[{"data":1,"prerenderedAt":759},["ShallowReactive",2],{"method-balm2021":3},{"method":4,"reference":52,"equipment":72,"figures":101,"results":102},{"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":32,"platform":34,"estimator":37,"association":38,"timeModel":39,"deskew":40,"loopClosure":41,"globalOptimization":42,"mapRepresentation":43,"prior":41,"outputGeometry":44,"compute":45,"codeUrl":46,"codeLicense":47,"relatedVersions":48},"balm2021","Liu & Zhang, 2021","BALM","BALM: Bundle Adjustment for Lidar Mapping",2021,"recent","C06","offline_map_refinement","BALM 將光達束調整（LiDAR bundle adjustment, BA）定義為最小化各特徵點到其所屬邊緣或平面的距離，並證明邊緣與平面參數可用封閉解消去，使最佳化只剩下掃描位姿，因而可以納入大量稠密平面與邊緣特徵。作者推導代價函數對位姿的一階與二階解析導數，並提出自適應體素化（adaptive voxelization），以八元樹遞迴切分空間，直到每個體素只含單一平面或邊緣。此 BA 被整合為 LOAM 架構的後端，在滑動視窗內做局部地圖精修。","BALM formulates LiDAR BA as point-to-edge\u002Fplane distance minimization in which feature parameters are eliminated in closed form, leaving a pose-only optimization; adaptive voxelization supplies correspondences, and the BA runs as a sliding-window back-end of LOAM.","full_text_reviewed","peer_reviewed_published","main_body","未見營建工地驗證；作者測試為校園戶外手持、建築室內樓梯手持與 UGV 走廊轉角（Sec. VI、Fig. 2、Fig. 7），以回到起點的漂移評估，無獨立參考量測。其點到平面 BA 公式被 BALM2、HBA、LEMON-Mapping 沿用或作比較（見各紀錄），此為對工程點雲一致化的主要意義（推論）。",[20,21],"public_benchmark","completed_building",[23,24,25],"Eliminating feature parameters reduces the optimization dimension so that large numbers of dense plane\u002Fedge features can be used (Sec. I contributions)","Local BA lowered LOAM drift in handheld Livox Horizon, UGV Livox Mid-40 and VLP-16 tests (Sec. VI-A to VI-C, Tables I-II)","Voxel-based correspondence search reduced scan-to-map time relative to kd-tree nearest-point search in the authors' comparison (Sec. VI-D, Fig. 9a)",[27,28,29,30,31],"Adaptive voxelization requires good initial pose alignment (Sec. VII)","Odometry front-end does not compensate motion distortion or use a motion model (Sec. VII)","Temporal sliding window keeps redundant, highly overlapping scans; keyframes left to future work (Sec. VII)","With the 40 degree FoV Livox Mid-40 the scan-to-map front-end still degenerates at corridor corners (zigzag trajectory); the local BA only mitigates the resulting map inconsistency (Sec. VI-B, Fig. 7)","(inference) BALM itself provides only local refinement without loop closure, so large-loop drift is outside its scope",[33],"3D LiDAR",[35,36],"handheld","wheeled UGV","sliding-window local BA over scan poses with closed-form elimination of edge\u002Fplane parameters and analytical first\u002Fsecond-order derivatives (second-order approximation, LM-type iterations)","edge and plane feature points (LOAM-style extraction) grouped by adaptive voxelization from a default voxel size down to a minimal size (sizes given only as examples: 1 m and 0.125 m) using an eigenvalue test on the point covariance; separate voxel maps for edges and planes stored as hash-indexed octrees; voxels with repeated eigenvalues are skipped and dense voxels may average points per scan; scan-to-map odometry matches each point to the nearest voxel plane or edge instead of five nearest points (Sec. IV Remarks 1-4, Sec. V, Sec. VI-D)","discrete poses","not compensated in the scan-to-map odometry front-end (authors state this in Sec. VII)","none","none (local sliding-window BA as LOAM back-end map refinement)","adaptive voxel map of edge and plane features (hash table of octrees); points