[{"data":1,"prerenderedAt":758},["ShallowReactive",2],{"method-r3livepp2024":3},{"method":4,"reference":64,"equipment":83,"figures":140,"results":172},{"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":28,"sensors":33,"platform":37,"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},"r3livepp2024","Lin & Zhang, 2024","R3LIVE++","R3LIVE++: A Robust, Real-Time, Radiance Reconstruction Package With a Tightly-Coupled LiDAR-Inertial-Visual State Estimator",2024,"recent","C07","odometry_with_local_mapping","R3LIVE++ 延伸 R3LIVE，在 VIO 中加入相機光度校正（響應函數與暗角）及曝光時間的線上估計，使地圖點儲存的是與曝光無關的輻射值（radiance）而非原始顏色。作者在 NCLT 公開資料集的 25 個序列上比較定位精度，並以自建資料集評估退化場景的穩健性與輻射地圖誤差。論文亦指出地圖點密度（約 1 cm）與光達原始點密度限制了可重建的影像細節。","Extends R3LIVE with photometric calibration and online exposure-time estimation so that map points store radiance, with larger-scale evaluation on NCLT and a released dataset.","full_text_reviewed","peer_reviewed_published","main_body","未於營建工地驗證；NCLT 為校園長期資料（作者提及含施工造成的長期結構變化），自建資料為香港兩所大學校園。對工程點雲而言，其曝光一致的輻射地圖與匯出流程具參考價值，但未報告點雲幾何精度（推論）。",[20,21],"public_benchmark","independent_reference",[23,24,25,26,27],"Online exposure-time estimation enables recovery of radiance rather than exposure-dependent color; lowest mean exposure-time error against Tum-cali and DSO on all five sequences, although DSO has a lower maximum error on hkust_campus_seq_03 (2.040 vs 3.514 ms) (VoR Sec. VI-F1, Table V)","Lowest average APE on 25 NCLT sequences (about 138 km): 8.5 +\u002F- 2.1 m vs 9.6 (Fast-LIO2), 10.3 (FAST-LIVO), 10.6 (R2LIVE), 15.0 (LVI-SAM) and 15.4 m (LIO-SAM) (VoR Table III); also a handheld R3LIVE-dataset (13 sequences per the text, 14 rows in Table II)","Returned to the start with 4.1 cm and 4.6 cm drift on two LiDAR-degenerate stairway sequences where FAST-LIO2, LIO-SAM and its own LIO subsystem failed (VoR Sec. VI-E1, Fig. 10)","Lowest average photometric error on the R3LIVE-dataset (18.01 vs 23.58 for R3LIVE and 38.60 for the baseline) (VoR Table VI)","CPU-only real-time radiance map reconstruction (Sec. VI-G)",[29,30,31,32],"Radiance map resolution limited by map point density (1 cm) and raw LiDAR density; higher density increases processing time (Sec. VIII-B)","Sun-facing LiDAR noise, under- or over-exposed images and moving objects degraded accuracy in some NCLT sequences (Sec. VI-D)","No loop closure; the version of record notes gradual drift and inconsistent reconstruction when revisiting places (Sec. VIII-B)","Not best on every NCLT sequence: e.g., Fast-LIO2 4.8 vs 6.6 m on 2012-01-15 and 3.3 vs 7.5 m on 2012-11-04 (VoR Table III)",[34,35,36],"3D LiDAR (LiVOX AVIA in the R3LIVE-dataset; NCLT 3D LiDAR, model not named in the paper)","IMU (model not reported)","RGB camera (FLIR Blackfly BFS-u3-13y3c global shutter in the R3LIVE-dataset; front-facing camera of the NCLT omnidirectional camera)",[38,39],"handheld","wheeled UGV","error-state iterated Kalman filter; state includes camera extrinsic, intrinsic, camera-IMU time offset and inverse exposure time","LiDAR point-to-plane (GICP-style) scan-to-map against the five nearest map points in an incremental k-d tree, with a plane fitted only if they lie within about 0.4 m (VoR Sec. IV-A); VIO tracks about 400 map points at least 50 pixels apart by Lucas-Kanade optical flow, first minimizing frame-to-frame PnP error, then