[{"data":1,"prerenderedAt":1558},["ShallowReactive",2],{"method-liosam2020":3},{"method":4,"reference":60,"equipment":83,"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":22,"limitations":26,"sensors":32,"platform":36,"estimator":40,"association":41,"timeModel":42,"deskew":43,"loopClosure":44,"globalOptimization":45,"mapRepresentation":46,"prior":47,"outputGeometry":48,"compute":49,"codeUrl":50,"codeLicense":51,"relatedVersions":52},"liosam2020","Shan et al., 2020","LIO-SAM","LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping",2020,"recent","C04","full_slam_with_global_correction","LIO-SAM 把 LiDAR 慣性里程計建構在因子圖（factor graph）上，以 iSAM2 增量最佳化 IMU 預積分、LiDAR 里程計、GNSS 與迴圈閉合四種因子，形成緊耦合（tightly-coupled）系統。IMU 積分的運動用來對點雲去畸變並提供掃描配準初值，LiDAR 里程計結果再回饋估計 IMU 偏差。為維持即時性，新關鍵影格只與固定數量的近期子關鍵影格（sub-keyframes）組成的局部體素地圖配準，而非與整張全域地圖配準；迴圈以歐氏距離搜尋候選並以掃描配準建立約束。","LIO-SAM fuses IMU preintegration, LOAM-style keyframe scan matching against a sliding local map, optional GPS and loop-closure factors in an iSAM2 factor graph, using the IMU for de-skewing and initial guesses.","full_text_reviewed","peer_reviewed_published","main_body","原論文在 MIT 校園手持、公園 UGV 與阿姆斯特丹運河船載資料中測試，未含營建工地。Feng 等人 [feng2025_construction_lidar_eval] 在施工中醫院門診大樓（RS-Helios-16P 與九軸 IMU 的施工機器人平台）以預設參數測試，作者報告 LIO-SAM 在十種方法中實際工地 APE RMSE 最低（2.26 m，Sec. 5.3, Table 4）；在模擬工地中，作者指出其前視圖仍可見垂直漂移造成的結構傾斜（Sec. 5.2, Fig. 13），並於結論指出緊耦合 IMU、地面約束與迴圈閉合不能完全消除垂直漂移。論文未說明實際工地 APE 所用參考軌跡的來源（全文僅描述 Gazebo 模擬的真實軌跡外掛），故實際工地 APE 只能視為作者報告值，不能當作已驗證的幾何精度。",[20,21],"cross_site","independent_reference",[23,24,25],"End-to-end translation error when returning to start: 0.12 m (Campus), 0.04 m (Park), 0.17 m (Amsterdam), with LOAM and LIOM failing or drifting in the same tests (Sec. IV-C to IV-E, Table II)","Park dataset RMSE w.r.t. GPS (x-y only) 0.96 m with loop closure and partial GPS use (Sec. IV-F, Table III)","Runtime depends mainly on feature map density rather than graph size (Sec. IV-F)",[27,28,29,30,31],"Without absolute measurements or loop closures, lidar-inertial odometry still drifts over long durations (Sec. III-D, V)","GPS elevation was very inaccurate (altitude errors approaching 100 m without loop closures) (Sec. III-E)","Sunlight-induced false lidar returns and degenerate scenes under bridges caused other methods to fail in the Amsterdam test (Sec. IV-E)","Accuracy evidence is limited to end-to-start return errors and an x-y RMSE against partially used GPS; no independent survey-grade trajectory or map reference (Sec. IV-C to IV-F)","Follow-up work states LIO-SAM needs nine-axis IMU readings for de-skewing and front-end odometry and could not be run on a six-axis-IMU dataset (liliom2021, Sec. 2, 5.2)",[33,34,35],"3D LiDAR (Velodyne VLP-16)","IMU (MicroStrain 3DM-GX5-25)","GNSS (Reach M, optional)",[37,38,39],"handheld","wheeled UGV (Clearpath Jackal)","boat (Duffy 21)","factor graph smoothing with iSAM2: IMU preintegration, lidar odometry, GPS and loop-closure factors; scan matching solved by Gauss-Newton (Sec. III)","LOAM-style edge\u002Fplanar features matched to a local voxel map built from n=25 recent sub-keyframes (point-to-edge \u002F point-to-plane) (Sec. III-C)","discrete keyframe states (keyframe added at 1 m or 10 deg pose change) (Sec. III-A, III-C)","IMU-estimated nonlinear motion de-skews each scan and gives the initial guess for scan matching (Sec. I, abstract)","Euclidean-distance candidate search within 15 m, scan matching of the new keyframe to +\u002F-12 sub-keyframes around the candidate, added as a loop factor; compatible with descriptor-based place recognition (Sec. III-E)","incremental factor-graph optimization (iSAM2) including GPS factors when estimated position covariance exceeds GPS covariance (Sec. III-D)","keyframe edge\u002Fplanar feature clouds; local voxel maps downsampled at 0.2 m (edge) and 0.4 m (planar) (Sec. III-C)","GNSS (optional); no prior map","global feature map assembled from keyframe edge and planar feature clouds plus the keyframe trajectory; lidar frames between keyframes (1 m or 10 deg pose change) are discarded; maps are shown aligned with Google Earth imagery (Figs. 4 to 7); dense map export not described","CPU only, Intel i7-10710U laptop; mapping runtime per scan 41.9-100.5 ms across datasets; up to 13x real-time playback in stress tests (Table IV)","https:\u002F\u002Fgithub.com\u002FTixiaoShan\u002FLIO-SAM","BSD 3-Clause (LICENSE