outside the window summarized by recursive covariance statistics","registered LiDAR feature point-cloud map and refined scan poses; no other exportable product reported","three parallel threads (feature extraction, 10 Hz scan-to-map odometry, map refinement); local BA over the 20 most recent scans triggered every 5 scans (2 Hz map output); laptop CPU i7-10750H with 16 GiB memory; local BA plus voxel-map update completes within 100 ms in most cases, so it can nearly keep pace with 10 Hz odometry (Sec. V, VI, VI-D, Fig. 9b)","https:\u002F\u002Fgithub.com\u002Fhku-mars\u002FBALM","GPL-2.0 (LICENSE file in repository; repository now hosts BALM 2.0)",[49],{"relation":50,"title":51,"doi_or_url":46},"code_release","hku-mars\u002FBALM repository (now hosts BALM 2.0; original BALM 1.0 code version not verified)",{"id":5,"kind":53,"shortName":7,"title":8,"authors":54,"year":9,"venue":57,"venueType":58,"publisher":59,"volumeIssuePages":60,"doi":61,"arxivId":62,"url":63,"firstPublicDate":64,"publicationStatus":16,"metadataStatus":65,"fulltextStatus":15,"era":10,"classicReason":66,"codeUrl":46,"cluster":11,"topics":67,"mdpi":68,"verification":69,"label":6,"fulltextRoute":70,"versionRead":71,"addedByCensus":68},"method",[55,56],"Zheng Liu","Fu Zhang","IEEE Robotics and Automation Letters","journal","IEEE","6(2):3184-3191","10.1109\u002Flra.2021.3062815","2010.08215","https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.08215","2020-10-16","metadata_verified","not_applicable",[11],false,"confirmed","arXiv","arXiv v2 (2021-01-13); IEEE RA-L version of record 6(2):3184-3191 not read",[73,80,85,91,95],{"category":74,"model":75,"canonical":75,"role":76,"dataset":77,"specs":78,"locator":79},"lidar","Livox Horizon","method input",null,"25 deg x 82 deg FoV; handheld","Sec. VI-A",{"category":74,"model":81,"canonical":82,"role":76,"dataset":77,"specs":83,"locator":84},"Livox Mid-40","Livox MID40","small 40 deg FoV; mounted on a UGV","Sec. VI-B",{"category":74,"model":86,"canonical":86,"role":87,"dataset":88,"specs":89,"locator":90},"Velodyne VLP-16","dataset sensor","LeGO-LOAM VLP-16 sample data","sample data offered by LeGO-LOAM on GitHub","Sec. VI-C",{"category":92,"model":93,"canonical":93,"role":76,"dataset":77,"specs":94,"locator":84},"platform","UGV (model not reported)","carries the Livox Mid-40 in the indoor corridor test",{"category":96,"model":97,"canonical":97,"role":98,"dataset":77,"specs":99,"locator":100},"compute","i7-10750H","compute for runtime","laptop computer with CPU i7-10750H and 16 GiB memory; all experiments","Sec. VI",[],{"totalRows":103,"groupCount":104,"groups":105,"others":716},40,12,[106,444,575,656],{"slug":107,"group":108,"sourceId":109,"sourceLabel":110,"table":111,"selfRows":112,"metrics":113,"seqs":121,"entrants":170,"cells":196,"outcomes":438,"locators":439,"hardware":440,"wordings":441,"notes":442},"balm2-2023-table-ii","balm2_2023:Table II","balm2_2023","Liu et al., 2023a","Table II",20,[114,119],{"label":115,"unit":116,"statistic":117,"alignment":118},"Absolute trajectory error (RMSE, meters)","m","RMSE","not_reported",{"label":120,"unit":116,"statistic":117,"alignment":118},"Absolute trajectory error (RMSE, meters), average over 19 sequences",[122,126,128,130,132,134,136,140,144,146,148,150,152,154,156,158,160,164,166,168],{"dataset":123,"sequence":124,"environment":125},"Hilti SLAM Challenge 2021","Basement1","handheld (Ouster OS0-64), Hilti indoor or