[{"data":1,"prerenderedAt":1210},["ShallowReactive",2],{"method-lonet2019":3},{"method":4,"reference":52,"equipment":77,"figures":111,"results":112},{"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":21,"limitations":25,"sensors":31,"platform":33,"estimator":35,"association":36,"timeModel":37,"deskew":38,"loopClosure":39,"globalOptimization":40,"mapRepresentation":41,"prior":42,"outputGeometry":43,"compute":44,"codeUrl":45,"codeLicense":46,"relatedVersions":47},"lonet2019","Li et al., 2019","LO-Net","LO-Net: Deep Real-Time Lidar Odometry",2019,"recent","C09","odometry_with_local_mapping","LO-Net 將相鄰兩幀 LiDAR 點雲以圓柱投影編碼成含距離與強度的資料矩陣，以孿生（Siamese）卷積網路直接迴歸 6 自由度相對位姿；網路內以距離加權的鄰點外積計算逐點法向量（非以可學習權重估計），並同時學習動態物遮罩（mask），以遮罩加權的法向量幾何一致性損失約束訓練。之後利用法向量挑選平滑區點、以遮罩排除移動物，再以點到平面（point-to-plane）的掃描對地圖（scan-to-map）配準精化位姿以降低累積漂移。此方法需要真值位姿做監督訓練，且只在 KITTI 與 Ford 車載資料上評估。","A supervised end-to-end network regresses relative LiDAR pose from cylindrical range-image encodings, using in-network analytic normals and a learned dynamic-object mask, and a point-to-plane scan-to-map module uses those outputs to refine odometry.","full_text_reviewed","peer_reviewed_published","main_body","未在營建工地、完工建物、隧道或基礎設施測試；只使用 KITTI 與 Ford 車載資料。其監督式訓練需真值位姿，在工地取得真值的成本未被討論。補充材料說明以 fireConv 等輕量模組降低參數量，目的在於讓網路能在行動機器人或背包系統等低效能平台執行，但論文未在這類平台實測。",[20],"public_benchmark",[22,23,24],"model trained only on KITTI was applied to the Ford dataset without fine-tuning and reported the best average errors among the methods the authors ran (Sec. 4.2, Table 1)","learned mask highlights moving objects and vegetation so they receive less weight (Sec. 4.4, Fig. 9)","total processing under the 0.1 s scan period on a GPU plus CPU platform (Sec. 4.5, Table 4)",[26,27,28,29,30],"requires ground-truth poses for training, which the authors say limits application scenarios (Sec. 5)","point clouds must be encoded into data matrices instead of processed directly (Sec. 5)","LOAM baseline numbers were produced by the authors after modifying the original code; they differ from the LOAM paper's numbers (Sec. 4.2, Table 1 note)","supervised learned LO methods rely on ground truth and target driving data with limited rotational motion (nubert2021delora Sec. II)","(inference) no test on handheld, legged, indoor or construction data, so domain transfer to construction sites is unknown",[32],"Velodyne HDL-64 3D LiDAR (KITTI and Ford data; Sec. 4, 4.5)",[34],"vehicle","supervised Siamese CNN regressing relative 6-DoF pose (translation + quaternion) from two scans, followed by iterative point-to-plane scan-to-map refinement (Sec. 3.3, 3.5)","implicit in the network (cylindrical range\u002Fintensity matrix input) with a mask-weighted normal-consistency loss; normals are computed inside the network by range-weighted cross products of four grid neighbours plus moving-average smoothing (Eq. 3), not by learned weights; mapping uses point-to-plane correspondences to map points, selecting smooth-area points by a convolution over the normal channels and excluding masked points (Sec. 3.2, 3.4, 3.5)","discrete poses (scan-to-scan); linear motion interpolation used inside the mapping step (Sec. 3.5)","mapping module removes motion distortion by linear interpolation of the LO-Net odometry before scan-to-map matching (Sec. 3.5)","none (not implemented for any method in the experiments, Sec. 4.2)","none","sliding