frame-to-map radiance error on individual map points using photometrically corrected images (CRF and vignetting); pixels at 0 or 255 are excluded from radiance updates (VoR Sec. V)","discrete poses; camera-IMU time offset estimated online","in-frame motion of each LiDAR scan compensated by IMU backward propagation (following FAST-LIO) before registration (Sec. IV-A)","none (Sec. VI-D states the system has no loop detection and correction)","none","radiance map: points with position, RGB radiance, position and radiance covariances and timestamps, stored in 0.1 m voxels","offline photometric calibration (response function, vignetting)","radiance (RGB) point map at 1 cm point spacing in the implementation; offline mesh and texture via CGAL and OpenMVS with export to pcd, ply, obj (arXiv v1 Sec. 7.2; the version of record refers to the GitHub utilities, Unreal Engine export and supplementary material); HDR images rendered at chosen exposure times (Sec. VII-A, VIII-B)","CPU-only Intel i7-9700K with 64 GB RAM: NCLT mean 34.271 ms per LiDAR scan and 16.600 ms per image; R3LIVE-dataset mean 23.453 ms per scan (the text says 22.7 ms) and 16.244 ms per image; processing per second of data 426 ms (NCLT) and 470 ms (R3LIVE-dataset) (VoR Sec. VI-G, Table VII)","https:\u002F\u002Fgithub.com\u002Fhku-mars\u002Fr3live","GPLv2 for personal and academic use; commercial use requires negotiation (README)",[53,57,61],{"relation":54,"title":55,"doi_or_url":56},"preprint","R3LIVE++ arXiv v1 (2022-09-08)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2209.03666",{"relation":58,"title":59,"doi_or_url":60},"conference_version","R3LIVE (ICRA 2022)","10.1109\u002FICRA46639.2022.9811935",{"relation":62,"title":63,"doi_or_url":50},"code_release","hku-mars\u002Fr3live",{"id":5,"kind":65,"shortName":7,"title":8,"authors":66,"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",[67,68],"Jiarong Lin","Fu Zhang","IEEE Transactions on Pattern Analysis and Machine Intelligence","journal","IEEE","46(12): 11168-11185","10.1109\u002Ftpami.2024.3456473","2209.03666","2022-09-08","metadata_verified","not_applicable",[11],false,"confirmed","NTU institutional (curl)","IEEE TPAMI 46(12):11168-11185 version of record (typeset PDF, 18 pages, via NTU institutional access), read in full; tables are vector graphics and were read from rendered page images; arXiv 2209.03666 v1 (2022-09-08) also read in full and compared. Results are taken from the version of record.",[84,92,97,103,109,113,118,122,125,128,131,134],{"category":85,"model":86,"canonical":87,"role":88,"dataset":89,"specs":90,"locator":91},"lidar","LiVOX AVIA 3D LiDAR","Livox Avia","method input","R3LIVE-dataset","FoV 70.4 x 77.2 deg; 10 Hz; about 240k points per second","VoR Sec. VI-B1; Sec. VI-G",{"category":93,"model":94,"canonical":94,"role":88,"dataset":89,"specs":95,"locator":96},"camera","FLIR Blackfly BFS-u3-13y3c global shutter camera","FoV 82.9 x 66.5 deg; 15 Hz; offline photometric calibration (response function, vignetting)","VoR Sec. VI-B1; Fig. 9; Sec. VI-G",{"category":98,"model":99,"canonical":99,"role":100,"dataset":89,"specs":101,"locator":102},"compute","DJI manifold-2c onboard computer (Intel i7-8550u CPU, 8GB RAM)","dataset sensor","onboard computer of the data-collection device","VoR