outdoor sequence",{"dataset":123,"sequence":127,"environment":125},"Basement4",{"dataset":123,"sequence":129,"environment":125},"Campus2",{"dataset":123,"sequence":131,"environment":125},"Construction2",{"dataset":123,"sequence":133,"environment":125},"LabSurvey2",{"dataset":123,"sequence":135,"environment":125},"UzhArea2",{"dataset":137,"sequence":138,"environment":139},"Hilti 2021, VIRAL and UrbanLoco (19 sequences)","Average","mixed: handheld, UAV, car",{"dataset":141,"sequence":142,"environment":143},"NTU VIRAL","eee01","UAV (horizontal 16-channel OS1)",{"dataset":141,"sequence":145,"environment":143},"eee02",{"dataset":141,"sequence":147,"environment":143},"eee03",{"dataset":141,"sequence":149,"environment":143},"nya01",{"dataset":141,"sequence":151,"environment":143},"nya02",{"dataset":141,"sequence":153,"environment":143},"nya03",{"dataset":141,"sequence":155,"environment":143},"sbs01",{"dataset":141,"sequence":157,"environment":143},"sbs02",{"dataset":141,"sequence":159,"environment":143},"sbs03",{"dataset":161,"sequence":162,"environment":163},"UrbanLoco","0117","car on urban streets (Velodyne HDL 32E)",{"dataset":161,"sequence":165,"environment":163},"0317",{"dataset":161,"sequence":167,"environment":163},"0426-1",{"dataset":161,"sequence":169,"environment":163},"0426-2",[171,175,178,180,183,184,186,188,190,192,194],{"name":172,"methodId":173,"linkable":174,"proposed":68,"self":68},"ICP (PCL, incremental)","besl1992icp",true,{"name":176,"methodId":177,"linkable":174,"proposed":68,"self":68},"GICP (PCL, incremental)","segal2009gicp",{"name":179,"methodId":77,"linkable":68,"proposed":68,"self":68},"NDT (PCL, incremental)",{"name":181,"methodId":182,"linkable":174,"proposed":68,"self":68},"EF","eigenfactors2019",{"name":7,"methodId":5,"linkable":174,"proposed":68,"self":174},{"name":185,"methodId":77,"linkable":68,"proposed":68,"self":68},"PA",{"name":187,"methodId":77,"linkable":68,"proposed":68,"self":68},"PA (inner)",{"name":189,"methodId":77,"linkable":68,"proposed":68,"self":68},"BAREG",{"name":191,"methodId":109,"linkable":174,"proposed":174,"self":68},"Ours (float)",{"name":193,"methodId":109,"linkable":174,"proposed":174,"self":68},"Ours (edge)",{"name":195,"methodId":109,"linkable":174,"proposed":174,"self":68},"Ours",[197,201,204,207,210,213,216,219,222,225,228,231,233,235,237,239,240,242,244,246,248,250,252,254,256,258,260,262,263,264,265,267,269,271,273,275,277,279,281,283,285,286,288,290,292,293,295,297,299,301,303,304,306,308,310,312,314,316,318,320,322,324,326,328,330,332,334,336,338,340,342,344,346,348,350,352,354,356,358,360,362,364,366,368,369,371,373,375,377,379,382,384,386,388,390,392,395,397,399,402,403,405,407,408,410,413,415,417,420,422,424,427,429,431,434,436],[198,198,198,199,200,198,200,200,198],0,0.058,-1,[202,198,198,203,200,198,200,200,198],1,0.063,[205,198,198,206,200,198,200,200,198],2,0.076,[208,198,198,209,200,198,200,200,198],3,0.047,[211,198,198,212,200,198,200,200,198],4,0.042,[214,198,198,215,200,198,200,200,198],5,0.038,[217,198,198,218,200,198,200,200,198],6,0.036,[220,198,198,221,200,198,200,200,198],7,0.04,[223,198,198,224,200,198,200,200,198],8,0.0359,[226,198,198,227,200,198,200,200,198],9,0.0361,[229,198,198,230,200,198,200,200,198],10,0.0353,[198,198,202,232,200,198,200,200,198],0.084,[202,198,202,234,200,198,200,200,198],0.089,[205,198,202,236,200,198,200,200,198],0.098,[208,198,202,238,200,198,200,200,198],0.071,[211,198,202,199