[{"data":1,"prerenderedAt":797},["ShallowReactive",2],{"method-cocolic2023":3},{"method":4,"reference":56,"equipment":80,"figures":132,"results":133},{"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":25,"sensors":28,"platform":32,"estimator":36,"association":37,"timeModel":38,"deskew":39,"loopClosure":40,"globalOptimization":41,"mapRepresentation":42,"prior":43,"outputGeometry":44,"compute":45,"codeUrl":46,"codeLicense":47,"relatedVersions":48},"cocolic2023","Lang et al., 2023","Coco-LIC","Coco-LIC: Continuous-Time Tightly-Coupled LiDAR-Inertial-Camera Odometry Using Non-Uniform B-Spline",2023,"recent","C07","odometry_with_local_mapping","Coco-LIC 以非均勻 B 樣條（non-uniform B-spline）表示連續時間軌跡，依 IMU 感知的運動劇烈程度動態配置控制點，在平緩運動時使用較少控制點、劇烈運動時加密，以兼顧精度與計算量。視覺像素的深度直接取自全域光達地圖，建立幀對地圖重投影因子，避免在長滑動視窗中最佳化視覺深度。連續時間表示讓不同頻率、非同步的光達、IMU 與相機量測可在任意時刻查詢位姿後融合。","A continuous-time LIC odometry that adapts B-spline control-point density to motion and uses LiDAR map depth for frame-to-map visual factors, fusing asynchronous measurements without interpolation.","full_text_reviewed","peer_reviewed_published","main_body","not_reported（資料為動作捕捉實驗、R3LIVE、FAST-LIVO 手持退化序列與 UrbanNav 都市車載資料）",[20,21],"public_benchmark","controlled_experiment",[23,24],"Non-uniform control points gave better accuracy-time trade-offs than uniform splines on violent and hybrid motion sequences with mocap ground truth; this experiment used LiDAR-inertial data only (cameras excluded), so it validates the spline placement rather than the full LIC system (Sec. IV-A, Table II)","Plausible start-to-end drift on all degenerate R3LIVE and FAST-LIVO sequences tested (Sec. IV-B)",[26,27],"Map management efficiency left for future work (Sec. V)","Evaluation is trajectory-only; no mapping-quality metric (inference from Sec. IV)",[29,30,31],"3D LiDAR (Velodyne VLP-16, HDL-32E, Livox Avia across datasets)","IMU","monocular camera",[33,34,35],"vehicle (UrbanNav, human-driven)","sensor rig in a motion-capture area (carrying mode not stated)","R3LIVE and FAST-LIVO dataset rig (carrying mode not stated in this paper)","Factor-graph nonlinear least squares over the new control points and IMU biases of each 0.1 s interval (LiDAR point-to-plane, visual reprojection with Cauchy kernel, raw IMU and bias random-walk factors, marginalization prior), solved with Levenberg-Marquardt in Ceres; separate cubic non-uniform B-splines for rotation and translation; raw IMU used without preintegration","LiDAR planar (surf) points matched to 5 nearest neighbours of the local keyscan map for point-to-plane residuals; global-map LiDAR points tracked across images by KLT optical flow, outliers removed with fundamental-matrix RANSAC and PnP, then used in frame-to-map reprojection factors without visual depth optimization","continuous-time (non-uniform B-spline with control-point density adapted to IMU-sensed motion)","No separate deskew step: each LiDAR planar point is transformed with the spline pose queried at its own timestamp, so motion distortion removal and trajectory estimation happen simultaneously","none reported","none","Two LiDAR maps: a local map built from keyscans selected by time and space for scan-to-map point-to-plane matching, and a global LiDAR map stored in 0.1 m voxels whose points are projected into images for frame-to-map visual factors; marginalization acts on control points, not on the map","LiDAR-IMU and camera-IMU extrinsics pre-calibrated; IMU noise parameters from datasheets; system assumed stationary at start to initialize IMU bias and gravity","not_reported (evaluation