Sec. VI-B1",{"category":104,"model":105,"canonical":105,"role":106,"dataset":89,"specs":107,"locator":108},"other","ArUco marker board","reference or ground truth","reference pose when the device returns to the start","VoR Sec. VI-B1; Fig. 8",{"category":110,"model":111,"canonical":111,"role":88,"dataset":89,"specs":112,"locator":108},"platform","handheld device with FDM 3D-printed mechanical components","schematics open-sourced",{"category":110,"model":114,"canonical":114,"role":100,"dataset":115,"specs":116,"locator":117},"Segway robot","NCLT","NCLT platform","VoR Sec. VI-A",{"category":93,"model":119,"canonical":119,"role":100,"dataset":115,"specs":120,"locator":121},"omnidirectional camera (front-facing camera, one of five, used)","5 Hz image rate","VoR Sec. VI-A; Sec. VI-G",{"category":85,"model":123,"canonical":123,"role":100,"dataset":115,"specs":124,"locator":121},"3D LiDAR (model not named in the paper)","10 Hz, about 695k points per second",{"category":85,"model":126,"canonical":126,"role":100,"dataset":115,"specs":127,"locator":117},"planar LiDAR (not used)","not_reported",{"category":129,"model":130,"canonical":130,"role":100,"dataset":115,"specs":127,"locator":117},"gnss","GPS (not used as input)",{"category":132,"model":133,"canonical":133,"role":100,"dataset":115,"specs":127,"locator":117},"wheel_or_leg_odometry","wheel encoders (not used)",{"category":98,"model":135,"canonical":135,"role":136,"dataset":137,"specs":138,"locator":139},"CPU-only PC, Intel i7-9700K CPU, 64 GB RAM","compute for runtime",null,"no GPU acceleration","VoR Sec. VI-G; Table VII",[141,154,164],{"refId":5,"refLabel":6,"fig":142,"whatZh":143,"license":144,"licenseUrl":145,"sourceUrl":146,"src":147,"width":148,"height":149,"thumb":150,"thumbWidth":151,"thumbHeight":152,"modified":153},"Fig. 7","手持資料收集裝置、作為真值的 ArUco 標記板與開源機構模型（正式版為 Fig. 8）","CC BY 4.0 (arXiv v1 per abs page); TPAMI version of record (c) 2024 IEEE","http:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby\u002F4.0\u002F","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2209.03666v1\u002Fpics\u002Fhandheld_cad_v2.jpg","\u002Ffigure-files\u002Fr3livepp2024\u002Ffig-7.webp",1358,961,"\u002Ffigure-files\u002Fr3livepp2024\u002Ffig-7.thumb.webp",480,340,"converted to WebP",{"refId":5,"refLabel":6,"fig":155,"whatZh":156,"license":144,"licenseUrl":145,"sourceUrl":157,"src":158,"width":159,"height":160,"thumb":161,"thumbWidth":151,"thumbHeight":162,"modified":163},"Fig. 16","香港大學主建物的即時重建輻射地圖鳥瞰，並附室外與室內細部","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2209.03666v1\u002Fpics\u002Fmain_horizon_v2.jpg","\u002Ffigure-files\u002Fr3livepp2024\u002Ffig-16.webp",1400,688,"\u002Ffigure-files\u002Fr3livepp2024\u002Ffig-16.thumb.webp",236,"resized to at most 1400 px wide and converted to WebP",{"refId":5,"refLabel":6,"fig":165,"whatZh":166,"license":144,"licenseUrl":145,"sourceUrl":167,"src":168,"width":159,"height":169,"thumb":170,"thumbWidth":151,"thumbHeight":171,"modified":163},"Fig. 9","樓梯前光達退化測試：光達朝向地面與側牆，以及 R3LIVE++ 與純光達慣性方法的軌跡比較（正式版為 Fig. 10）","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2209.03666v1\u002Fpics\u002FLiDAR_degenerate_v3.jpg","\u002Ffigure-files\u002Fr3livepp2024\u002Ffig-9.webp",550,"\u002Ffigure-files\u002Fr3livepp2024\u002Ffig-9.thumb.webp",189,{"totalRows":173,"groupCount":174,"groups":175,"others":742},49,7,[176,565,665,702],{"slug":177,"group":178,"sourceId":5,"sourceLabel":6,"table":179,"selfRows":180,"metrics":181,"seqs":185,"entrants":239,"cells":258,"outcomes":524,"locators":525,"hardware":527,"wordings":528,"notes":563},"r3livepp2024-table-iii","r3livepp2024:Table