file)",[53,57],{"relation":54,"title":55,"doi_or_url":56},"preprint","arXiv 2007.00258 (v1 2020-07-01, v3 2020-07-14)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.00258",{"relation":58,"title":59,"doi_or_url":50},"code_release","TixiaoShan\u002FLIO-SAM",{"id":5,"kind":61,"shortName":7,"title":8,"authors":62,"year":9,"venue":69,"venueType":70,"publisher":71,"volumeIssuePages":72,"doi":73,"arxivId":74,"url":56,"firstPublicDate":75,"publicationStatus":16,"metadataStatus":76,"fulltextStatus":15,"era":10,"classicReason":77,"codeUrl":50,"cluster":11,"topics":78,"mdpi":79,"verification":80,"label":6,"fulltextRoute":81,"versionRead":82,"addedByCensus":79},"method",[63,64,65,66,67,68],"Tixiao Shan","Brendan Englot","Drew Meyers","Wei Wang","Carlo Ratti","Daniela Rus","2020 IEEE\u002FRSJ International Conference on Intelligent Robots and Systems (IROS)","conference","IEEE","pp. 5135-5142","10.1109\u002Firos45743.2020.9341176","2007.00258","2020-07-01","metadata_verified","not_applicable",[11],false,"corrected","arXiv","arXiv 2007.00258v3 (2020-07-14); not compared with the IROS 2020 version of record",[84,91,96,100,105,109,113],{"category":85,"model":86,"canonical":86,"role":87,"dataset":88,"specs":89,"locator":90},"lidar","Velodyne VLP-16","method input",null,"10 Hz rotation rate in the runtime discussion","Sec. IV; Sec. IV-F",{"category":92,"model":93,"canonical":93,"role":87,"dataset":88,"specs":94,"locator":95},"imu","MicroStrain 3DM-GX5-25","not_reported","Sec. IV",{"category":97,"model":98,"canonical":98,"role":87,"dataset":88,"specs":99,"locator":90},"gnss","Reach M","optional GPS factor; also the RMSE reference in the Park dataset",{"category":101,"model":102,"canonical":102,"role":87,"dataset":88,"specs":103,"locator":104},"platform","custom-built handheld device","Rotation, Walking and Campus datasets on the MIT campus","Sec. IV; Fig. 2a",{"category":101,"model":106,"canonical":106,"role":87,"dataset":88,"specs":107,"locator":108},"Clearpath Jackal","UGV without suspension; Park dataset on a forested hiking trail","Sec. IV; Sec. IV-D; Fig. 2b",{"category":101,"model":110,"canonical":110,"role":87,"dataset":88,"specs":111,"locator":112},"Duffy 21","electric boat; Amsterdam canal dataset (about 3 h)","Sec. IV; Sec. IV-E; Fig. 2c",{"category":114,"model":115,"canonical":115,"role":116,"dataset":88,"specs":117,"locator":95},"compute","laptop with Intel i7-10710U","compute for runtime","CPU only, no parallel computing; ROS in Ubuntu",[],{"totalRows":120,"groupCount":121,"groups":122,"others":1152},582,75,[123,371,728,925],{"slug":124,"group":125,"sourceId":126,"sourceLabel":127,"table":128,"selfRows":129,"metrics":130,"seqs":144,"entrants":167,"cells":175,"outcomes":364,"locators":366,"hardware":367,"wordings":368,"notes":369},"lioekf2024-table-i","lioekf2024:Table I","lioekf2024","Wu et al., 2024a","Table I",32,[131,135,138,141],{"label":132,"unit":133,"statistic":134,"alignment":94},"Avg. tra. (KITTI relative translation error)","%","mean",{"label":136,"unit":137,"statistic":134,"alignment":94},"Avg. rot. (KITTI relative rotation error)","deg\u002Fm (as printed)",{"label":139,"unit":140,"statistic":94,"alignment":94},"ATE. tra. (unit taken as m from the column name; the Table I footnote lists the two ATE units in swapped order)","m",{"label":142,"unit":143,"statistic":94,"alignment":94},"ATE. rot. (unit taken as deg from the column name; the Table I footnote lists the two ATE units in swapped order)","deg",[145,149,151,153,157,159,161,165],{"dataset":146,"sequence":147,"environment":148},"UrbanNav","20210517","urban driving, Hong Kong",{"dataset":146,"sequence":150,"environment":148},"20210518",{"dataset":146,"sequence":152,"environment":148},"20210521",{"dataset":154,"sequence":155,"environment":156},"M2DGR","street 01-05 (average)","campus streets, wheeled robot",{"dataset":154,"sequence":158,"environment":156},"street 06",{"dataset":154,"sequence":160,"environment":156},"street 08",{"dataset":162,"sequence":163,"environment":164},"Newer College Dataset","short exp","handheld, Oxford college",{"dataset":162,"sequence":166,"environment":164},"long