,200,198,200,200,198],[214,198,202,241,200,198,200,200,198],0.048,[217,198,202,243,200,198,200,200,198],0.045,[220,198,202,245,200,198,200,200,198],0.054,[223,198,202,247,200,198,200,200,198],0.0444,[226,198,202,249,200,198,200,200,198],0.0448,[229,198,202,251,200,198,200,200,198],0.0443,[198,198,205,253,200,198,200,200,198],0.105,[202,198,205,255,200,198,200,200,198],0.109,[205,198,205,257,200,198,200,200,198],0.124,[208,198,205,259,200,198,200,200,198],0.08,[211,198,205,261,200,198,200,200,198],0.066,[214,198,205,199,200,198,200,200,198],[217,198,205,245,200,198,200,200,198],[220,198,205,203,200,198,200,200,198],[223,198,205,266,200,198,200,200,198],0.0535,[226,198,205,268,200,198,200,200,198],0.053,[229,198,205,270,200,198,200,200,198],0.0531,[198,198,208,272,200,198,200,200,198],0.108,[202,198,208,274,200,198,200,200,198],0.104,[205,198,208,276,200,198,200,200,198],0.113,[208,198,208,278,200,198,200,200,198],0.086,[211,198,208,280,200,198,200,200,198],0.068,[214,198,208,282,200,198,200,200,198],0.06,[217,198,208,284,200,198,200,200,198],0.059,[220,198,208,203,200,198,200,200,198],[223,198,208,287,200,198,200,200,198],0.0563,[226,198,208,289,200,198,200,200,198],0.0577,[229,198,208,291,200,198,200,200,198],0.0553,[198,198,211,261,200,198,200,200,198],[202,198,211,294,200,198,200,200,198],0.069,[205,198,211,296,200,198,200,200,198],0.072,[208,198,211,298,200,198,200,200,198],0.046,[211,198,211,300,200,198,200,200,198],0.025,[214,198,211,302,200,198,200,200,198],0.019,[217,198,211,302,200,198,200,200,198],[220,198,211,305,200,198,200,200,198],0.023,[223,198,211,307,200,198,200,200,198],0.0185,[226,198,211,309,200,198,200,200,198],0.0189,[229,198,211,311,200,198,200,200,198],0.0181,[198,198,214,313,200,198,200,200,198],0.182,[202,198,214,315,200,198,200,200,198],0.191,[205,198,214,317,200,198,200,200,198],0.211,[208,198,214,319,200,198,200,200,198],0.161,[211,198,214,321,200,198,200,200,198],0.141,[214,198,214,323,200,198,200,200,198],0.122,[217,198,214,325,200,198,200,200,198],0.121,[220,198,214,327,200,198,200,200,198],0.127,[223,198,214,329,200,198,200,200,198],0.1205,[226,198,214,331,200,198,200,200,198],0.1102,[229,198,214,333,200,198,200,200,198],0.1171,[198,202,217,335,200,198,200,200,198],0.411,[202,202,217,337,200,198,200,200,198],0.41,[205,202,217,339,200,198,200,200,198],0.412,[208,202,217,341,200,198,200,200,198],0.268,[211,202,217,343,200,198,200,200,198],0.221,[214,202,217,345,200,198,200,200,198],0.186,[217,202,217,347,200,198,200,200,198],0.179,[220,202,217,349,200,198,200,200,198],0.203,[223,202,217,351,200,198,200,200,198],0.1775,[226,202,217,353,200,198,200,200,198],0.1826,[229,202,217,355,200,198,200,200,198],0.1763,[208,198,220,357,200,198,200,200,198],0.102,[211,198,220,359,200,198,200,200,198],0.073,[229,198,220,361,200,198,200,200,198],0.0382,[208,198,223,363,200,198,200,200,198],0.092,[211,198,223,365,200,198,200,200,198],0.062,[229,198,223,367,200,198,200,200,198],0.0356,[208,198,226,276,200,198,200,200,198],[211,198,226,370,200,198,200,200,198],0.081,[229,198,226,372,200,198,200,200,198],0.0517,[208,198,229,374,200,198,200,200,198],0.107,[211,198,229,376,200,198,200,200,198],0.082,[229,198,229,378,200,198,200,200,198],0.0362,[208,198,380,381,200,198,200,200,198],11,0.097,[211,198,380,383,200,198,200,200,198],0.067,[229,198,380,385,200,198,200,200,198],0.0468,[208,198,104,387,200,198,200,200,198],0.085,[211,198,104,389,200,198,200,200,198],0.074,[229,198