III","Table III",26,[182],{"label":183,"unit":184,"statistic":127,"alignment":127},"APE (m)","m",[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],{"dataset":115,"sequence":187,"environment":188},"2012-01-08 (6495.7 m, 01:25:35)","University of Michigan North Campus, indoor and outdoor, all seasons (Segway robot)",{"dataset":115,"sequence":190,"environment":188},"2012-01-15 (7499.8 m, 01:52:19)",{"dataset":115,"sequence":192,"environment":188},"2012-01-22 (6183.1 m, 01:27:22)",{"dataset":115,"sequence":194,"environment":188},"2012-02-02 (6315.8 m, 01:38:36)",{"dataset":115,"sequence":196,"environment":188},"2012-02-04 (5641.0 m, 01:18:30)",{"dataset":115,"sequence":198,"environment":188},"2012-02-05 (6649.3 m, 01:34:17)",{"dataset":115,"sequence":200,"environment":188},"2012-02-12 (5829.1 m, 01:25:35)",{"dataset":115,"sequence":202,"environment":188},"2012-02-18 (6249.2 m, 01:29:55)",{"dataset":115,"sequence":204,"environment":188},"2012-02-19 (6232.7 m, 01:29:11)",{"dataset":115,"sequence":206,"environment":188},"2012-03-17 (5907.2 m, 01:22:53)",{"dataset":115,"sequence":208,"environment":188},"2012-03-31 (6073.7 m, 01:27:53)",{"dataset":115,"sequence":210,"environment":188},"2012-04-29 (3183.1 m, 00:43:18)",{"dataset":115,"sequence":212,"environment":188},"2012-05-11 (6116.7 m, 01:25:05)",{"dataset":115,"sequence":214,"environment":188},"2012-05-26 (6340.7 m, 01:28:34)",{"dataset":115,"sequence":216,"environment":188},"2012-06-15 (4085.9 m, 00:55:10)",{"dataset":115,"sequence":218,"environment":188},"2012-08-04 (5492.1 m, 01:20:32)",{"dataset":115,"sequence":220,"environment":188},"2012-08-20 (6014.5 m, 01:23:48)",{"dataset":115,"sequence":222,"environment":188},"2012-09-28 (5574.4 m, 01:17:59)",{"dataset":115,"sequence":224,"environment":188},"2012-10-28 (5682.1 m, 01:26:10)",{"dataset":115,"sequence":226,"environment":188},"2012-11-04 (4788.3 m, 01:20:39)",{"dataset":115,"sequence":228,"environment":188},"2012-11-17 (5751.9 m, 01:29:44)",{"dataset":115,"sequence":230,"environment":188},"2012-12-01 (4991.9 m, 01:16:48)",{"dataset":115,"sequence":232,"environment":188},"2013-01-10 (1137.3 m, 00:17:04)",{"dataset":115,"sequence":234,"environment":188},"2013-02-23 (5235.3 m, 01:20:08)",{"dataset":115,"sequence":236,"environment":188},"2013-04-05 (4523.7 m, 01:09:27)",{"dataset":115,"sequence":238,"environment":188},"Average",[240,243,246,249,252,255],{"name":241,"methodId":5,"linkable":242,"proposed":242,"self":242},"Our (R3LIVE++)",true,{"name":244,"methodId":245,"linkable":242,"proposed":79,"self":79},"R2LIVE","r2live2021",{"name":247,"methodId":248,"linkable":242,"proposed":79,"self":79},"LVI-SAM","lvisam2021",{"name":250,"methodId":251,"linkable":242,"proposed":79,"self":79},"FAST-LIVO","fastlivo2022",{"name":253,"methodId":254,"linkable":242,"proposed":79,"self":79},"Fast-LIO2","fastlio2_2022",{"name":256,"methodId":257,"linkable":242,"proposed":79,"self":79},"LIO-SAM","liosam2020",[259,263,266,269,272,275,278,281,283,286,288,290,292,295,298,301,303,306,309,312,313,316,318