exp",[168,172,173],{"name":169,"methodId":170,"linkable":171,"proposed":79,"self":79},"FAST-LIO2","fastlio2_2022",true,{"name":7,"methodId":5,"linkable":171,"proposed":79,"self":171},{"name":174,"methodId":126,"linkable":171,"proposed":171,"self":79},"LIO-EKF",[176,180,183,186,189,191,193,195,197,199,201,203,205,207,209,211,213,215,217,219,221,223,225,227,229,231,233,235,237,239,241,243,245,247,249,251,253,254,256,257,259,261,263,265,267,268,270,272,274,277,279,281,283,285,287,289,291,293,295,297,299,302,303,305,307,309,311,313,315,317,319,321,323,326,328,330,332,333,334,335,336,338,340,342,344,347,349,351,353,354,355,356,357,359,361,362],[177,177,177,178,179,177,179,179,177],0,4.11,-1,[177,181,177,182,179,177,179,179,177],1,1.68,[177,184,177,185,179,177,179,179,177],2,17.62,[177,187,177,188,179,177,179,179,177],3,4.45,[181,177,177,190,179,177,179,179,177],3.18,[181,181,177,192,179,177,179,179,177],1.4,[181,184,177,194,179,177,179,179,177],20.74,[181,187,177,196,179,177,179,179,177],4.2,[184,177,177,198,179,177,179,179,177],3.2,[184,181,177,200,179,177,179,179,177],1.45,[184,184,177,202,179,177,179,179,177],24.73,[184,187,177,204,179,177,179,179,177],5.05,[177,177,181,206,179,177,179,179,177],2.73,[177,181,181,208,179,177,179,179,177],1.3,[177,184,181,210,179,177,179,179,177],23.02,[177,187,181,212,179,177,179,179,177],3.27,[181,177,181,214,179,177,179,179,177],2.52,[181,181,181,216,179,177,179,179,177],1.31,[181,184,181,218,179,177,179,179,177],20.37,[181,187,181,220,179,177,179,179,177],2.98,[184,177,181,222,179,177,179,179,177],2.2,[184,181,181,224,179,177,179,179,177],1.14,[184,184,181,226,179,177,179,179,177],22.44,[184,187,181,228,179,177,179,179,177],7.46,[177,177,184,230,179,177,179,179,177],3.56,[177,181,184,232,179,177,179,179,177],1.63,[177,184,184,234,179,177,179,179,177],47.29,[177,187,184,236,179,177,179,179,177],5.18,[181,177,184,238,179,177,179,179,177],2.94,[181,181,184,240,179,177,179,179,177],1.62,[181,184,184,242,179,177,179,179,177],30.98,[181,187,184,244,179,177,179,179,177],4.51,[184,177,184,246,179,177,179,179,177],2.96,[184,181,184,248,179,177,179,179,177],1.54,[184,184,184,250,179,177,179,179,177],34.97,[184,187,184,252,179,177,179,179,177],4.47,[177,177,187,240,179,177,179,179,177],[177,181,187,255,179,177,179,179,177],0.79,[177,184,187,204,179,177,179,179,177],[177,187,187,258,179,177,179,179,177],1.79,[181,177,187,260,179,177,179,179,177],3.15,[181,181,187,262,179,177,179,179,177],1.52,[181,184,187,264,179,177,179,179,177],10.19,[181,187,187,266,179,177,179,179,177],4.27,[184,177,187,182,179,177,179,179,177],[184,181,187,269,179,177,179,179,177],0.83,[184,184,187,271,179,177,179,179,177],5.33,[184,187,187,273,179,177,179,179,177],1.7,[177,177,275,276,179,177,179,179,177],4,3.41,[177,181,275,278,179,177,179,179,177],1.55,[177,184,275,280,179,177,179,179,177],8.93,[177,187,275,282,179,177,179,179,177],2.29,[181,177,275,284,179,177,179,179,177],3.65,[181,181,275,286,179,177,179,179,177],1.65,[181,184,275,288,179,177,179,179,177],9.04,[181,187,275,290,179,177,179,179,177],2.41,[184,177,275,292,179,177,179,179,177],3.37,[184,181,275,294,179,177,179,179,177],1.56,[184,184,275,296,179,177,179,179,177],9.05,[184,187,275,298,179,177,179,179,177],2.27,[177,177,300,301,179,177,179,179,177],5,1.1,[177,181,300,286,179,177,179,179,177],[177,184,300,304,179,177,179,179,177],2.12,[177,187,300,306,179,177,179,179,177],1.71,[181,177,300,308,179,177,179,179,177],3.73,[181,181,300,310,179,177,179,179,177],5.78,[181,184,300,312,179,177,179,179,177],4.21,[181,187,300,314,179,177,179,179,177],6.36,[184,177,300,316,179,177,179,179,177],1.28,[184,181,300,318,179,177,179,179,177],1.85,[184,184,300,320,179,177,179,179,177],2.22,[184,187,300,322,179,177,179,179,177],1.88,[177,177,324,325,179,177,179,179,177],6,1.05,[177,181,324,327,179,177,179,179,177],1.01,[177,184,324,329,179,177,179,179,177],5.14,[177,187,324,331,179,177,179,179,177],2.49,[181,177,324,88,177,177,179,179,177],[181,181,324,88,177,177,179,179,177],[181,184,324,88,177,177,179,179,177],[181,187,324,88,177,177,179,179,177],[184,177,324,337,179,177,179,179,177],0.63,[184,181,324,339,179,177,179,179,177],0.73,[184,184,324,341,179,177,179,179,177],4.16,[184,187,324,343,179,177,179,179,177],1.75,[177,177,345,346,179,177,179,179,177],7,1.09,[177,181,345,348,179,177,179,179,177],1.33,[177,184,345,350,179,177,179,179,177],6.33,[177,187,345,352,179,177,179,179,177],4.22,[181,177,345,88,177,177,179,179,177],[181,181,345,88,177,177,179,179,177],[181,184,345,88,177,177,179,179,177],[181,187,345,88,177,177,179,179,177],[184,177,345,358,179,177,179,179,177],0.74,[184,181,345,360,179,177,179,179,177],0.91,[184,184,345,329,179,177,179,179,177],[184,187,345,363,179,177,179,179,177],2.34,[365],"not_run",[128],[],[],[370],"Default