point map holding the last n_m = 100 transformed scans (Sec. 3.5, Sec. 4 implementation details)","supervised training with ground-truth poses (KITTI 00-06); mask sub-network pre-trained on the KITTI 3D object detection dataset (Sec. 4)","6-DoF trajectory and an accumulated point map shown in figures; map export is not described (Fig. 4)","about 80.1 ms per scan on KITTI Seq. 00: 8.5 ms data preparation (CPU), 10.2 ms inference (GTX 1080 Ti GPU), 61.4 ms mapping (CPU, Intel Core i7 3.4 GHz) (Sec. 4.5, Table 4)",null,"not_verified",[48],{"relation":49,"title":50,"doi_or_url":51},"preprint","LO-Net: Deep Real-time Lidar Odometry (arXiv v1 2019-04-17, v2 2020-01-17)","https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.08242",{"id":5,"kind":53,"shortName":7,"title":8,"authors":54,"year":9,"venue":62,"venueType":63,"publisher":64,"volumeIssuePages":65,"doi":66,"arxivId":67,"url":68,"firstPublicDate":69,"publicationStatus":16,"metadataStatus":70,"fulltextStatus":15,"era":10,"classicReason":71,"codeUrl":45,"cluster":11,"topics":72,"mdpi":73,"verification":74,"label":6,"fulltextRoute":75,"versionRead":76,"addedByCensus":73},"method",[55,56,57,58,59,60,61],"Qing Li","Shaoyang Chen","Cheng Wang","Xin Li","Chenglu Wen","Ming Cheng","Jonathan Li","2019 IEEE\u002FCVF Conference on Computer Vision and Pattern Recognition (CVPR)","conference","IEEE","pp. 8465-8474 (Crossref); the CVF open-access PDF footer shows 8473-8482","10.1109\u002Fcvpr.2019.00867","1904.08242","https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_CVPR_2019\u002Fpapers\u002FLi_LO-Net_Deep_Real-Time_Lidar_Odometry_CVPR_2019_paper.pdf","2019-04-17","metadata_verified","not_applicable",[11],false,"corrected","arXiv","arXiv v2 (2020-01-17) with supplementary material (network parameters, trajectory plots); main text and Table 1 and Table 4 cross-checked against the CVF open-access accepted version",[78,86,90,96,101,107],{"category":79,"model":80,"canonical":81,"role":82,"dataset":83,"specs":84,"locator":85},"lidar","Velodyne HDL-64","Velodyne HDL-64E","dataset sensor","KITTI odometry","64 laser beams, 10 Hz, about 1.3 million points\u002Fsecond; encoded as 64 x 1800 matrices cropped to 1792","Sec. 4 implementation details; Sec. 4.5",{"category":79,"model":80,"canonical":81,"role":82,"dataset":87,"specs":88,"locator":89},"Ford Campus Vision and Lidar","horizontally scanning 3D lidar mounted on top of a vehicle; rotates at 10 Hz","Sec. 4.1; Sec. 4.5",{"category":91,"model":92,"canonical":92,"role":93,"dataset":83,"specs":94,"locator":95},"gnss","IMU\u002FGPS (models not named)","reference or ground truth","ground-truth poses for KITTI sequences 00-10","Sec. 4.1",{"category":97,"model":98,"canonical":98,"role":93,"dataset":87,"specs":99,"locator":100},"imu","IMU (model not named)","Ford ground-truth trajectories generated from IMU readings","Supplementary Fig. 18 caption; Sec. 4.1",{"category":102,"model":103,"canonical":103,"role":104,"dataset":45,"specs":105,"locator":106},"compute","NVIDIA 1080 Ti","compute for runtime","GPU for training and for 10.2 ms inference","Sec. 4 implementation details; Sec. 4.5; Table 4",{"category":102,"model":108,"canonical":108,"role":104,"dataset":45,"specs":109,"locator":110},"Intel Core i7 3.4GHz 4-core CPU","data preparation 8.5 ms and mapping 61.4 ms per scan","Sec. 4.5; Table 