrants":546,"cells":562,"outcomes":670,"locators":672,"hardware":673,"wordings":674,"notes":675},"pinslam2024-table-xi","pinslam2024:Table XI","pinslam2024","Pan et al., 2024","Table XI",[532,534,536,538],{"label":533,"unit":394,"statistic":38,"alignment":38},"Map. Acc.",{"label":535,"unit":394,"statistic":38,"alignment":38},"Map. Comp.",{"label":537,"unit":394,"statistic":38,"alignment":38},"C-l1 (Chamfer-L1)",{"label":539,"unit":105,"statistic":38,"alignment":38},"F-score (20 cm threshold)",[541,544],{"dataset":409,"sequence":542,"environment":543},"Quad (02_long)","outdoor campus quadrangle and building, handheld",{"dataset":409,"sequence":545,"environment":543},"Math Institute (math_easy)",[547,549,551,553,555,557,560],{"name":548,"methodId":414,"linkable":140,"proposed":68,"self":68},"VDB-Fusion [77] with KISS-ICP poses",{"name":550,"methodId":419,"linkable":140,"proposed":68,"self":68},"SHINE [100] with KISS-ICP poses",{"name":552,"methodId":89,"linkable":68,"proposed":68,"self":68},"NKSR [20] with KISS-ICP poses",{"name":554,"methodId":154,"linkable":140,"proposed":68,"self":68},"Puma [76] own odometry",{"name":556,"methodId":5,"linkable":140,"proposed":68,"self":140},"SLAMesh [62] own odometry",{"name":558,"methodId":559,"linkable":140,"proposed":68,"self":68},"Nerf-LOAM [11] own odometry","nerfloam2023",{"name":561,"methodId":528,"linkable":140,"proposed":140,"self":68},"PIN-SLAM (own odometry)",[563,565,567,569,571,573,575,577,579,581,583,585,587,589,591,593,595,597,599,601,603,605,607,609,611,613,615,617,619,621,623,625,627,629,631,633,635,637,639,641,643,645,647,649,651,652,653,654,655,657,659,661,663,664,666,668],[163,163,163,564,165,163,165,165,163],14.03,[163,167,163,566,165,163,165,165,163],25.46,[163,170,163,568,165,163,165,165,163],19.75,[163,173,163,570,165,163,165,165,163],69.5,[163,163,167,572,165,163,165,165,163],15.21,[163,167,167,574,165,163,165,165,163],28.66,[163,170,167,576,165,163,165,165,163],21.94,[163,173,167,578,165,163,165,165,163],63.35,[167,163,163,580,165,163,165,165,163],14.87,[167,167,163,582,165,163,165,165,163],20.02,[167,170,163,584,165,163,165,165,163],17.45,[167,173,163,586,165,163,165,165,163],68.85,[167,163,167,588,165,163,165,165,163],14.46,[167,167,167,590,165,163,165,165,163],34.03,[167,170,167,592,165,163,165,165,163],24.24,[167,173,167,594,165,163,165,165,163],64.38,[170,163,163,596,165,163,165,165,163],15.67,[170,167,163,598,165,163,165,165,163],36.87,[170,170,163,600,165,163,165,165,163],26.27,[170,173,163,602,165,163,165,165,163],58.57,[170,163,167,604,165,163,165,165,163],15.11,[170,167,167,606,165,163,165,165,163],27.1,[170,170,167,608,165,163,165,165,163],21.11,[170,173,167,610,165,163,165,165,163],65.08,[173,163,163,612,165,163,165,165,163],15.3,[173,167,163,614,165,163,165,165,163],71.91,[173,170,163,616,165,163,165,165,163],43.6,[173,173,163,618,165,163,165,165,163],57.27,[173,163,167,620,165,163,165,165,163],15.81,[173,167,167,622,165,163,165,165,163],46,[173,170,167,624,165,163,165,165,163],30.91,[173,173,167,626,165,163,165,165,163],54.95,[176,163,163,628,165,163,165,165,163],19.21,[176,167,163,630,165,163,165,165,163],48.83,[176,170,163,632,165,163,165,165,163],34.02,[176,173,163,634,165,163,165,165,163],45.24,[176,163,167,636,165,163,165,165,163],12.8,[176,167,167,638,165,163,165,165,163],23.5,[176,170,167,640,165,163,165,165,163],18.16,[176,173,167,642,165,163,165,165,163],75.17,[179,163,163,644,165,163,165,165,163],12.89,[179,167,163,646,165,163,165,165,163],22.21,[179,170,163,648,165,163,165,165,163],17.55,[179,173,163,650,165,163,165,165,163],74.37,[179,163,167,89,163,163,165,165,163],[179,167,167,89,163,163,165,165,163],[179,170,167,89,163,163,165,165,163],[179,173,167,89,163,163,165,165,163],[182,163,163,656,165,163,165,165,163],11.55,[182,167,163,658,165,163,165,165,163],15.25,[182,170,163,660,165,163,165,165,163],13.4,[182,173,163,662,165,163,165,165,163],82.08,[182,163,167,492,165,163,165,165,163],[182,167,167,665,165,163,165,165,163],21.91,[182,170,167,667,165,163,165,165,163],17.8,[182,173,167,669,165,163,165,165,163],75.49,[671],"not_reported ('-')",[530],[],[],[676],"Newer College 3D reconstruction against the survey-grade TLS reference model (mm-level accuracy); Quad from 02_long, Math Institute from math_easy; 20 cm voxel for all methods; F-score threshold 20 cm; mapping-only methods use KISS-ICP poses; '-' not reported or