parameters for FAST-LIO2 and LIO-SAM, LIO-SAM loop closure disabled; one LIO-EKF configuration for all data; KITTI relative errors and ATE; LIO-SAM not run on Newer College (needs IMU attitude)",{"slug":372,"group":373,"sourceId":374,"sourceLabel":375,"table":376,"selfRows":377,"metrics":378,"seqs":383,"entrants":438,"cells":453,"outcomes":690,"locators":692,"hardware":694,"wordings":695,"notes":726},"r3livepp2024-table-iii","r3livepp2024:Table III","r3livepp2024","Lin & Zhang, 2024","Table III",26,[379,381],{"label":380,"unit":140,"statistic":94,"alignment":94},"APE (m)",{"label":382,"unit":140,"statistic":94,"alignment":94},"APE (m) with STD",[384,388,390,392,394,396,398,400,402,404,406,408,410,412,414,416,418,420,422,424,426,428,430,432,434,436],{"dataset":385,"sequence":386,"environment":387},"NCLT","2012-01-08 (6495.7 m, 01:25:35)","University of Michigan North Campus, indoor and outdoor, all seasons (Segway robot)",{"dataset":385,"sequence":389,"environment":387},"2012-01-15 (7499.8 m, 01:52:19)",{"dataset":385,"sequence":391,"environment":387},"2012-01-22 (6183.1 m, 01:27:22)",{"dataset":385,"sequence":393,"environment":387},"2012-02-02 (6315.8 m, 01:38:36)",{"dataset":385,"sequence":395,"environment":387},"2012-02-04 (5641.0 m, 01:18:30)",{"dataset":385,"sequence":397,"environment":387},"2012-02-05 (6649.3 m, 01:34:17)",{"dataset":385,"sequence":399,"environment":387},"2012-02-12 (5829.1 m, 01:25:35)",{"dataset":385,"sequence":401,"environment":387},"2012-02-18 (6249.2 m, 01:29:55)",{"dataset":385,"sequence":403,"environment":387},"2012-02-19 (6232.7 m, 01:29:11)",{"dataset":385,"sequence":405,"environment":387},"2012-03-17 (5907.2 m, 01:22:53)",{"dataset":385,"sequence":407,"environment":387},"2012-03-31 (6073.7 m, 01:27:53)",{"dataset":385,"sequence":409,"environment":387},"2012-04-29 (3183.1 m, 00:43:18)",{"dataset":385,"sequence":411,"environment":387},"2012-05-11 (6116.7 m, 01:25:05)",{"dataset":385,"sequence":413,"environment":387},"2012-05-26 (6340.7 m, 01:28:34)",{"dataset":385,"sequence":415,"environment":387},"2012-06-15 (4085.9 m, 00:55:10)",{"dataset":385,"sequence":417,"environment":387},"2012-08-04 (5492.1 m, 01:20:32)",{"dataset":385,"sequence":419,"environment":387},"2012-08-20 (6014.5 m, 01:23:48)",{"dataset":385,"sequence":421,"environment":387},"2012-09-28 (5574.4 m, 01:17:59)",{"dataset":385,"sequence":423,"environment":387},"2012-10-28 (5682.1 m, 01:26:10)",{"dataset":385,"sequence":425,"environment":387},"2012-11-04 (4788.3 m, 01:20:39)",{"dataset":385,"sequence":427,"environment":387},"2012-11-17 (5751.9 m, 01:29:44)",{"dataset":385,"sequence":429,"environment":387},"2012-12-01 (4991.9 m, 01:16:48)",{"dataset":385,"sequence":431,"environment":387},"2013-01-10 (1137.3 m, 00:17:04)",{"dataset":385,"sequence":433,"environment":387},"2013-02-23 (5235.3 m, 01:20:08)",{"dataset":385,"sequence":435,"environment":387},"2013-04-05 (4523.7 m, 01:09:27)",{"dataset":385,"sequence":437,"environment":387},"Average",[439,441,444,447,450,452],{"name":440,"methodId":374,"linkable":171,"proposed":171,"self":79},"Our (R3LIVE++)",{"name":442,"methodId":443,"linkable":171,"proposed":79,"self":79},"R2LIVE","r2live2021",{"name":445,"methodId":446,"linkable":171,"proposed":79,"self":79},"LVI-SAM","lvisam2021",{"name":448,"methodId":449,"linkable":171,"proposed":79,"self":79},"FAST-LIVO","fastlivo2022",{"name":451,"methodId":170,"linkable":171,"proposed":79,"self":79},"Fast-LIO2",{"name":7,"methodId":5,"linkable":171,"proposed":79,"self":171},[454,456,458,460,462,464,466,467,468,470,472,474,477,480,482,485,488,491,492,495,497,500,503,506,508,510,512,513,514,517,519,521,524,525,527,528,529,531,533,535,538,540,541,543,545,546,547,549,551,553,554,556,558,560,562,564,566,567,569,570,572,574,576,577,578,580,581,583,584,586,588,589,592,593,594,596,597,598,600,601,604,605,606,608,609,610,612,614,615,617,618,620,622,623,624,626,628,630,632,634,635,637,638,640,642,644,646,648,650,653,654,655,656,657,658,659,660,662,664,666,667,668,670,672,673,675,676,678,679,680,681,682,684,685,687,688],[177,177,177,455,179,177,179,177,177],10.8,[181,177,177,457,179,177,179,181,177],22.4,[184,177,177,459,179,177,179,184,177],23.4,[187,177,177,461,179,177,179,187,177],13.4,[275,177,177,463,179,177,179,275,177],18.5,[300,177,177,465,179,177,179,300