is trajectory-based)","Desktop Intel i7-8700 @ 3.2 GHz with 32 GB RAM; on UrbanNav Medium the average module times are 31.46 ms LiDAR association, 18.90 ms visual association and 9.09 ms optimization, and the 785 s sequence is processed in about 639 s","https:\u002F\u002Fgithub.com\u002FAPRIL-ZJU\u002FCoco-LIC","GPL-3.0 (stated in README)",[49,53],{"relation":50,"title":51,"doi_or_url":52},"preprint","Coco-LIC arXiv (2023-09-18; RA-L accepted preprint)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.09808",{"relation":54,"title":55,"doi_or_url":46},"code_release","APRIL-ZJU\u002FCoco-LIC",{"id":5,"kind":57,"shortName":7,"title":8,"authors":58,"year":9,"venue":66,"venueType":67,"publisher":68,"volumeIssuePages":69,"doi":70,"arxivId":71,"url":52,"firstPublicDate":72,"publicationStatus":16,"metadataStatus":73,"fulltextStatus":15,"era":10,"classicReason":74,"codeUrl":46,"cluster":11,"topics":75,"mdpi":76,"verification":77,"label":6,"fulltextRoute":78,"versionRead":79,"addedByCensus":76},"method",[59,60,61,62,63,64,65],"Xiaolei Lang","Chao Chen","Kai Tang","Yukai Ma","Jiajun Lv","Yong Liu","Xingxing Zuo","IEEE Robotics and Automation Letters","journal","IEEE","8(11): 7074-7081","10.1109\u002Flra.2023.3315542","2309.09808","2023-09-18","metadata_verified","not_applicable",[11],false,"corrected","arXiv","arXiv 2309.09808v1 (2023-09-18; RA-L accepted preprint, 8 pages); IEEE RA-L version of record not compared",[81,88,92,97,103,106,110,115,119,121,125],{"category":82,"model":83,"canonical":83,"role":84,"dataset":85,"specs":86,"locator":87},"lidar","Velodyne VLP-16","method input","self-collected mocap sequences (Smooth, Violent, Hybrid)","16-beam 3D LiDAR, 10 Hz","Sec. IV-A",{"category":89,"model":90,"canonical":90,"role":84,"dataset":85,"specs":91,"locator":87},"imu","Xsens MTi-300","400 Hz",{"category":93,"model":94,"canonical":94,"role":95,"dataset":85,"specs":96,"locator":87},"other","motion capture system","reference or ground truth","ground truth at 120 Hz, millimeter-level accuracy",{"category":82,"model":98,"canonical":98,"role":99,"dataset":100,"specs":101,"locator":102},"Livox Avia","dataset sensor","R3LIVE and FAST-LIVO challenging sequences","10 Hz, solid-state small FoV","Sec. IV-B1",{"category":89,"model":104,"canonical":104,"role":99,"dataset":100,"specs":105,"locator":102},"Livox Avia internal IMU","200 Hz",{"category":107,"model":108,"canonical":108,"role":99,"dataset":100,"specs":109,"locator":102},"camera","camera (model not stated)","15 Hz",{"category":82,"model":111,"canonical":111,"role":99,"dataset":112,"specs":113,"locator":114},"Velodyne HDL-32E","UrbanNav","32-beam 3D LiDAR, 10 Hz","Sec. IV-B2",{"category":116,"model":117,"canonical":117,"role":99,"dataset":112,"specs":118,"locator":114},"stereo_camera","stereo camera (model not stated)","15 Hz; only the left camera used",{"category":89,"model":120,"canonical":120,"role":99,"dataset":112,"specs":91,"locator":114},"Xsens MTi-10",{"category":122,"model":123,"canonical":123,"role":99,"dataset":112,"specs":124,"locator":114},"platform","human-driving vehicle","urban data collection vehicle",{"category":126,"model":127,"canonical":127,"role":128,"dataset":129,"specs":130,"locator":131},"compute","desktop PC with Intel i7-8700 CPU","compute for runtime",null,"3.2 GHz, 32 GB RAM","Sec. IV",[],{"totalRows":134,"groupCount":135,"groups":136,"others":786},41,6,[137,407,565,720],{"slug":138,"group":139,"sourceId":140,"sourceLabel":141,"table":142,"selfRows":143,"metrics":144,"seqs":149,"entrants":183,"cells":207,"outcomes":399,"locators":401,"hardware":403,"wordings":404,"notes":405},"chen2025geode-table-5","chen2025geode:Table 