,104,391,200,198,200,200,198],0.0413,[208,198,393,394,200,198,200,200,198],13,0.083,[211,198,393,396,200,198,200,200,198],0.077,[229,198,393,398,200,198,200,200,198],0.0385,[208,198,400,401,200,198,200,200,198],14,0.094,[211,198,400,365,200,198,200,200,198],[229,198,400,404,200,198,200,200,198],0.0377,[208,198,406,272,200,198,200,200,198],15,[211,198,406,296,200,198,200,200,198],[229,198,406,409,200,198,200,200,198],0.0427,[208,198,411,412,200,198,200,200,198],16,0.728,[211,198,411,414,200,198,200,200,198],0.625,[229,198,411,416,200,198,200,200,198],0.4956,[208,198,418,419,200,198,200,200,198],17,0.878,[211,198,418,421,200,198,200,200,198],0.732,[229,198,418,423,200,198,200,200,198],0.6488,[208,198,425,426,200,198,200,200,198],18,1.014,[211,198,425,428,200,198,200,200,198],0.875,[229,198,425,430,200,198,200,200,198],0.6886,[208,198,432,433,200,198,200,200,198],19,1.113,[211,198,432,435,200,198,200,200,198],0.924,[229,198,432,437,200,198,200,200,198],0.8223,[],[111],[],[],[443],"ATE RMSE (m) of multi-view registration; scans deskewed by FAST-LIO2 (odometry output discarded) and downsampled from 10 Hz to 2 Hz; ICP, GICP, NDT from PCL run incrementally against the last 20 scans; the ICP trajectory is the common initialization and adaptive voxelization (root voxel 1 m Hilti, 2 m VIRAL and UrbanLoco) the common association for EF, BALM, PA and Ours (BAREG uses its own); plane features only except Ours (edge); ground truth: Hilti total station or motion capture, VIRAL Leica Nova MS60, UrbanLoco Novatel SPAN-CPT RTK\u002FINS. Column order verified from PDF layout: Ours (float), Ours (edge), Ours",{"slug":445,"group":446,"sourceId":447,"sourceLabel":448,"table":449,"selfRows":211,"metrics":450,"seqs":458,"entrants":469,"cells":496,"outcomes":567,"locators":569,"hardware":570,"wordings":571,"notes":572},"yan2026tunnel-table-2","yan2026tunnel:Table 2","yan2026tunnel","Yan et al., 2026a","Table 2",[451,453,455],{"label":452,"unit":116,"statistic":117,"alignment":118},"ATE (m)",{"label":454,"unit":116,"statistic":117,"alignment":118},"Average ATE (m)",{"label":456,"unit":457,"statistic":117,"alignment":118},"Average RPE (%) per 100 m","% per 100 m",[459,463,466],{"dataset":460,"sequence":461,"environment":462},"WHU-Helmet (WHUH)","Tunnel","WHU-Helmet Tunnel sequence, 790.32 m, 1403 s",{"dataset":460,"sequence":464,"environment":465},"Subway","WHU-Helmet Subway sequence, 854.24 m, 1580 s",{"dataset":460,"sequence":467,"environment":468},"Average (Tunnel, Subway)","WHU-Helmet Tunnel and Subway",[470,473,476,479,480,483,486,489,492,494],{"name":471,"methodId":472,"linkable":174,"proposed":68,"self":68},"ORB-SLAM3","orbslam3_2021",{"name":474,"methodId":475,"linkable":174,"proposed":68,"self":68},"VINS-Mono","vinsmono2018",{"name":477,"methodId":478,"linkable":174,"proposed":68,"self":68},"LOAM","loam2014",{"name":7,"methodId":5,"linkable":174,"proposed":68,"self":174},{"name":481,"methodId":482,"linkable":174,"proposed":68,"self":68},"LIO-Mapping","liomapping2019",{"name":484,"methodId":485,"linkable":174,"proposed":68,"self":68},"Fast-LIO2","fastlio2_2022",{"name":487,"methodId":488,"linkable":174,"proposed":68,"self":68},"R3live++","r3livepp2024",{"name":490,"methodId":491,"linkable":174,"proposed":68,"self":68},"COIN-LIO","coinlio2024",{"name":493,"methodId":77,"linkable":68,"proposed":68,"self":68},"VINS-FEN",{"name":495,"methodId":447,"linkable":174,"proposed":174,"self":68},"This