,320,323,326,328,330,332,334,336,338,340,343,345,347,350,351,353,356,359,362,364,365,366,368,370,372,373,374,376,377,378,380,382,384,385,387,389,391,393,395,397,398,400,401,403,405,407,409,410,412,413,415,416,418,420,421,424,425,426,428,430,432,433,434,435,436,439,440,441,443,444,445,447,448,449,451,452,454,456,457,458,459,460,462,464,466,468,470,471,473,474,476,477,479,481,483,485,488,489,490,491,492,493,494,495,497,498,500,501,502,504,506,507,509,510,512,513,514,515,516,518,519,521,522],[260,260,260,261,262,260,262,260,260],0,10.8,-1,[264,260,260,265,262,260,262,264,260],1,22.4,[267,260,260,268,262,260,262,267,260],2,23.4,[270,260,260,271,262,260,262,270,260],3,13.4,[273,260,260,274,262,260,262,273,260],4,18.5,[276,260,260,277,262,260,262,276,260],5,21.7,[260,260,264,279,262,260,262,280,260],6.6,6,[264,260,264,282,262,260,262,174,260],5.1,[270,260,264,284,262,260,262,285,260],8.5,8,[273,260,264,287,262,260,262,174,260],4.8,[260,260,267,289,262,260,262,285,260],9.2,[264,260,267,291,262,260,262,260,260],12.6,[267,260,267,293,262,260,262,294,260],8.3,9,[270,260,267,296,262,260,262,297,260],14.1,10,[273,260,267,299,262,260,262,300,260],7.1,11,[276,260,267,294,262,260,262,302,260],12,[260,260,270,304,262,260,262,305,260],5.3,13,[264,260,270,307,262,260,262,308,260],6.1,14,[267,260,270,310,262,260,262,311,260],18.1,15,[270,260,270,302,262,260,262,285,260],[273,260,270,314,262,260,262,315,260],9.1,16,[276,260,270,317,262,260,262,302,260],15.6,[260,260,273,319,262,260,262,280,260],5.6,[264,260,273,321,262,260,262,322,260],8.4,17,[267,260,273,324,262,260,262,325,260],9.6,18,[270,260,273,327,262,260,262,280,260],6.2,[273,260,273,329,262,260,262,280,260],7.2,[276,260,273,261,262,260,262,331,260],19,[260,260,276,284,262,260,262,333,260],20,[264,260,276,335,262,260,262,308,260],7.6,[270,260,276,337,262,260,262,322,260],8.2,[273,260,276,339,262,260,262,311,260],7.8,[260,260,280,341,262,260,262,342,260],4.5,21,[264,260,280,344,262,260,262,322,260],6.5,[267,260,280,346,262,260,262,270,260],40,[270,260,280,348,262,260,262,349,260],16.4,22,[273,260,280,293,262,260,262,302,260],[276,260,280,352,262,260,262,294,260],45,[260,260,174,354,262,260,262,355,260],40.5,23,[264,260,174,357,262,260,262,358,260],59.3,24,[270,260,174,360,262,260,262,361,260],43,25,[273,260,174,363,262,260,262,180,260],57,[260,260,285,284,262,260,262,308,260],[264,260,285,279,262,260,262,342,260],[267,260,285,367,262,260,262,300,260],8.9,[270,260,285,369,262,260,262,333,260],8.6,[273,260,285,280,262,260,262,371,260],27,[276,260,285,324,262,260,262,280,260],[260,260,294,287,262,260,262,280,260],[264,260,294,375,262,260,262,308,260],5.9,[267,260,294,291,262,260,262,280,260],[270,260,294,304,262,260,262,300,260],[273,260,294,379,262,260,262,280,260],4.7,[276,260,294,381,262,260,262,280,260],11.8,[260,260,297,383,262,260,262,300,260],4.9,[264,260,297,297,262,260,262,285,260],[267,260,297,331,262,260,262,386,260],28,[270,260,297,388,262,260,262,308,260],5.2,[273,260,297,390,262,260,262,315,260],7.3,[276,260,297,392,262,260,262,386,260],18.3,[260,260,300,394,262,260,262,322,260],6.3,[264,260,300,396,262,260,262,308,260],6.4,[267,260,300,375,262,260,262,302,260],[270,260,300,399,262,260,262,322,260],7.7,[273,260,300,396,262,260,262,308,260],[276,260,300,402,262,260,262,322,260],5.7,[260,260,302,404,262,260