5","chen2025geode","Chen et al., 2025b","Table 5",15,[145],{"label":146,"unit":147,"statistic":148,"alignment":148},"ATE (m)","m","not_reported",[150,154,156,158,160,162,164,166,168,170,172,174,176,179,181],{"dataset":151,"sequence":152,"environment":153},"GEODE","Metro tunnels, alpha (Velodyne VLP-16), Tunneling 3","metro tunnel, mine tunnelling method (translational degeneracy along the axis)",{"dataset":151,"sequence":155,"environment":153},"Metro tunnels, alpha (Velodyne VLP-16), Tunneling 4",{"dataset":151,"sequence":157,"environment":153},"Metro tunnels, alpha (Velodyne VLP-16), Tunneling 5",{"dataset":151,"sequence":159,"environment":153},"Metro tunnels, beta (Ouster OS1-64), Tunneling 2",{"dataset":151,"sequence":161,"environment":153},"Metro tunnels, beta (Ouster OS1-64), Tunneling 3",{"dataset":151,"sequence":163,"environment":153},"Metro tunnels, beta (Ouster OS1-64), Tunneling 4",{"dataset":151,"sequence":165,"environment":153},"Metro tunnels, beta (Ouster OS1-64), Tunneling 5",{"dataset":151,"sequence":167,"environment":153},"Metro tunnels, gamma (Livox AVIA), Tunneling 1",{"dataset":151,"sequence":169,"environment":153},"Metro tunnels, gamma (Livox AVIA), Tunneling 2",{"dataset":151,"sequence":171,"environment":153},"Metro tunnels, gamma (Livox AVIA), Tunneling 3",{"dataset":151,"sequence":173,"environment":153},"Metro tunnels, gamma (Livox AVIA), Tunneling 4",{"dataset":151,"sequence":175,"environment":153},"Metro tunnels, gamma (Livox AVIA), Tunneling 5",{"dataset":151,"sequence":177,"environment":178},"Stairs, alpha (Velodyne VLP-16), Stairs","building stairs and corridors across multiple floors (loop from the seventh floor)",{"dataset":151,"sequence":180,"environment":178},"Stairs, beta (Ouster OS1-64), Stairs",{"dataset":151,"sequence":182,"environment":178},"Stairs, gamma (Livox AVIA), Stairs",[184,188,191,194,197,198,201,204],{"name":185,"methodId":186,"linkable":187,"proposed":76,"self":76},"COIN-LIO","coinlio2024",true,{"name":189,"methodId":190,"linkable":187,"proposed":76,"self":76},"FAST-LIO2","fastlio2_2022",{"name":192,"methodId":193,"linkable":187,"proposed":76,"self":76},"DLIO","dlio2023",{"name":195,"methodId":196,"linkable":187,"proposed":76,"self":76},"FAST-LIVO","fastlivo2022",{"name":7,"methodId":5,"linkable":187,"proposed":76,"self":187},{"name":199,"methodId":200,"linkable":187,"proposed":76,"self":76},"R3LIVE","r3live2022",{"name":202,"methodId":203,"linkable":187,"proposed":76,"self":76},"LVI-SAM","lvisam2021",{"name":205,"methodId":206,"linkable":187,"proposed":76,"self":76},"VINS-Fusion","vinsfusion2019",[208,211,214,217,220,222,225,226,229,230,232,234,236,237,239,240,241,242,243,245,247,248,250,252,254,255,256,258,260,262,263,264,266,268,269,270,271,272,273,275,276,278,279,280,282,283,284,285,286,288,289,290,291,292,294,296,298,299,301,303,304,306,308,310,311,313,315,317,319,321,323,325,326,328,329,331,332,334,336,337,338,340,341,343,344,345,347,349,350,352,353,355,356,357,359,360,362,364,366,368,370,371,373,374,376,379,381,383,385,387,388,389,390,392,393,394,395,396,397,398],[209,209,209,129,209,209,210,210,209],0,-1,[212,209,209,213,210,209,210,210,209],1,0.21,[215,209,209,216,210,209,210,210,209],2,0.18,[218,209,209,219,210,209,210,210,209],3,0.2,[221,209,209,129,209,209,210,210,209],4,[223,209,209,224,210,209,210,210,209],5,0.59,[135,209,209,129,212,209,210,210,209],[227,209,209,228,210,209,210,210,209],7,47.66,[209,209,212,129,209,209,210,210,209],[212,209,212,231