unavailable",{"slug":678,"group":679,"sourceId":388,"sourceLabel":389,"table":680,"selfRows":182,"metrics":681,"seqs":685,"entrants":704,"cells":716,"outcomes":780,"locators":782,"hardware":784,"wordings":785,"notes":786},"zhu2025meshloam-table-iii","zhu2025meshloam:Table III","Table III",[682],{"label":683,"unit":684,"statistic":38,"alignment":38},"ATE (m)","m",[686,690,693,695,698,701],{"dataset":687,"sequence":688,"environment":689},"Hilti SLAM Challenge 2021","RPG","indoor (RPG)",{"dataset":687,"sequence":691,"environment":692},"Base1","basement",{"dataset":687,"sequence":694,"environment":692},"Base4",{"dataset":687,"sequence":696,"environment":697},"Lab","laboratory",{"dataset":687,"sequence":699,"environment":700},"Cons2","construction site (outdoor)",{"dataset":687,"sequence":702,"environment":703},"Camp2","campus (outdoor)",[705,707,710,713,714,715],{"name":706,"methodId":146,"linkable":140,"proposed":68,"self":68},"SuMa [8]",{"name":708,"methodId":709,"linkable":140,"proposed":68,"self":68},"FLOAM [4]","floam2021",{"name":711,"methodId":712,"linkable":140,"proposed":68,"self":68},"KISS-ICP [5]","kissicp2023",{"name":416,"methodId":154,"linkable":140,"proposed":68,"self":68},{"name":421,"methodId":5,"linkable":140,"proposed":68,"self":140},{"name":423,"methodId":388,"linkable":140,"proposed":140,"self":68},[717,719,721,723,725,727,728,730,732,734,736,738,740,742,743,745,747,749,751,752,753,754,755,756,757,759,761,763,765,767,769,771,772,774,776,778],[163,163,163,718,165,163,165,165,163],0.262,[163,163,167,720,165,163,165,165,163],2.244,[163,163,170,722,165,163,165,165,163],0.286,[163,163,173,724,165,163,165,165,163],0.045,[163,163,176,726,165,163,165,165,163],1.642,[163,163,179,89,163,163,165,165,163],[167,163,163,729,165,163,165,165,163],2.775,[167,163,167,731,165,163,165,165,163],0.914,[167,163,170,733,165,163,165,165,163],0.287,[167,163,173,735,165,163,165,165,163],0.182,[167,163,176,737,165,163,165,165,163],11.515,[167,163,179,739,165,163,165,165,163],8.946,[170,163,163,741,165,163,165,165,163],0.187,[170,163,167,266,165,163,165,165,163],[170,163,170,744,165,163,165,165,163],0.119,[170,163,173,746,165,163,165,165,163],0.073,[170,163,176,748,165,163,165,165,163],0.835,[170,163,179,750,165,163,165,165,163],5.052,[173,163,163,89,163,163,165,165,163],[173,163,167,89,163,163,165,165,163],[173,163,170,89,163,163,165,165,163],[173,163,173,89,163,163,165,165,163],[173,163,176,89,163,163,165,165,163],[173,163,179,89,163,163,165,165,163],[176,163,163,758,165,163,165,165,163],0.165,[176,163,167,760,165,163,165,165,163],0.175,[176,163,170,762,165,163,165,165,163],0.33,[176,163,173,764,165,163,165,165,163],0.048,[176,163,176,766,165,163,165,165,163],0.339,[176,163,179,768,165,163,165,165,163],0.653,[179,163,163,770,165,163,165,165,163],0.173,[179,163,167,758,165,163,165,165,163],[179,163,170,773,165,163,165,165,163],0.267,[179,163,173,775,165,163,165,165,163],0.049,[179,163,176,777,165,163,165,165,163],0.083,[179,163,179,779,165,163,165,165,163],0.113,[781],"failed",[783],"Table III (VoR)",[],[],[787],"Hilti SLAM Challenge 2021, Ouster OS0-64 data only; all methods run by the authors with their own implementations; caption gives ATE in m while the text says %; most sequences have 3-DoF ground truth; 'x' = failed registration",[789,795,800,805,809,814,819],{"group":790,"slug":791,"sourceLabel":389,"table":792,"selfRows":182,"datasets":793},"zhu2025meshloam:Table VIII","zhu2025meshloam-table-viii","Table VIII",[794],"KITTI Odometry",{"group":796,"slug":797,"sourceLabel":6,"table":798,"selfRows":176,"datasets":799},"ruan2023slamesh:Text Sec. IV-E","ruan2023slamesh-text-sec-iv-e","Text Sec. IV-E",[38],{"group":801,"slug":802,"sourceLabel":6,"table":803,"selfRows":173,"datasets":804},"ruan2023slamesh:Table I","ruan2023slamesh-table-i","Table I",[78],{"group":806,"slug":807,"sourceLabel":389,"table":803,"selfRows":170,"datasets":808},"zhu2025meshloam:Table I","zhu2025meshloam-table-i",[794],{"group":810,"slug":811,"sourceLabel":389,"table":812,"selfRows":170,"datasets":813},"zhu2025meshloam:Table IV","zhu2025meshloam-table-iv","Table IV",[78,409],{"group":815,"slug":816,"sourceLabel":6,"table":817,"selfRows":167,"datasets":818},"ruan2023slamesh:Text Sec. IV-D","ruan2023slamesh-text-sec-iv-d","Text Sec. IV-D",[82],{"group":820,"slug":821,"sourceLabel":389,"table":100,"selfRows":167,"datasets":822},"zhu2025meshloam:Table II","zhu2025meshloam-table-ii",[794],1790510663448]