4",[],{"totalRows":113,"groupCount":114,"groups":115,"others":1175},119,10,[116,418,664,930],{"slug":117,"group":118,"sourceId":5,"sourceLabel":6,"table":119,"selfRows":120,"metrics":121,"seqs":129,"entrants":148,"cells":169,"outcomes":411,"locators":413,"hardware":414,"wordings":415,"notes":416},"lonet2019-table-1","lonet2019:Table 1","Table 1",32,[122,126],{"label":123,"unit":124,"statistic":125,"alignment":40},"t_rel: average translational RMSE (%) on length of 100 m-800 m","%","mean",{"label":127,"unit":128,"statistic":125,"alignment":40},"r_rel: average rotational RMSE (deg\u002F100 m) on length of 100 m-800 m","deg\u002F100m",[130,133,135,137,139,141,143,146],{"dataset":83,"sequence":131,"environment":132},"07 (not used for training)","urban, country and highway driving, vehicle-mounted Velodyne HDL-64",{"dataset":83,"sequence":134,"environment":132},"08 (not used for training)",{"dataset":83,"sequence":136,"environment":132},"09 (not used for training)",{"dataset":83,"sequence":138,"environment":132},"10 (not used for training)",{"dataset":83,"sequence":140,"environment":132},"mean over 00-06 (training sequences)",{"dataset":83,"sequence":142,"environment":132},"mean over 07-10 (test sequences)",{"dataset":87,"sequence":144,"environment":145},"Ford-1","urban campus driving with many moving vehicles, roof-mounted lidar",{"dataset":87,"sequence":147,"environment":145},"Ford-2",[149,153,156,159,161,164,166,167],{"name":150,"methodId":151,"linkable":152,"proposed":73,"self":73},"ICP-po2po (PCL)","besl1992icp",true,{"name":154,"methodId":155,"linkable":152,"proposed":73,"self":73},"ICP-po2pl (PCL)","chen1992pointtoplane",{"name":157,"methodId":158,"linkable":152,"proposed":73,"self":73},"GICP [30]","segal2009gicp",{"name":160,"methodId":45,"linkable":73,"proposed":73,"self":73},"CLS [34]",{"name":162,"methodId":163,"linkable":152,"proposed":73,"self":73},"LOAM [45] (authors' modified re-run)","loam2017_auro",{"name":165,"methodId":45,"linkable":73,"proposed":73,"self":73},"Velas et al. [35] (values from [35])",{"name":7,"methodId":5,"linkable":152,"proposed":152,"self":152},{"name":168,"methodId":5,"linkable":152,"proposed":152,"self":152},"LO-Net+Mapping",[170,174,177,179,181,184,186,189,191,194,196,199,201,204,206,209,211,213,215,217,219,221,223,225,227,229,231,233,235,236,238,240,242,244,246,248,250,251,253,255,257,259,261,263,265,267,269,271,273,275,277,278,280,282,284,286,288,290,292,294,295,297,299,301,303,305,307,309,311,313,315,317,319,321,323,325,326,328,330,332,334,336,337,338,339,341,342,344,345,347,348,350,351,352,353,354,355,357,359,361,362,364,366,368,370,372,374,376,378,380,381,383,385,387,388,390,392,393,395,396,398,400,401,403,405,407,408,410],[171,171,171,172,173,171,173,173,171],0,5.17,-1,[171,175,171,176,173,171,173,173,171],1,3.35,[171,171,175,178,173,171,173,173,171],10.04,[171,175,175,180,173,171,173,173,171],4.93,[171,171,182,183,173,171,173,173,171],2,6.93,[171,175,182,185,173,171,173,173,171],2.89,[171,171,187,188,173,171,173,173,171],3,8.91,[171,175,187,190,173,171,173,173,171],4.74,[171,171,192,193,173,171,173,173,171],4,7.13,[171,175,192,195,173,171,173,173,171],3.08,[171,171,197,198,173,171,173,173,171],5,7.76,[171,175,197,200,173,171,173,173,171],3.98,[171,171,202,203,173,171,173,173,171],6,8.2,[171,175,202,205,173,171,173,173,171],2.64,[171,171,207,208,173,171,173,173,171],7,16.23,[171,175,207,210