,210,209,210,210,209],0.24,[215,209,212,233,210,209,210,210,209],0.14,[218,209,212,235,210,209,210,210,209],0.19,[221,209,212,129,209,209,210,210,209],[223,209,212,238,210,209,210,210,209],0.25,[135,209,212,129,212,209,210,210,209],[227,209,212,129,212,209,210,210,209],[209,209,215,129,209,209,210,210,209],[212,209,215,235,210,209,210,210,209],[215,209,215,244,210,209,210,210,209],0.13,[218,209,215,246,210,209,210,210,209],0.17,[221,209,215,129,209,209,210,210,209],[223,209,215,249,210,209,210,210,209],11.44,[135,209,215,251,210,209,210,210,209],0.3,[227,209,215,253,210,209,210,210,209],0.69,[209,209,218,233,210,209,210,210,209],[212,209,218,233,210,209,210,210,209],[215,209,218,257,210,209,210,210,209],0.11,[218,209,218,259,210,209,210,210,209],0.12,[221,209,218,261,210,209,210,210,209],0.15,[223,209,218,233,210,209,210,210,209],[135,209,218,233,210,209,210,210,209],[227,209,218,265,210,209,210,210,209],2.31,[209,209,221,267,210,209,210,210,209],0.23,[212,209,221,246,210,209,210,210,209],[215,209,221,233,210,209,210,210,209],[218,209,221,261,210,209,210,210,209],[221,209,221,235,210,209,210,210,209],[223,209,221,219,210,209,210,210,209],[135,209,221,274,210,209,210,210,209],0.37,[227,209,221,129,212,209,210,210,209],[209,209,223,277,210,209,210,210,209],0.16,[212,209,223,246,210,209,210,210,209],[215,209,223,261,210,209,210,210,209],[218,209,223,281,210,209,210,210,209],0.27,[221,209,223,246,210,209,210,210,209],[223,209,223,238,210,209,210,210,209],[135,209,223,216,210,209,210,210,209],[227,209,223,129,212,209,210,210,209],[209,209,135,287,210,209,210,210,209],3.84,[212,209,135,259,210,209,210,210,209],[215,209,135,257,210,209,210,210,209],[218,209,135,244,210,209,210,210,209],[221,209,135,216,210,209,210,210,209],[223,209,135,293,210,209,210,210,209],0.26,[135,209,135,295,210,209,210,210,209],0.22,[227,209,135,297,210,209,210,210,209],0.36,[209,209,227,129,209,209,210,210,209],[212,209,227,300,210,209,210,210,209],1.16,[215,209,227,302,210,209,210,210,209],8.51,[218,209,227,297,210,209,210,210,209],[221,209,227,305,210,209,210,210,209],0.42,[223,209,227,307,210,209,210,210,209],2.83,[135,209,227,309,210,209,210,210,209],35.4,[227,209,227,129,212,209,210,210,209],[209,209,312,129,209,209,210,210,209],8,[212,209,312,314,210,209,210,210,209],1.88,[215,209,312,316,210,209,210,210,209],15.43,[218,209,312,318,210,209,210,210,209],1.56,[221,209,312,320,210,209,210,210,209],2.06,[223,209,312,322,210,209,210,210,209],86.51,[135,209,312,324,210,209,210,210,209],2.1,[227,209,312,129,212,209,210,210,209],[209,209,327,129,209,209,210,210,209],9,[212,209,327,235,210,209,210,210,209],[215,209,327,330,210,209,210,210,209],2.63,[218,209,327,219,210,209,210,210,209],[221,209,327,333,210,209,210,210,209],4.06,[223,209,327,335,210,209,210,210,209],63.05,[135,209,327,231,210,209,210,210,209],[227,209,327,129,212,209,210,210,209],[209,209,339,129,209,209,210,210,209],10,[212,209,339,261,210,209,210,210,209],[215,209,339,342,210,209,210,210,209],5.74,[218,209,339,244,210,209,210,210,209],[221,209,339,216,210,209,210,210,209],[223,209,339,346,210,209,210,210,209],57.46,[135,209,339,348,210,209,210,210,209],0.35,[227,209,339,129,212,209,210,210,209],[209,209,351,129,209,209,210,210,209],11,[212,209,351,281,210,209,210,210,209],[215,209,351,354,210,209,210,210,209],2.48,[218,209,351,259,210,209,210,210,209],[221,209,351,246,210,209,210,210,209],[223,209,351,358,210,209,210,210,209],1.3,[135,209,351,246,210,209,210,210,209],[227,209,351,361,210,209,210,210,209],22.03,[209,209,363,129,209,209,210,210,209],12,[212,209,363,365