,177],21.7,[184,181,181,88,177,177,179,179,177],[300,181,181,88,177,177,179,179,177],[177,177,184,469,179,177,179,324,177],9.2,[181,177,184,471,179,177,179,177,177],12.6,[184,177,184,473,179,177,179,345,177],8.3,[187,177,184,475,179,177,179,476,177],14.1,8,[275,177,184,478,179,177,179,479,177],7.1,9,[300,177,184,479,179,177,179,481,177],10,[177,177,187,483,179,177,179,484,177],5.3,11,[181,177,187,486,179,177,179,487,177],6.1,12,[184,177,187,489,179,177,179,490,177],18.1,13,[187,177,187,487,179,177,179,324,177],[275,177,187,493,179,177,179,494,177],9.1,14,[300,177,187,496,179,177,179,481,177],15.6,[177,177,275,498,179,177,179,499,177],5.6,15,[181,177,275,501,179,177,179,502,177],8.4,16,[184,177,275,504,179,177,179,505,177],9.6,17,[187,177,275,507,179,177,179,499,177],6.2,[275,177,275,509,179,177,179,499,177],7.2,[300,177,275,455,179,177,179,511,177],18,[184,181,300,88,177,177,179,179,177],[300,181,300,88,177,177,179,179,177],[177,177,324,515,179,177,179,516,177],4.5,19,[181,177,324,518,179,177,179,502,177],6.5,[184,177,324,520,179,177,179,187,177],40,[187,177,324,522,179,177,179,523,177],16.4,20,[275,177,324,473,179,177,179,481,177],[300,177,324,526,179,177,179,345,177],45,[184,181,345,88,177,177,179,179,177],[300,181,345,88,177,177,179,179,177],[177,177,476,530,179,177,179,487,177],8.5,[181,177,476,532,179,177,179,516,177],6.6,[184,177,476,534,179,177,179,479,177],8.9,[187,177,476,536,179,177,179,537,177],8.6,21,[275,177,476,324,179,177,179,539,177],22,[300,177,476,504,179,177,179,499,177],[177,177,479,542,179,177,179,499,177],4.8,[181,177,479,544,179,177,179,487,177],5.9,[184,177,479,471,179,177,179,499,177],[187,177,479,483,179,177,179,479,177],[275,177,479,548,179,177,179,499,177],4.7,[300,177,479,550,179,177,179,499,177],11.8,[177,177,481,552,179,177,179,479,177],4.9,[181,177,481,481,179,177,179,324,177],[184,177,481,516,179,177,179,555,177],23,[187,177,481,557,179,177,179,487,177],5.2,[275,177,481,559,179,177,179,494,177],7.3,[300,177,481,561,179,177,179,555,177],18.3,[177,177,484,563,179,177,179,502,177],6.3,[181,177,484,565,179,177,179,487,177],6.4,[184,177,484,544,179,177,179,481,177],[187,177,484,568,179,177,179,502,177],7.7,[275,177,484,565,179,177,179,487,177],[300,177,484,571,179,177,179,502,177],5.7,[177,177,487,573,179,177,179,516,177],3.7,[181,177,487,575,179,177,179,516,177],3.8,[184,177,487,196,179,177,179,187,177],[187,177,487,552,179,177,179,479,177],[275,177,487,579,179,177,179,484,177],4.1,[300,177,487,196,179,177,179,187,177],[177,177,490,582,179,177,179,516,177],4.6,[181,177,490,563,179,177,179,479,177],[184,177,490,561,179,177,179,585,177],24,[187,177,490,587,179,177,179,537,177],7.4,[275,177,490,565,179,177,179,479,177],[300,177,490,590,179,177,179,591,177],18.4,25,[184,181,494,88,177,177,179,179,177],[300,181,494,88,177,177,179,179,177],[177,177,499,595,179,177,179,377,177],3.9,[181,177,499,573,179,177,179,377,177],[184,177,499,484,179,177,179,300,177],[187,177,499,300,179,177,179,599,177],27,[275,177,499,345,179,177,179,537,177],[300,177,499,602,179,177,179,603,177],12.7,28,[177,177,502,515,179,177,179,499,177],[181,177,502,515,179,177,179,499,177],[184,177,502,607,179,177,179,499,177],11.2,[187,177,502,300,179,177,179,479,177],[275,177,502,324,179,177,179,499,177],[300,177,502,611,179,177,179,499,177],11.4,[177,177,505,613,179,177,179,499,177],7.9,[181,177,505,532,179,177,179,484,177],[184,177,505,616,179,177,179,484,177],34.4,[187,177,505,536,179,177,179,516,177],[275,177,505,619,179,177,179,490,177],10.4,[300,177,505,621,179,177,179,499,177],36.7,[184,181,511,88,177,177,179,179,177],[300,181,511,88,177,177,179,179,177],[177,177,516,625,179,177,179,177,177],7.5,[181,177,516,627,179,177,179,476,177],9.3,[184,177,516,629,179,177,179,476,177],3.4,[187,177,516,631,179,177,179,345,177],8.1,[275,177,516,633,179,177,179,599,177],3.3,[300,177,516,629,179,177,179,345,177],[177,177,523,636,179,177,179,537,177],8.7,[181,177,523,518,179,177,179,484,177],[184,177,523,639,179,177,179,484,177],21.9,[187,177,523,641,179,177,179,490,177],8.2,[275,177,523,643,179,177,179,516,177],5.8,[300,177,523,645,179,177,179,490,177],24.2,[177,177,537,647,179,177,179,502,177],11.3,[181,177,537,649,179,177,179,490,177],14.2,[184,177,537,651,179,177,179,652,177],6.9,29,[187,177,537,550,179,177,179,502,177],[275,177,537,587,179,177,179,479,177],[300,177,537,509,179,177,179,177,177],[177,177,539,629,179,177,179,516,177],[181