,173,171,173,173,171],2.84,[175,171,171,212,173,171,173,173,171],1.55,[175,175,171,214,173,171,173,173,171],1.42,[175,171,175,216,173,171,173,173,171],4.42,[175,175,175,218,173,171,173,173,171],2.14,[175,171,182,220,173,171,173,173,171],3.95,[175,175,182,222,173,171,173,173,171],1.71,[175,171,187,224,173,171,173,173,171],6.13,[175,175,187,226,173,171,173,173,171],2.6,[175,171,192,228,173,171,173,173,171],5.15,[175,175,192,230,173,171,173,173,171],1.91,[175,171,197,232,173,171,173,173,171],4.01,[175,175,197,234,173,171,173,173,171],1.97,[175,171,202,176,173,171,173,173,171],[175,175,202,237,173,171,173,173,171],1.65,[175,171,207,239,173,171,173,173,171],5.68,[175,175,207,241,173,171,173,173,171],1.96,[182,171,171,243,173,171,173,173,171],0.64,[182,175,171,245,173,171,173,173,171],0.45,[182,171,175,247,173,171,173,173,171],1.58,[182,175,175,249,173,171,173,173,171],0.75,[182,171,182,234,173,171,173,173,171],[182,175,182,252,173,171,173,173,171],0.77,[182,171,187,254,173,171,173,173,171],1.31,[182,175,187,256,173,171,173,173,171],0.62,[182,171,192,258,173,171,173,173,171],2.23,[182,175,192,260,173,171,173,173,171],0.78,[182,171,197,262,173,171,173,173,171],1.38,[182,175,197,264,173,171,173,173,171],0.65,[182,171,202,266,173,171,173,173,171],3.07,[182,175,202,268,173,171,173,173,171],1.17,[182,171,207,270,173,171,173,173,171],5.11,[182,175,207,272,173,171,173,173,171],1.47,[187,171,171,274,173,171,173,173,171],1.04,[187,175,171,276,173,171,173,173,171],0.73,[187,171,175,218,173,171,173,173,171],[187,175,175,279,173,171,173,173,171],1.05,[187,171,182,281,173,171,173,173,171],1.95,[187,175,182,283,173,171,173,173,171],0.92,[187,171,187,285,173,171,173,173,171],3.46,[187,175,187,287,173,171,173,173,171],1.28,[187,171,192,289,173,171,173,173,171],2.11,[187,175,192,291,173,171,173,173,171],0.86,[187,171,197,293,173,171,173,173,171],2.15,[187,175,197,175,173,171,173,173,171],[187,171,202,296,173,171,173,173,171],10.54,[187,175,202,298,173,171,173,173,171],3.9,[187,171,207,300,173,171,173,173,171],14.78,[187,175,207,302,173,171,173,173,171],4.6,[192,171,171,304,173,171,173,173,171],0.69,[192,175,171,306,173,171,173,173,171],0.5,[192,171,175,308,173,171,173,173,171],1.18,[192,175,175,310,173,171,173,173,171],0.44,[192,171,182,312,173,171,173,173,171],1.2,[192,175,182,314,173,171,173,173,171],0.48,[192,171,187,316,173,171,173,173,171],1.51,[192,175,187,318,173,171,173,173,171],0.57,[192,171,192,320,173,171,173,173,171],1.35,[192,175,192,322,173,171,173,173,171],0.51,[192,171,197,324,173,171,173,173,171],1.15,[192,175,197,306,173,171,173,173,171],[192,171,202,327,173,171,173,173,171],1.68,[192,175,202,329,173,171,173,173,171],0.54,[192,171,207,331,173,171,173,173,171],1.78,[192,175,207,333,173,171,173,173,171],0.49,[197,171,171,335,173,171,173,173,171],1.77,[197,175,171,45,171,171,173,173,171],[197,171,175,185,173,171,173,173,171],[197,175,175,45,171,171,173,173,171],[197,171,182,340,173,171,173,173,171],4.94,[197,175,182,45,171,171,173,173,171],[197,171,187,343,173,171,173,173,171],3.27,[197,175,187,45,171,171,173,173,171],[197,171,192,346,173,171,173,173,171],3.12,[197,175,192,45,171,171,173,173,171],[197,171,197,349,173,171,173,173,171],3.22,[197,175,197,45,171,171,173,173,171],[197,171,202,45,171,171,173,173,171],[197,175,202,45,171,171,173,173,171],[197,171,207,45,171,171,173,173,171],[197,175,207,45,171,171,173,173