,210,209,210,210,209],4.69,[215,209,363,367,210,209,210,210,209],4.89,[218,209,363,369,210,209,210,210,209],3.28,[221,209,363,129,209,209,210,210,209],[223,209,363,372,210,209,210,210,209],4.54,[135,209,363,129,212,209,210,210,209],[227,209,363,375,210,209,210,210,209],3.66,[209,209,377,378,210,209,210,210,209],13,0.45,[212,209,377,380,210,209,210,210,209],0.38,[215,209,377,382,210,209,210,210,209],0.41,[218,209,377,384,210,209,210,210,209],0.4,[221,209,377,386,210,209,210,210,209],6.84,[223,209,377,129,212,209,210,210,209],[135,209,377,129,212,209,210,210,209],[227,209,377,224,210,209,210,210,209],[209,209,391,129,209,209,210,210,209],14,[212,209,391,129,212,209,210,210,209],[215,209,391,129,212,209,210,210,209],[218,209,391,129,212,209,210,210,209],[221,209,391,129,212,209,210,210,209],[223,209,391,129,212,209,210,210,209],[135,209,391,129,212,209,210,210,209],[227,209,391,129,212,209,210,210,209],[74,400],"failed",[402],"Table 5 (VoR)",[],[],[406],"ATE (m) per sequence, average of five runs, parameters not tuned per sequence; X = breakdown or error > 100 m; - = algorithm not adapted to this data. Only Metro tunnels and Stairs blocks extracted; shield-tunnel sequences are absent from Table 5 because all methods failed (Sec. 5.2). Stairs GT from PALoc (inlier RMSE 0.07 m alpha, 0.08 m beta); gamma stairs GT not accurate.",{"slug":408,"group":409,"sourceId":5,"sourceLabel":6,"table":410,"selfRows":339,"metrics":411,"seqs":418,"entrants":434,"cells":444,"outcomes":558,"locators":560,"hardware":561,"wordings":562,"notes":563},"cocolic2023-table-iii","cocolic2023:Table III","Table III",[412,415],{"label":413,"unit":147,"statistic":414,"alignment":41},"start-to-end drift error (translation)","mean",{"label":416,"unit":417,"statistic":414,"alignment":41},"start-to-end drift error (rotation)","deg",[419,423,425,428,431],{"dataset":420,"sequence":421,"environment":422},"R3LIVE dataset","degenerate seq 00","degenerate sequence from the R3LIVE or FAST-LIVO dataset, carrying mode not stated in this paper (degeneration type L)",{"dataset":420,"sequence":424,"environment":422},"degenerate seq 01",{"dataset":420,"sequence":426,"environment":427},"degenerate seq 02","degenerate sequence from the R3LIVE or FAST-LIVO dataset, carrying mode not stated in this paper (degeneration type L, V1)",{"dataset":429,"sequence":430,"environment":422},"FAST-LIVO dataset","LiDAR Degenerate",{"dataset":429,"sequence":432,"environment":433},"Visual Challenge","degenerate sequence from the R3LIVE or FAST-LIVO dataset, carrying mode not stated in this paper (degeneration type L, V2)",[435,436,439,440,441,443],{"name":189,"methodId":190,"linkable":187,"proposed":76,"self":76},{"name":437,"methodId":438,"linkable":187,"proposed":76,"self":76},"VINS-Mono","vinsmono2018",{"name":199,"methodId":200,"linkable":187,"proposed":76,"self":76},{"name":195,"methodId":196,"linkable":187,"proposed":76,"self":76},{"name":442,"methodId":129,"linkable":76,"proposed":76,"self":76},"CLIC",{"name":7,"methodId":5,"linkable":187,"proposed":187,"self":187},[445,447,449,451,453,455,457,458,460,462,464,466,468,470,472,474,476,478,480,482,484,486,488,490,492,493,494,495,496,498,500,502,504,506,508,510,512,514,516,518,520,522,524,526,528,530,532,534,536,538,540,541,542,544,546,548,550,552,554,556],[209,209,209,446,210,209,210,210,209],9.019,[209,212,209,448,210,209,210,210,209],3.441,[212,209,209,450,210,209,210,210,209],0.807,[212,212,209,452,210,209,210,210,209],12.736,[215,209,209,454,210,209,210,210,209],0.035,[215,212,209,456,210,209,210,210,209],0.405,[218