,262,342,260],3.7,[264,260,302,406,262,260,262,342,260],3.8,[267,260,302,408,262,260,262,270,260],4.2,[270,260,302,383,262,260,262,300,260],[273,260,302,411,262,260,262,305,260],4.1,[276,260,302,408,262,260,262,270,260],[260,260,305,414,262,260,262,342,260],4.6,[264,260,305,394,262,260,262,300,260],[267,260,305,392,262,260,262,417,260],29,[270,260,305,419,262,260,262,333,260],7.4,[273,260,305,396,262,260,262,300,260],[276,260,305,422,262,260,262,423,260],18.4,30,[260,260,308,399,262,260,262,333,260],[264,260,308,394,262,260,262,280,260],[270,260,308,427,262,260,262,333,260],7.9,[273,260,308,304,262,260,262,429,260],31,[260,260,311,431,262,260,262,429,260],3.9,[264,260,311,404,262,260,262,429,260],[267,260,311,300,262,260,262,276,260],[270,260,311,276,262,260,262,174,260],[273,260,311,174,262,260,262,333,260],[276,260,311,437,262,260,262,438,260],12.7,32,[260,260,315,341,262,260,262,280,260],[264,260,315,341,262,260,262,280,260],[267,260,315,442,262,260,262,280,260],11.2,[270,260,315,276,262,260,262,300,260],[273,260,315,280,262,260,262,280,260],[276,260,315,446,262,260,262,280,260],11.4,[260,260,322,427,262,260,262,280,260],[264,260,322,279,262,260,262,305,260],[267,260,322,450,262,260,262,305,260],34.4,[270,260,322,369,262,260,262,342,260],[273,260,322,453,262,260,262,311,260],10.4,[276,260,322,455,262,260,262,280,260],36.7,[260,260,325,399,262,260,262,308,260],[264,260,325,285,262,260,262,308,260],[270,260,325,285,262,260,262,308,260],[273,260,325,399,262,260,262,308,260],[260,260,331,461,262,260,262,260,260],7.5,[264,260,331,463,262,260,262,297,260],9.3,[267,260,331,465,262,260,262,297,260],3.4,[270,260,331,467,262,260,262,294,260],8.1,[273,260,331,469,262,260,262,174,260],3.3,[276,260,331,465,262,260,262,294,260],[260,260,333,472,262,260,262,333,260],8.7,[264,260,333,344,262,260,262,305,260],[267,260,333,475,262,260,262,305,260],21.9,[270,260,333,337,262,260,262,311,260],[273,260,333,478,262,260,262,342,260],5.8,[276,260,333,480,262,260,262,311,260],24.2,[260,260,342,482,262,260,262,322,260],11.3,[264,260,342,484,262,260,262,311,260],14.2,[267,260,342,486,262,260,262,487,260],6.9,33,[270,260,342,381,262,260,262,322,260],[273,260,342,419,262,260,262,300,260],[276,260,342,329,262,260,262,260,260],[260,260,349,465,262,260,262,342,260],[264,260,349,414,262,260,262,300,260],[267,260,349,383,262,260,262,280,260],[270,260,349,404,262,260,262,305,260],[273,260,349,496,262,260,262,342,260],3.5,[276,260,349,282,262,260,262,300,260],[260,260,355,499,262,260,262,300,260],11.6,[264,260,355,271,262,260,262,333,260],[267,260,355,291,262,260,262,308,260],[270,260,355,503,262,260,262,308,260],12.2,[273,260,355,505,262,260,262,333,260],11.9,[276,260,355,503,262,260,262,349,260],[260,260,358,508,262,260,262,322,260],8.8,[264,260,358,505,262,260,262,302,260],[267,260,358,511,262,260,262,285,260],9.8,[270,260,358,453,262,260,262,311,260],[273,260,358,396,262,260,262,322,260],[276,260,358,294,262,260,262,285,260],[260,260,361,284,262,260,262,333,260],[264,260,361,517,262,260,262,315,260],10.6,[267,260,361,311,262,260,262,270,260],[270,260,361,520,262,260,262,311,260],10.3,[273,260,361,324,262,260,262,322,260],[276,260,361,523,262,260,262,297,260],15.4,[],[526],"VoR Table III",[],[529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562],"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 