,177,539,582,179,177,179,479,177],[184,177,539,552,179,177,179,499,177],[187,177,539,573,179,177,179,484,177],[275,177,539,661,179,177,179,516,177],3.5,[300,177,539,663,179,177,179,479,177],5.1,[177,177,555,665,179,177,179,479,177],11.6,[181,177,555,461,179,177,179,537,177],[184,177,555,471,179,177,179,487,177],[187,177,555,669,179,177,179,487,177],12.2,[275,177,555,671,179,177,179,537,177],11.9,[300,177,555,669,179,177,179,523,177],[177,177,585,674,179,177,179,502,177],8.8,[181,177,585,671,179,177,179,481,177],[184,177,585,677,179,177,179,324,177],9.8,[187,177,585,619,179,177,179,490,177],[275,177,585,565,179,177,179,502,177],[300,177,585,479,179,177,179,324,177],[177,177,591,530,179,177,179,537,177],[181,177,591,683,179,177,179,494,177],10.6,[184,177,591,499,179,177,179,187,177],[187,177,591,686,179,177,179,490,177],10.3,[275,177,591,504,179,177,179,502,177],[300,177,591,689,179,177,179,476,177],15.4,[691],"failed",[693],"VoR Table III",[],[696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725],"APE (m), printed with STD 2.7 m","APE (m), printed with STD 3.5 m","APE (m), printed with STD 3.7 m","APE (m), printed with STD 2.9 m","APE (m), printed with STD 3.3 m","APE (m), printed with STD 3.6 m","APE (m), printed with STD 2.5 m","APE (m), printed with STD 2.8 m","APE (m), printed with STD 3.0 m","APE (m), printed with STD 1.9 m","APE (m), printed with STD 2.6 m","APE (m), printed with STD 1.7 m","APE (m), printed with STD 2.0 m","APE (m), printed with STD 2.4 m","APE (m), printed with STD 2.3 m","APE (m), printed with STD 1.8 m","APE (m), printed with STD 2.2 m","APE (m), printed with STD 5.0 m","APE (m), printed with STD 5.3 m","APE (m), printed with STD 1.6 m","APE (m), printed with STD 3.4 m","APE (m), printed with STD 2.1 m","APE (m), printed with STD 1.3 m","APE (m), printed with STD 3.2 m","APE (m), printed with STD 4.4 m","APE (m), printed with STD 4.5 m","APE (m), printed with STD 1.4 m","APE (m), printed with STD 1.5 m","APE (m), printed with STD 3.8 m","APE (m), printed with STD 3.1 m",[727],"VoR Table III: absolute position error (APE, m) with standard deviation on NCLT (front-facing camera and 3D LiDAR, Segway robot), computed on the odometry output at LiDAR input for every method; loop closure of LIO-SAM and LVI-SAM deactivated; photometric calibration disabled for R3LIVE++ (unavailable for NCLT); '-' = failed midway, excluded from the averages; Our_LIO column omitted. The text says 2012-03-17 and 2012-08-04 were excluded for a 100 ms LiDAR-IMU timestamp delay, yet both appear in the 25-row table.",{"slug":729,"group":730,"sourceId":731,"sourceLabel":732,"table":733,"selfRows":539,"metrics":734,"seqs":738,"entrants":783,"cells":788,"outcomes":919,"locators":920,"hardware":921,"wordings":922,"notes":923},"loglio2024-table-ii","loglio2024:Table II","loglio2024","Huang et al., 2024b","Table II",[735],{"label":736,"unit":140,"statistic":737,"alignment":94},"translation RMSE","RMSE",[739,742,744,746,748,750,752,754,756,758,760,762,764,766,768,770,772,774,776,778,780,782],{"dataset":154,"sequence":740,"environment":741},"gate01 (172 s)","ground robot; indoor door and hall sequences, outdoor gate and street sequences (street on wide campus roads at night); walk not classified in the text (Sec. 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s)",{"dataset":154,"sequence":771,"environment":741},"street10 (810 s)",{"dataset":154,"sequence":773,"environment":741},"hall01 (351 s)",{"dataset":154,"sequence":775,"environment":741},"hall02 (128 s)",{"dataset":154,"sequence":777,"environment":741},"hall03 (164 s)",{"dataset":154,"sequence":779,"environment":741},"hall04 (181 s)",{"dataset":154,"sequence":781,"environment":741},"hall05 (402 s)",{"dataset":154,"sequence":134,"environment":741},[784,786,787],{"name":785,"methodId":731,"linkable":171,"proposed":171,"self":79},"LOG-LIO",{"name":169,"methodId":170,"linkable":171,"proposed":79,"self":79},{"name":7,"methodId":5,"linkable":171,"proposed":79,"self":171},[789,791,793,795,797,799,801,803,805,806,808,810,812,814,816,818,820,822,824,826,828,830,832,834,836,838,840,842,844,846,848,850,852,854,856,858,860,862,864,866,867,869,871,873,875,877,879,881,883,885,887,889,891,893,895,897,899,901,903,905,907,909,911,913,915,917],[177,177,177,790,179,177,179,179,177],0.097,[177,177,181,792,179,177,179,179,177],0.27,[177,177,184,794,179,177,179,179,177],0.085,[177,177