,171],[202,171,171,356,173,171,173,173,171],1.7,[202,175,171,358,173,171,173,173,171],0.89,[202,171,175,360,173,171,173,173,171],2.12,[202,175,175,252,173,171,173,173,171],[202,171,182,363,173,171,173,173,171],1.37,[202,175,182,365,173,171,173,173,171],0.58,[202,171,187,367,173,171,173,173,171],1.8,[202,175,187,369,173,171,173,173,171],0.93,[202,171,192,371,173,171,173,173,171],1.09,[202,175,192,373,173,171,173,173,171],0.63,[202,171,197,375,173,171,173,173,171],1.75,[202,175,197,377,173,171,173,173,171],0.79,[202,171,202,379,173,171,173,173,171],2.27,[202,175,202,256,173,171,173,173,171],[202,171,207,382,173,171,173,173,171],2.18,[202,175,207,384,173,171,173,173,171],0.59,[207,171,171,386,173,171,173,173,171],0.56,[207,175,171,245,173,171,173,173,171],[207,171,175,389,173,171,173,173,171],1.08,[207,175,175,391,173,171,173,173,171],0.43,[207,171,182,252,173,171,173,173,171],[207,175,182,394,173,171,173,173,171],0.38,[207,171,187,283,173,171,173,173,171],[207,175,187,397,173,171,173,173,171],0.41,[207,171,192,399,173,171,173,173,171],0.81,[207,175,192,310,173,171,173,173,171],[207,171,197,402,173,171,173,173,171],0.83,[207,175,197,404,173,171,173,173,171],0.42,[207,171,202,406,173,171,173,173,171],1.1,[207,175,202,306,173,171,173,173,171],[207,171,207,409,173,171,173,173,171],1.29,[207,175,207,310,173,171,173,173,171],[412],"not_reported (NA)",[119],[],[],[417],"KITTI odometry metric: t_rel = average translational RMSE (%) and r_rel = average rotational RMSE (deg\u002F100 m) over 100-800 m lengths. LO-Net trained on KITTI 00-06 and tested on 07-10 and on Ford without fine-tuning; loop closure disabled for all methods. LOAM values outside brackets come from the authors' modified re-run; bracketed values are quoted from the LOAM paper [45]. Velas et al. values quoted from [35] (r_rel and Ford NA). ICP variants run with PCL. Truncated: per-sequence rows 00-06 (training sequences) omitted; the mean over them (mean-dagger) is kept.",{"slug":419,"group":420,"sourceId":421,"sourceLabel":422,"table":423,"selfRows":424,"metrics":425,"seqs":428,"entrants":452,"cells":479,"outcomes":658,"locators":659,"hardware":660,"wordings":661,"notes":662},"litamin2-2021-table-iii","litamin2_2021:Table III","litamin2_2021","Yokozuka et al., 2021","Table III",22,[426],{"label":427,"unit":124,"statistic":125,"alignment":71},"KITTI stats translation [%]",[429,432,434,436,438,440,442,444,446,448,450],{"dataset":83,"sequence":430,"environment":431},"00 (4541 frames)","vehicle, road",{"dataset":83,"sequence":433,"environment":431},"01 (1101 frames)",{"dataset":83,"sequence":435,"environment":431},"02 (4661 frames)",{"dataset":83,"sequence":437,"environment":431},"03 (801 frames)",{"dataset":83,"sequence":439,"environment":431},"04 (271 frames)",{"dataset":83,"sequence":441,"environment":431},"05 (2761 frames)",{"dataset":83,"sequence":443,"environment":431},"06 (1101 frames)",{"dataset":83,"sequence":445,"environment":431},"07 (1101 frames)",{"dataset":83,"sequence":447,"environment":431},"08 (4071 frames)",{"dataset":83,"sequence":449,"environment":431},"09 (1591 frames)",{"dataset":83,"sequence":451,"environment":431},"10 (1201 frames)",[453,455,457,459,462,464,467,470,473,475,477],{"name":454,"methodId":421,"linkable":152,"proposed":152,"self":73},"LiTAMIN2 (ICP+Cov), without loop closure",{"name":456,"methodId":421,"linkable":152,"proposed":152,"self":73},"LiTAMIN2 (ICP), without loop closure",{"name":458,"methodId":45,"linkable":73,"proposed":73,"self":73},"LiTAMIN [2], without loop closure",{"name":460,"methodId":461,"linkable":152,"proposed":73,"self":73},"SuMa (Frame-to-Frame)","suma2018",{"name":463,"methodId":461,"linkable":152,"proposed":73,"self":73},"SuMa (Frame-to-Model), without loop closure",{"name":465,"methodId":466,"linkable":152,"proposed":73,"self":73},"LeGO-LOAM","legoloam2018",{"name":468,"methodId":469,"linkable":73,"proposed":73,"self":73},"hdl graph slam","koide2019_hdlgraphslam",{"name":471,"methodId":472,"linkable":152,"proposed":73,"self":73},"LOAM (open source, run by authors)","loam2014",{"name":474,"methodId":163,"linkable":152,"proposed":73,"self":73},"LOAM (from [10])",{"name":476,"methodId":5,"linkable":152,"proposed":73,"self":152},"LO-Net (Frame-to-Frame)",{"name":478,"methodId":5,"linkable":152,"proposed":73,"self":152},"LO-Net (Frame-to-Model)",[480,481,483,485,487,488,490,491,492,495,497,499,500,502,504,506,508,510,511,512,514,515,517,519,521,523,524,526,527,528,529,530,531,533,534,536,538,540,542,544,545,547,549,551,552,554,555,557,558,560,561,562,564,565,567,568,570,572,573,575,577,578,579,581,582,583,585,587,589,591,592,594,596,598,599,600,602,604,606,608,610,611,613,614,616,617,619,620,622,623,624,625,626,628,629,630,631,632,633,634,635,636,638,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657],[171,171,171,260,173,171,173,173,171],[171,171,175,482,173,171,173,173,171],2.1,[171,171,182,484,173,171,173,173,171],0.95,[171,171,187,486,173,171,173,173,171],0.96,[171,171,192,279,173,171,173,173,171],[171,171,197,489,173,171,173,173,171],0.55,[171,171,202,489,173,171,173,173,171],[171,171,207,314,173,171,173,173,171],[171,171,493,494,173,171,173,173,171],8,1.01,[171,171,496,304,173,171,173,173,171],9,[171,171,114,498,173,171,173,173,171],0.8,[175,171,171,249,173,171,173,173,171],[175,171,175,501,173,171,173,173,171],1.88,[175,171,182,503,173,171,173,173,171],0.99,[175,171,187,505,173,171,173,173,171],0.84,[175,171,192,507,173,171,173,173,171],0.9,[175,171,197,509,173,171,173,173,171],0.74,[175,171,202,245,173,171,173,173,171],[175,171,207,489,173,171,173,173,171],[175,171,493,513,173,171,173,173,171],1.25,[175,171,496,509,173,171,173,173,171],[175,171,114,516,173,171,173,173,171],1.36,[182,171,171,518,173,171,173,173,171],0.91,[182,171,175,520,173,171,173,173,171],11.3,[182,171,182,522,173,171,173,173,171],1.3,[182,171,187,268,173,171,173,173,171],[182,171,192,525,173,171,173,173,171],18.7,[182,171,197,249,173,171,173,173,171],[182,171,202,384,173,171,173,173,171],[182,171,207,314,173,171,173,173,171],[182,171,493,274,173,171,173,173,171],[182,171,496,503,173,171,173,173,171],[182,171,114,532,173,171,173,173,171],3.78,[187,171,171,289,173,171,173,173,171],[187,171,175,535,173,171,173,173,171],4.31,[187,171,182,537,173,171,173,173,171],2.32,[187,171,187,539,173,171,173,173,171],1.63,[187,171,192,541,173,171,173,173,171],11.9,[187,171,197,543,173,171,173,173,171],1.46,[187,171,202,358,173,171,173,173,171],[187,171,207,546,173,171,173,173,171],1.87,[187,171,493,548,173,171,173,173,171],2.56,[187,171,496,550,173,171,173,173,171],1.99,[187,171,114,293,173,171,173,173,171],[192,171,171,553,173,171,173,173,171],0.72,[192,171,175,335,173,171,173,173,171],[192,171,182,556,173,171,173,173,171],1.06,[192,171