,209,209,305,210,209,210,210,209],[218,212,209,459,210,209,210,210,209],3.621,[221,209,209,461,210,209,210,210,209],0.031,[221,212,209,463,210,209,210,210,209],0.578,[223,209,209,465,210,209,210,210,209],0.016,[223,212,209,467,210,209,210,210,209],0.428,[209,209,212,469,210,209,210,210,209],3.161,[209,212,212,471,210,209,210,210,209],15.632,[212,209,212,473,210,209,210,210,209],2.455,[212,212,212,475,210,209,210,210,209],3.523,[215,209,212,477,210,209,210,210,209],0.114,[215,212,212,479,210,209,210,210,209],0.536,[218,209,212,481,210,209,210,210,209],0.881,[218,212,212,483,210,209,210,210,209],2.248,[221,209,212,485,210,209,210,210,209],1.287,[221,212,212,487,210,209,210,210,209],1.293,[223,209,212,489,210,209,210,210,209],0.062,[223,212,212,491,210,209,210,210,209],0.262,[209,209,215,129,209,209,210,210,209],[209,212,215,129,209,209,210,210,209],[212,209,215,129,209,209,210,210,209],[212,212,215,129,209,209,210,210,209],[215,209,215,497,210,209,210,210,209],0.065,[215,212,215,499,210,209,210,210,209],1.124,[218,209,215,501,210,209,210,210,209],3.855,[218,212,215,503,210,209,210,210,209],9.303,[221,209,215,505,210,209,210,210,209],2.457,[221,212,215,507,210,209,210,210,209],2.823,[223,209,215,509,210,209,210,210,209],0.122,[223,212,215,511,210,209,210,210,209],1.283,[209,209,218,513,210,209,210,210,209],0.798,[209,212,218,515,210,209,210,210,209],1.816,[212,209,218,517,210,209,210,210,209],1.644,[212,212,218,519,210,209,210,210,209],2.572,[215,209,218,521,210,209,210,210,209],8.733,[215,212,218,523,210,209,210,210,209],1.861,[218,209,218,525,210,209,210,210,209],0.049,[218,212,218,527,210,209,210,210,209],2.025,[221,209,218,529,210,209,210,210,209],6.811,[221,212,218,531,210,209,210,210,209],5.911,[223,209,218,533,210,209,210,210,209],0.045,[223,212,218,535,210,209,210,210,209],2.497,[209,209,221,537,210,209,210,210,209],2.957,[209,212,221,539,210,209,210,210,209],2.707,[212,209,221,129,209,209,210,210,209],[212,212,221,129,209,209,210,210,209],[215,209,221,543,210,209,210,210,209],0.234,[215,212,221,545,210,209,210,210,209],0.751,[218,209,221,547,210,209,210,210,209],0.043,[218,212,221,549,210,209,210,210,209],0.408,[221,209,221,551,210,209,210,210,209],5.262,[221,212,221,553,210,209,210,210,209],4.537,[223,209,221,555,210,209,210,210,209],0.166,[223,212,221,557,210,209,210,210,209],0.889,[559],"failed (drift over 10 m)",[410],[],[],[564],"Start-to-end drift (translation m \u002F rotation deg) on degenerate Livox Avia sequences without ground truth; rig returns to start; loop closure disabled; average of 6 runs; fail = drift over 10 m; L = LiDAR faces ground or walls, V1 = camera faces white wall, V2 = motion blur",{"slug":566,"group":567,"sourceId":5,"sourceLabel":6,"table":568,"selfRows":327,"metrics":569,"seqs":573,"entrants":594,"cells":611,"outcomes":713,"locators":715,"hardware":716,"wordings":717,"notes":718},"cocolic2023-table-ii","cocolic2023:Table II","Table II",[570],{"label":571,"unit":147,"statistic":572,"alignment":148},"RMSE of APE","RMSE",[574,578,580,582,584,586,588,590,592],{"dataset":575,"sequence":576,"environment":577},"self-collected mocap sequences","Smooth1","motion-capture area (indoor or outdoor not stated); smooth, violent and hybrid motion sequences",{"dataset":575,"sequence":579,"environment":577},"Smooth2",{"dataset":575,"sequence":581,"environment":577},"Smooth3",{"dataset":575,"sequence":583,"environment":577},"Violent1",{"dataset":575,"sequence":585,"environment":577},"Violent2",{"dataset":575,"sequence":587,"environment":577},"Violent3",{"dataset":575,"sequence":589,"environment":577},"Hybrid1",{"dataset":575,"sequence":591,"environment":577},"Hybrid2",{"dataset":575,"sequence":593,"environment":577},"Hybrid3",[595,597,599,601,603,605,607,609],{"name":596,"methodId":129,"linkable":76,"proposed":76,"self":76},"uni-1 (LIO, uniform B-spline)",{"name":598,"methodId":129,"linkable":76,"proposed":76,"self":76},"uni-2 (LIO, uniform B-spline)",{"name":600,"methodId":129,"linkable":76,"proposed":76,"self":76},"uni-3 (LIO, uniform B-spline)",{"name":602,"methodId":129,"linkable":76,"proposed":76,"self":76},"uni-4 (LIO, uniform B-spline)",{"name":604,"methodId":129,"linkable":76,"proposed":76,"self":76},"uni-5 (LIO, uniform B-spline)",{"name":606,"methodId":129,"linkable":76,"proposed":76,"self":76},"uni-8 (LIO, uniform B-spline)",{"name":608,"methodId":129,"linkable":76,"proposed":76,"self":76},"uni-16 (LIO, uniform B-spline)",{"name":610,"methodId":5,"linkable":187,"proposed":187,"self":187},"non-uni (LIO, adaptive non-uniform B-spline)",[612,614,616,618,620,621,622,623,624,625,627,629,630,632,634,635,637,639,641,642,644,645,647,649,651,653,654,655,656,657,658,659,661,663,665,666,667,668,669,671,672,673,675,677,678,680,682,683,684,685,686,688,690,691,693,694,695,696,697,698,699,700,701,702,703,705,706,707,708,710,711,712],[209,209,209,613,210,209,210,210,209],0.011,[209,209,212,615,210,209,210,210,209],0.027,[209,209,215,617,210,209,210,210,209],0.018,[209,209,218,619,210,209,210,210,209],0.355,[209,209,221,129,209,209,210,210,209],[209,209,223,129,209,209,210,210,209],[209,209,135,129,209,209,210,210,209],[209,209,227,129,209,209,210,210,209],[209,209,312,129,209,209,210,210,209],[212,209,209,626,210,209,210,210,209],0.012,[212,209,212,628,210,209,210,210,209],0.048,[212,209,215,617,210,209,210,210,209],[212,209,218,631,210,209,210,210,209],0.06,[212,209,221,633,210,209,210,210,209],0.079,[212,209,223,233,210,209,210,210,209],[212,209,135,636,210,209,210,210,209],0.164,[212,209,227,638,210,209,210,210,209],0.059,[212,209,312,640,210,209,210,210,209],0.038,[215,209,209,626,210,209,210,210,209],[215,209,212,643,210,209,210,210,209],0.05,[215,209,215,617,210,209,210,210,209],[215,209,218,646,210,209,210,210,209],0.054,[215,209,221,648,210,209,210,210,209],0.052,[215,209,223,650,210,209,210,210,209],0.112,[215,209,135,652,210,209,210,210,209],0.105,[215,209,227,638,210,209,210,210,209],[215,209,312,454,210,209,210,210,209],[218,209,209,626,210,209,210,210,209],[218,209,212,628,210,209,210,210,209],[218,209,215,617,210,209,210,210,209],[218,209,218,648,210,209,210,210,209],[218,209,221,660,210,209,210,210,209],0.051,[218,209,223,662,210,209,210,210,209],0.102,[218,209,135,664,210,209,210,210,209],0.101,[218,209,227,631,210,209,210,210,209],[218,209,312,454,210,209,210,210,209],[221,209,209,626,210,209,210,210,209],[221,209,212,660,210,209,210,210,209],[221,209,215,670,210,209,210,210,209],0.019,[221,209,218,646,210,209,210,210,209],[221,209,221,660,210,209,210,210,209],[221,209,223,674,210,209,210,210,209],0.104,[221,209,135,676,210,209,210,210,209],0.107,[221,209,227,631,210,209,210,210,209],[221,209,312,679,210,209,210,210,209],0.036,[223,209,209,681,210,209,210,210,209],0.013,[223,209,212,648,210,209,210,210,209],[223,209,215,617,210,209,210,210,209],[223,209,218,648,210,209,210,210,209],[223,209,221,631,210,209,210,210,209],[223,209,223,687,210,209,210,210,209],0.118,[223,209,135,689,210,209,210,210,209],0.113,[223,209,227,129,209,209,210,210,209],[223,209,312,692,210,209,210,210,209],0.037,[135,209,209,525,210,209,210,210,209],[135,209,212,129,209,209,210,210,209],[135,209