,187,796,179,177,179,179,177],0.078,[177,177,275,798,179,177,179,179,177],0.251,[177,177,300,800,179,177,179,179,177],0.172,[177,177,324,802,179,177,179,179,177],0.246,[177,177,345,804,179,177,179,179,177],2.448,[177,177,476,790,179,177,179,179,177],[177,177,479,807,179,177,179,179,177],0.485,[177,177,481,809,179,177,179,179,177],0.331,[177,177,484,811,179,177,179,179,177],0.342,[177,177,487,813,179,177,179,179,177],2.916,[177,177,490,815,179,177,179,179,177],0.13,[177,177,494,817,179,177,179,179,177],3.164,[177,177,499,819,179,177,179,179,177],0.388,[177,177,502,821,179,177,179,179,177],0.256,[177,177,505,823,179,177,179,179,177],0.274,[177,177,511,825,179,177,179,179,177],0.345,[177,177,516,827,179,177,179,179,177],0.944,[177,177,523,829,179,177,179,179,177],1.045,[177,177,537,831,179,177,179,179,177],0.684,[181,177,177,833,179,177,179,179,177],0.091,[181,177,181,835,179,177,179,179,177],0.279,[181,177,184,837,179,177,179,179,177],0.109,[181,177,187,839,179,177,179,179,177],0.112,[181,177,275,841,179,177,179,179,177],0.271,[181,177,300,843,179,177,179,179,177],0.2,[181,177,324,845,179,177,179,179,177],0.329,[181,177,345,847,179,177,179,179,177],2.754,[181,177,476,849,179,177,179,179,177],0.106,[181,177,479,851,179,177,179,179,177],0.552,[181,177,481,853,179,177,179,179,177],0.377,[181,177,484,855,179,177,179,179,177],0.434,[181,177,487,857,179,177,179,179,177],3.512,[181,177,490,859,179,177,179,179,177],0.17,[181,177,494,861,179,177,179,179,177],3.648,[181,177,499,863,179,177,179,179,177],0.956,[181,177,502,865,179,177,179,179,177],0.258,[181,177,505,823,179,177,179,179,177],[181,177,511,868,179,177,179,179,177],0.343,[181,177,516,870,179,177,179,179,177],0.952,[181,177,523,872,179,177,179,179,177],1.049,[181,177,537,874,179,177,179,179,177],0.799,[184,177,177,876,179,177,179,179,177],0.122,[184,177,181,878,179,177,179,179,177],0.288,[184,177,184,880,179,177,179,179,177],0.095,[184,177,187,882,179,177,179,179,177],0.08,[184,177,275,884,179,177,179,179,177],0.269,[184,177,300,886,179,177,179,179,177],0.18,[184,177,324,888,179,177,179,179,177],0.559,[184,177,345,890,179,177,179,179,177],3.32,[184,177,476,892,179,177,179,179,177],0.102,[184,177,479,894,179,177,179,179,177],1.009,[184,177,481,896,179,177,179,179,177],0.407,[184,177,484,898,179,177,179,179,177],0.332,[184,177,487,900,179,177,179,179,177],1.614,[184,177,490,902,179,177,179,179,177],0.161,[184,177,494,904,179,177,179,179,177],2.657,[184,177,499,906,179,177,179,179,177],8.56,[184,177,502,908,179,177,179,179,177],0.281,[184,177,505,910,179,177,179,179,177],0.285,[184,177,511,912,179,177,179,179,177],0.579,[184,177,516,914,179,177,179,179,177],1.076,[184,177,523,916,179,177,179,179,177],1.015,[184,177,537,918,179,177,179,179,177],1.095,[],[733],[],[],[924],"M2DGR; translation RMSE of ATE; loop closure disabled; map and scan downsampling 0.4 m; first and last 100 s of street07 and street10 discarded (RTK instability); LOG-C ablation column not stored",{"slug":926,"group":927,"sourceId":928,"sourceLabel":929,"table":930,"selfRows":523,"metrics":931,"seqs":936,"entrants":958,"cells":979,"outcomes":1146,"locators":1147,"hardware":1148,"wordings":1149,"notes":1150},"glim2024-table-v","glim2024:Table V","glim2024","Koide et al., 2024","Table V",[932,934],{"label":933,"unit":140,"statistic":94,"alignment":94},"Absolute Trajectory Error [m] (no loop closure)",{"label":935,"unit":140,"statistic":94,"alignment":94},"Absolute Trajectory Error [m] (loop closure)",[937,941,943,945,947,949,951,953,955,957],{"dataset":938,"sequence":939,"environment":940},"Multi-Camera Newer College","quad-easy","handheld campus indoor and 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[5]",{"name":969,"methodId":88,"linkable":79,"proposed":79,"self":79},"CLINS [11] (without loop closure; unlabeled row above CLINS)",{"name":971,"methodId":88,"linkable":79,"proposed":79,"self":79},"CLINS [11] (with loop closure)",{"name":973,"methodId":974,"linkable":171,"proposed":79,"self":79},"DLO [14]","dlo2022",{"name":976,"methodId":928,"linkable":171,"proposed":171,"self":79},"GLIM (odometry, without loop closure; unlabeled row above GLIM)",{"name":978,"methodId":928,"linkable":171,"proposed":171,"self":79},"GLIM (with loop 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C1)","Hilti handheld sequence site1-handheld-4 (name per Table C1)","Hilti handheld sequence site1-handheld-5 (name per