1.8 m","APE (m), printed with STD 1.5 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 2.2 m","APE (m), printed with STD 5.0 m","APE (m), printed with STD 5.3 m","APE (m), printed with STD 2.1 m","APE (m), printed with STD 1.6 m","APE (m), printed with STD 3.4 m","APE (m), printed with STD 3.9 m","APE (m), printed with STD 4.8 m","APE (m), printed with STD 4.1 m","APE (m), printed with STD 4.6 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 3.8 m","APE (m), printed with STD 3.1 m",[564],"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":566,"group":567,"sourceId":568,"sourceLabel":569,"table":570,"selfRows":294,"metrics":571,"seqs":576,"entrants":598,"cells":603,"outcomes":658,"locators":659,"hardware":660,"wordings":662,"notes":663},"yan2026tunnel-table-5","yan2026tunnel:Table 5","yan2026tunnel","Yan et al., 2026a","Table 5",[572],{"label":573,"unit":574,"statistic":575,"alignment":77},"Time consumption (ms)","ms","mean",[577,581,583,585,587,589,591,593,596],{"dataset":578,"sequence":579,"environment":580},"Kimera-Multi Campus-Tunnel (KMCT)","ac2-005","",{"dataset":578,"sequence":582,"environment":580},"acl-001",{"dataset":578,"sequence":584,"environment":580},"api-003",{"dataset":578,"sequence":586,"environment":580},"hat-002",{"dataset":578,"sequence":588,"environment":580},"sob-002",{"dataset":578,"sequence":590,"environment":580},"spl-007",{"dataset":578,"sequence":592,"environment":580},"tho-001",{"dataset":594,"sequence":595,"environment":580},"WHU-Helmet (WHUH)","Tunnel",{"dataset":594,"sequence":597,"environment":580},"Subway",[599,601,602],{"name":600,"methodId":568,"linkable":242,"proposed":242,"self":79},"This work (Sum of VIO, LO and EKF)",{"name":247,"methodId":248,"linkable":242,"proposed":79,"self":79},{"name":7,"methodId":5,"linkable":242,"proposed":79,"self":242},[604,606,608,610,612,614,616,618,620,622,624,626,628,630,632,634,636,638,640,642,644,646,648,650,652,654,656],[260,260,260,605,262,260,260,262,260],27.51,[260,260,264,607,262,260,260,262,260],25.57,[260,260,267,609,262,260,260,262,260],25.39,[260,260,270,611,262,260,260,262,260],25.94,[260,260,273,613,262,260,260,262,260],25.99,[260,260,276,615,262,260,260,262,260],26.65,[260,260,280,617,262,260,260,262,260],25.6,[260,260,174,619,262,260,260,262,260],28.98,[260,260,285,621,262,260,260,262,260],28.66,[264,260,260,623,262,260,260,262,260],51.36,[264,260,264,625,262,260,260,262,260],59.77,[264,260,267,627,262,260,260,262,260],59.92,[264,260,270,629,262,260,260,262,260],51.09,[264,260,273,631,262,260,260,262,260],51.27,[264,260,276,633,262,260,260,262,260],53.3,[264,260,280,635,262,260,260,262,260],49.71,[264,260,174,637,262,260,260,262,260],65.22,[264,260,285,639,262,260,260,262,260],67.57,[267,260,260,641,262,260,260,262,260],30.53,[267,260,264,643,262,260,260,262,260],31.21,[267,260,267,645,262,260,260,262,260],30.14,[267,260,270,647,262,260,260,262,260],28.92,[267,260,273,649,262,260,260,262,260],29.05,[267,260,276,651,262,260,260,262,260],31.27,[267,260,280,653,262,260,260,262,260],28.79,[267,260,174,655,262,260,260,262,260],33.5,[267,260,285,657,262,260,260,262,260],35.83,[],[570],[661],"Intel i9-12900H CPU, NVIDIA 3070 Ti, 32 GB RAM, Ubuntu 20.04",[],[664],"Time consumption per frame; proposed method component times (VIO, LO, EKF) not