work",[497,499,500,501,503,505,507,509,511,513,515,517,519,521,523,525,527,529,531,533,535,537,539,541,543,545,547,549,551,553,555,557,559,561,563,565],[198,198,198,498,200,198,200,200,198],5.92,[202,198,198,77,198,198,200,200,198],[205,198,198,77,198,198,200,200,198],[208,198,198,502,200,198,200,200,198],4.53,[211,198,198,504,200,198,200,200,198],5.95,[214,198,198,506,200,198,200,200,198],4.21,[217,198,198,508,200,198,200,200,198],5.3,[220,198,198,510,200,198,200,200,198],4.05,[223,198,198,512,200,198,200,200,198],4.39,[226,198,198,514,200,198,200,200,198],4.04,[198,198,202,516,200,198,200,200,198],6.25,[202,198,202,518,200,198,200,200,198],6.18,[205,198,202,520,200,198,200,200,198],10.37,[208,198,202,522,200,198,200,200,198],4.38,[211,198,202,524,200,198,200,200,198],7.92,[214,198,202,526,200,198,200,200,198],5.29,[217,198,202,528,200,198,200,200,198],8.51,[220,198,202,530,200,198,200,200,198],4.19,[223,198,202,532,200,198,200,200,198],4.77,[226,198,202,534,200,198,200,200,198],3.44,[198,202,205,536,200,198,200,200,202],6.09,[198,205,205,538,200,198,200,200,202],7.58,[208,202,205,540,200,198,200,200,202],4.46,[208,205,205,542,200,198,200,200,202],2.54,[211,202,205,544,200,198,200,200,202],6.94,[211,205,205,546,200,198,200,200,202],3.34,[214,202,205,548,200,198,200,200,202],4.75,[214,205,205,550,200,198,200,200,202],2.35,[217,202,205,552,200,198,200,200,202],6.91,[217,205,205,554,200,198,200,200,202],2.95,[220,202,205,556,200,198,200,200,202],4.12,[220,205,205,558,200,198,200,200,202],2.22,[223,202,205,560,200,198,200,200,202],4.58,[223,205,205,562,200,198,200,200,202],2.3,[226,202,205,564,200,198,200,200,202],3.74,[226,205,205,566,200,198,200,200,202],1.93,[568],"failed",[449],[],[],[573,574],"ATE (RMSE, m) on WHU-Helmet with dataset ground-truth trajectory; LiDAR baselines use variants adapted to the Livox configuration; 'Failed' = failure to run","ATE (RMSE, m) on WHU-Helmet with dataset ground-truth trajectory; LiDAR baselines use variants adapted to the Livox configuration; 'Failed' = failure to run; averages only for methods that ran on both sequences",{"slug":576,"group":577,"sourceId":578,"sourceLabel":579,"table":580,"selfRows":211,"metrics":581,"seqs":585,"entrants":596,"cells":609,"outcomes":649,"locators":651,"hardware":652,"wordings":653,"notes":654},"zhou2021planeadjust-table-i","zhou2021planeadjust:Table I","zhou2021planeadjust","Zhou et al., 2021","Table I",[582],{"label":583,"unit":116,"statistic":584,"alignment":118},"keyframe ATE (m), median of 5 runs","median",[586,590,592,594],{"dataset":587,"sequence":588,"environment":589},"own indoor datasets A-D (NavVis M6)","A (261.9 m)","building interior, mobile mapping device",{"dataset":587,"sequence":591,"environment":589},"B (294.0 m)",{"dataset":587,"sequence":593,"environment":589},"C (391.7 m)",{"dataset":587,"sequence":595,"environment":589},"D (139.7 m)",[597,600,602,604,606,608],{"name":598,"methodId":599,"linkable":174,"proposed":68,"self":68},"LeGO-LOAM [8]","legoloam2018",{"name":601,"methodId":5,"linkable":174,"proposed":68,"self":174},"BALM [29]",{"name":603,"methodId":77,"linkable":68,"proposed":68,"self":68},"pi-LSAM [22]",{"name":605,"methodId":578,"linkable":174,"proposed":174,"self":68},"Ours - LPA - GPA",{"name":607,"methodId":578,"linkable":174,"proposed":174,"self":68},"Ours - GPA",{"name":195,"methodId":578,"linkable":174,"proposed":174,"self":68},[610,611,613,614,615,617,619,620,622,623,625,627,629,631,633,635,637,639,641,642,643,645,646,648],[198,198,198,77,198,198,200,200,198],[198,198,202,612,200,198,200,200,198],1.33,[198,198,205,77,198,198,200,200,198],[198,198,208,77,198,198,200,200,198],[202,198,198,616,200,198,200,200,198],0.34,[202,198,202,618,200,198,200,200,198],0.21,[202,198,205,77,198,198,200,200,198],[202,198,208,621,200,198,200,200,198],0.23,[205,198,198,376,200,198,200,200,198],[205,198,202,624,200,198,200,200,198],0.16,[205,198,205,626,200,198,200,200,198],0.13,[205,198,208,628,200,198,200,200,198],0.11,[208,198,198,630,200,198,200,200,198],0.29,[208,198,202,632,200,198,200,200,198],0.25,[208,198,205,634,200,198,200,200,198],0.24,[208,198,208,636,200,198,200,200,198],0.46,[211,198,198,638,200,198,200,200,198],0.18,[211,198,202,640,200,198,200,200,198],0.14,[211,198,205,628,200,198,200,200,198],[211,198,208,624,200,198,200,200,198],[214,198,198,644,200,198,200,200,198],0.039,[214,198,202,212,200,198,200,200,198],[214,198,205,647,200,198,200,200,198],0.033,[214,198,208,241,200,198,200,200,198],[650],"failed (did not complete the sequence)",[580],[],[],[655],"Keyframe ATE (m), median of 5 runs, on four indoor NavVis M6 datasets (VLP-16 data only) with large rotations; ground truth = offline fused NavVis trajectory; '-' = failed to complete",{"slug":657,"group":658,"sourceId":109,"sourceLabel":110,"table":659,"selfRows":205,"metrics":660,"seqs":663,"entrants":667,"cells":676,"outcomes":709,"locators":710,"hardware":711,"wordings":713,"notes":714},"balm2-2023-table-iv","balm2_2023:Table IV","Table IV",[661],{"label":662,"unit":118,"statistic":118,"alignment":41},"Optimization time (total, unit not stated)",[664,666],{"dataset":123,"sequence":131,"environment":665},"handheld (Ouster OS0-64), construction sequence",{"dataset":137,"sequence":138,"environment":139},[668,669,670,671,672,673,674,675],{"name":181,"methodId":182,"linkable":174,"proposed":68,"self":68},{"name":7,"methodId":5,"linkable":174,"proposed":68,"self":174},{"name":185,"methodId":77,"linkable":68,"proposed":68,"self":68},{"name":187,"methodId":77,"linkable":68,"proposed":68,"self":68},{"name":189,"methodId":77,"linkable":68,"proposed":68,"self":68},{"name":191,"methodId":109,"linkable":174,"proposed":174,"self":68},{"name":193,"methodId":109,"linkable":174,"proposed":174,"self":68},{"name":195,"methodId":109,"linkable":174,"proposed":174,"self":68},[677,679,681,683,685,687,689,691,693,695,697,699,701,703,705,707],[198,198,198,678,200,198,198,200,198],1415.18,[202,198,198,680,200,198,198,200,198],412,[205,198,198,682,200,198,198,200,198],335.7,[208,198,198,684,200,198,198,200,198],313.23,[211,198,198,686,200,198,198,200,198],231.48,[214,198,198,688,200,198,198,200,198],33.04,[217,198,198,690,200,198,198,200,198],47.34,[220,198,198,692,200,198,198,200,198],47.12,[198,198,202,694,200,198,198,200,198],647.29,[202,198,202,696,200,198,198,200,198],232.54,[205,198,202,698,200,198,198,200,198],202.64,[208,198,202,700,200,198,198,200,198],171.11,[211,198,202,702,200,198,198,200,198],132.1,[214,198,202,704,200,198,198,200,198],18.15,[217,198,202,706,200,198,198,200,198],31.16,[220,198,202,708,200,198,198,200,198],30.58,[],[659],[712],"desktop Intel i7-10750H, 16 GB RAM",[],[715],"Total optimization time of the BA methods on the Table II inputs (pairwise methods excluded); the table does not state the time unit; desktop Intel i7-10750H, 16 GB RAM (Sec. 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