,215,129,209,209,210,210,209],[135,209,218,129,209,209,210,210,209],[135,209,221,129,209,209,210,210,209],[135,209,223,129,209,209,210,210,209],[135,209,135,129,209,209,210,210,209],[135,209,227,129,209,209,210,210,209],[135,209,312,129,209,209,210,210,209],[227,209,209,613,210,209,210,210,209],[227,209,212,704,210,209,210,210,209],0.028,[227,209,215,617,210,209,210,210,209],[227,209,218,640,210,209,210,210,209],[227,209,221,660,210,209,210,210,209],[227,209,223,709,210,209,210,210,209],0.1,[227,209,135,709,210,209,210,210,209],[227,209,227,638,210,209,210,210,209],[227,209,312,454,210,209,210,210,209],[714],"failed (RMSE over 1 m, marked fail)",[568],[],[],[719],"LiDAR-inertial only (cameras excluded) comparison of uniform B-splines with x control points per 0.1 s (uni-x) against the adaptive non-uniform placement; VLP-16 + Xsens MTi-300 rig with mocap ground truth; values averaged over 6 runs; optimization-time half of each cell omitted to respect the row cap",{"slug":721,"group":722,"sourceId":5,"sourceLabel":6,"table":723,"selfRows":218,"metrics":724,"seqs":726,"entrants":736,"cells":743,"outcomes":779,"locators":781,"hardware":782,"wordings":783,"notes":784},"cocolic2023-table-iv","cocolic2023:Table IV","Table IV",[725],{"label":571,"unit":147,"statistic":572,"alignment":148},[727,730,733],{"dataset":112,"sequence":728,"environment":729},"UrbanNav-HK-Medium-Urban-1","dense urban roads with dynamic objects, 3.64 km",{"dataset":112,"sequence":731,"environment":732},"UrbanNav-HK-Deep-Urban-1","dense urban roads with dynamic objects, 4.51 km",{"dataset":112,"sequence":734,"environment":735},"UrbanNav-HK-Harsh-Urban-1","dense urban roads with dynamic objects, 3.04 km",[737,738,739,740,741,742],{"name":189,"methodId":190,"linkable":187,"proposed":76,"self":76},{"name":437,"methodId":438,"linkable":187,"proposed":76,"self":76},{"name":202,"methodId":203,"linkable":187,"proposed":76,"self":76},{"name":195,"methodId":196,"linkable":187,"proposed":76,"self":76},{"name":442,"methodId":129,"linkable":76,"proposed":76,"self":76},{"name":7,"methodId":5,"linkable":187,"proposed":187,"self":187},[744,746,748,750,752,754,755,757,759,761,763,765,767,769,771,773,775,777],[209,209,209,745,210,209,210,210,209],6.734,[209,209,212,747,210,209,210,210,209],6.335,[209,209,215,749,210,209,210,210,209],2.82,[212,209,209,751,210,209,210,210,209],102.582,[212,209,212,753,210,209,210,210,209],63.894,[212,209,215,129,209,209,210,210,209],[215,209,209,756,210,209,210,210,209],7.456,[215,209,212,758,210,209,210,210,209],8.607,[215,209,215,760,210,209,210,210,209],25.684,[218,209,209,762,210,209,210,210,209],7.331,[218,209,212,764,210,209,210,210,209],7.159,[218,209,215,766,210,209,210,210,209],2.787,[221,209,209,768,210,209,210,210,209],6.923,[221,209,212,770,210,209,210,210,209],6.001,[221,209,215,772,210,209,210,210,209],2.415,[223,209,209,774,210,209,210,210,209],6.031,[223,209,212,776,210,209,210,210,209],5.688,[223,209,215,778,210,209,210,210,209],2.189,[780],"failed (marked fail)",[723],[],[],[785],"RMSE of APE on UrbanNav (HDL-32E, left camera of stereo pair, Xsens MTi-10, human-driven vehicle); R3LIVE not run because it supports only solid-state LiDAR; loop closure disabled; average of 6 runs",[787,792],{"group":788,"slug":789,"sourceLabel":6,"table":790,"selfRows":218,"datasets":791},"cocolic2023:Table V","cocolic2023-table-v","Table V",[112],{"group":793,"slug":794,"sourceLabel":6,"table":795,"selfRows":212,"datasets":796},"cocolic2023:Text Sec.IV-B2","cocolic2023-text-sec-iv-b2","Text Sec.IV-B2",[112],1790510662539]