extracted, only their sum",{"slug":666,"group":667,"sourceId":5,"sourceLabel":6,"table":668,"selfRows":273,"metrics":669,"seqs":678,"entrants":683,"cells":685,"outcomes":694,"locators":695,"hardware":697,"wordings":699,"notes":700},"r3livepp2024-table-vii","r3livepp2024:Table VII","Table VII",[670,672,674,676],{"label":671,"unit":574,"statistic":575,"alignment":45},"LiDAR frame (ms), mean, STD 10.551 ms",{"label":673,"unit":574,"statistic":575,"alignment":45},"Camera frame (ms), mean, STD 4.168 ms",{"label":675,"unit":574,"statistic":575,"alignment":45},"LiDAR frame (ms), mean, STD 7.431 ms",{"label":677,"unit":574,"statistic":575,"alignment":45},"Camera frame (ms), mean, STD 2.705 ms",[679,682],{"dataset":115,"sequence":680,"environment":681},"mean over sequences","mixed",{"dataset":89,"sequence":680,"environment":681},[684],{"name":7,"methodId":5,"linkable":242,"proposed":242,"self":242},[686,688,690,692],[260,260,260,687,262,260,260,262,260],34.271,[260,264,260,689,262,260,260,262,260],16.6,[260,267,264,691,262,260,260,262,260],23.453,[260,270,264,693,262,260,260,262,260],16.244,[],[696],"VoR Table VII",[698],"CPU-only PC, Intel i7-9700K, 64 GB RAM",[],[701],"VoR Table VII mean (with STD) of sequence-average processing time per LiDAR or camera frame, CPU only",{"slug":703,"group":704,"sourceId":5,"sourceLabel":6,"table":705,"selfRows":273,"metrics":706,"seqs":716,"entrants":725,"cells":727,"outcomes":734,"locators":735,"hardware":738,"wordings":739,"notes":740},"r3livepp2024-text-sec-vi-e","r3livepp2024:Text Sec.VI-E","Text Sec.VI-E",[707,710,714],{"label":708,"unit":709,"statistic":127,"alignment":45},"drift at return to the starting point","cm",{"label":711,"unit":712,"statistic":127,"alignment":713},"drift in rotation at the end pose (ArUco reference)","deg","control points",{"label":715,"unit":709,"statistic":127,"alignment":713},"drift in translation at the end pose (ArUco reference)",[717,720,722],{"dataset":89,"sequence":718,"environment":719},"degenerate_seq_00","stairway, LiDAR degenerate (handheld)",{"dataset":89,"sequence":721,"environment":719},"degenerate_seq_01",{"dataset":89,"sequence":723,"environment":724},"degenerate_seq_02","narrow passage with white walls, LiDAR degenerate and texture-less (handheld)",[726],{"name":7,"methodId":5,"linkable":242,"proposed":242,"self":242},[728,729,730,732],[260,260,260,411,262,260,262,262,260],[260,260,264,414,262,260,262,262,260],[260,264,267,731,262,264,262,262,260],1.62,[260,267,267,733,262,264,262,262,260],4.57,[],[736,737],"VoR Sec. VI-E1; Fig. 10","VoR Sec. VI-E2",[],[],[741],"Robustness tests on R3LIVE-dataset: degenerate_seq_00 and 01 in front of a stairway with the LiDAR facing the ground and a wall; degenerate_seq_02 a narrow T-shaped passage with white walls; drift at return to the start (ArUco marker for seq_02)",[743,748,753],{"group":744,"slug":745,"sourceLabel":569,"table":746,"selfRows":273,"datasets":747},"yan2026tunnel:Table 2","yan2026tunnel-table-2","Table 2",[594],{"group":749,"slug":750,"sourceLabel":6,"table":751,"selfRows":264,"datasets":752},"r3livepp2024:Table VI","r3livepp2024-table-vi","Table VI",[89],{"group":754,"slug":755,"sourceLabel":569,"table":756,"selfRows":264,"datasets":757},"yan2026tunnel:Table 4","yan2026tunnel-table-4","Table 4",[594],1790510656490]