Table C1)",{"group":1216,"slug":1217,"sourceLabel":1199,"table":1218,"selfRows":490,"datasets":1219},"voxelslam2026:Table 2 (odometry without LC)","voxelslam2026-table-2-odometry-without-lc","Table 2 (odometry without LC)",[1202,1203,1204,1205,1206,1207,1208,1209,1210,1211,1212,1213,1214],{"group":1221,"slug":1222,"sourceLabel":1223,"table":733,"selfRows":487,"datasets":1224},"clins2021:Table II","clins2021-table-ii","Lv et al., 2021",[1225],"LIOM dataset (Ye et al., ICRA 2019)",{"group":1227,"slug":1228,"sourceLabel":1229,"table":1175,"selfRows":487,"datasets":1230},"fastlio2_2022:Table IV","fastlio2-2022-table-iv","Xu et al., 2022",[1163,385,1165,1231],"UrbanLoco HK (ulhk)",{"group":1233,"slug":1234,"sourceLabel":1235,"table":733,"selfRows":487,"datasets":1236},"lvisam2021:Table II","lvisam2021-table-ii","Shan et al., 2021",[1237,1238],"Handheld (authors' 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2024",[1354],"UrbanNav (Hong Kong)",{"group":1356,"slug":1357,"sourceLabel":1352,"table":733,"selfRows":324,"datasets":1358},"glio2024:Table II","glio2024-table-ii",[1354],{"group":1360,"slug":1361,"sourceLabel":1362,"table":1338,"selfRows":324,"datasets":1363},"liliom2021:Table 1","liliom2021-table-1","Li et al., 2021b",[1364,1365,146],"UTBM (EU long-term)","UrbanLoco",{"group":1367,"slug":1368,"sourceLabel":1169,"table":376,"selfRows":324,"datasets":1369},"slict2023:Table III","slict2023-table-iii",[1370],"SLICT in-house NTU campus dataset",{"group":1372,"slug":1373,"sourceLabel":1374,"table":376,"selfRows":324,"datasets":1375},"trajlo2024:Table III","trajlo2024-table-iii","Zheng & Zhu, 2024",[1376],"Hilti 2021 SLAM challenge",{"group":1378,"slug":1379,"sourceLabel":1380,"table":1381,"selfRows":300,"datasets":1382},"feng2025_construction_lidar_eval:Table 3","feng2025-construction-lidar-eval-table-3","Feng et al., 2025","Table 3",[1383],"Feng et al. simulated construction-site dataset (Gazebo)",{"group":1385,"slug":1386,"sourceLabel":1380,"table":1249,"selfRows":300,"datasets":1387},"feng2025_construction_lidar_eval:Table 4","feng2025-construction-lidar-eval-table-4",[1388],"Feng et al. real construction-site dataset (Xi'an hospital)",{"group":1390,"slug":1391,"sourceLabel":929,"table":128,"selfRows":300,"datasets":1392},"glim2024:Table I","glim2024-table-i",[1393],"simulation (Velodyne VLP-16 model, OpenVINS IMU synthesis)",{"group":1395,"slug":1396,"sourceLabel":1324,"table":1182,"selfRows":300,"datasets":1397},"pointlio2023:Table 5","pointlio2023-table-5",[1327,1328,1398],"utbm",{"group":1400,"slug":1401,"sourceLabel":1169,"table":733,"selfRows":300,"datasets":1402},"slict2023:Table II","slict2023-table-ii",[162],{"group":1404,"slug":1405,"sourceLabel":1276,"table":930,"selfRows":275,"datasets":1406},"clic2023:Table V","clic2023-table-v",[1407],"LVI-SAM dataset",{"group":1409,"slug":1410,"sourceLabel":1260,"table":1175,"selfRows":275,"datasets":1411},"dliom2023:Table IV","dliom2023-table-iv",[1412],"TONGJI dataset",{"group":1414,"slug":1415,"sourceLabel":1260,"table":930,"selfRows":275,"datasets":1416},"dliom2023:Table V","dliom2023-table-v",[94],{"group":1418,"slug":1419,"sourceLabel":1420,"table":376,"selfRows":275,"datasets":1421},"rflio2021:Table III","rflio2021-table-iii","Qian et al., 2021",[1422],"self-collected datasets",{"group":1424,"slug":1425,"sourceLabel":1307,"table":376,"selfRows":275,"datasets":1426},"superodom2021:Table III","superodom2021-table-iii",[1427],"authors' DS-drone data",{"group":1429,"slug":1430,"sourceLabel":1431,"table":733,"selfRows":187,"datasets":1432},"artslam2022:Table II","artslam2022-table-ii","Frosi & Matteucci, 2022",[1433],"KITTI odometry",{"group":1435,"slug":1436,"sourceLabel":1431,"table":376,"selfRows":187,"datasets":1437},"artslam2022:Table III","artslam2022-table-iii",[1438],"KITTI raw",{"group":1440,"slug":1441,"sourceLabel":1431,"table":1175,"selfRows":187,"datasets":1442},"artslam2022:Table IV","artslam2022-table-iv",[1433],{"group":1444,"slug":1445,"sourceLabel":1446,"table":1243,"selfRows":187,"datasets":1447},"chen2026lnconstructionrobots:Table 2","chen2026lnconstructionrobots-table-2","Chen et al., 2026",[1448,1449,1450,1451,1452,1453],"EuRoC MAV (cited study ref. [77])","cited study ref. [43] (Feng et al. 2025)","cited study ref. [58]","cited study ref. [76]","cited study ref. [78]","cited study ref. 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