,187,318,173,171,173,173,171],[192,171,192,559,173,171,173,173,171],0.39,[192,171,197,306,173,171,173,173,171],[192,171,202,559,173,171,173,173,171],[192,171,207,563,173,171,173,173,171],0.37,[192,171,493,494,173,171,173,173,171],[192,171,496,566,173,171,173,173,171],0.47,[192,171,114,304,173,171,173,173,171],[197,171,171,569,173,171,173,173,171],2.17,[197,171,175,571,173,171,173,173,171],13.4,[197,171,182,569,173,171,173,173,171],[197,171,187,574,173,171,173,173,171],2.34,[197,171,192,576,173,171,173,173,171],1.27,[197,171,197,287,173,171,173,173,171],[197,171,202,556,173,171,173,173,171],[197,171,207,580,173,171,173,173,171],1.12,[197,171,493,550,173,171,173,173,171],[197,171,496,234,173,171,173,173,171],[197,171,114,584,173,171,173,173,171],2.21,[202,171,171,586,173,171,173,173,171],3.92,[202,171,175,588,173,171,173,173,171],93.5,[202,171,182,590,173,171,173,173,171],11.2,[202,171,187,222,173,171,173,173,171],[202,171,192,593,173,171,173,173,171],96,[202,171,197,595,173,171,173,173,171],1.41,[202,171,202,597,173,171,173,173,171],11.1,[202,171,207,287,173,171,173,173,171],[202,171,493,569,173,171,173,173,171],[202,171,496,601,173,171,173,173,171],4.32,[202,171,114,603,173,171,173,173,171],2.36,[207,171,171,605,173,171,173,173,171],1.92,[207,171,175,607,173,171,173,173,171],2.69,[207,171,182,609,173,171,173,173,171],4.05,[207,171,187,262,173,171,173,173,171],[207,171,192,612,173,171,173,173,171],1.21,[207,171,197,268,173,171,173,173,171],[207,171,202,615,173,171,173,173,171],0.82,[207,171,207,518,173,171,173,173,171],[207,171,493,618,173,171,173,173,171],1.43,[207,171,496,612,173,171,173,173,171],[207,171,114,621,173,171,173,173,171],1.53,[493,171,171,260,173,171,173,173,171],[493,171,175,618,173,171,173,173,171],[493,171,182,283,173,171,173,173,171],[493,171,187,291,173,171,173,173,171],[493,171,192,627,173,171,173,173,171],0.71,[493,171,197,318,173,171,173,173,171],[493,171,202,264,173,171,173,173,171],[493,171,207,373,173,171,173,173,171],[493,171,493,580,173,171,173,173,171],[493,171,496,252,173,171,173,173,171],[493,171,114,377,173,171,173,173,171],[496,171,171,272,173,171,173,173,171],[496,171,175,516,173,171,173,173,171],[496,171,182,637,173,171,173,173,171],1.52,[496,171,187,639,173,171,173,173,171],1.03,[496,171,192,322,173,171,173,173,171],[496,171,197,274,173,171,173,173,171],[496,171,202,627,173,171,173,173,171],[496,171,207,356,173,171,173,173,171],[496,171,493,360,173,171,173,173,171],[496,171,496,363,173,171,173,173,171],[496,171,114,367,173,171,173,173,171],[114,171,171,260,173,171,173,173,171],[114,171,175,214,173,171,173,173,171],[114,171,182,494,173,171,173,173,171],[114,171,187,276,173,171,173,173,171],[114,171,192,386,173,171,173,173,171],[114,171,197,256,173,171,173,173,171],[114,171,202,489,173,171,173,173,171],[114,171,207,386,173,171,173,173,171],[114,171,493,389,173,171,173,173,171],[114,171,496,252,173,171,173,173,171],[114,171,114,283,173,171,173,173,171],[],[423],[],[],[663],"KITTI odometry sequences 00-10; KITTI stats translation error (%) averaged over 100-800 m segments with the benchmark code; rows without loop closure; LiTAMIN2 with 3 m voxels; LOAM (from [10]), LO-Net and DeepLO values copied from their papers",{"slug":665,"group":666,"sourceId":667,"sourceLabel":668,"table":669,"selfRows":670,"metrics":671,"seqs":680,"entrants":713,"cells":747,"outcomes":922,"locators":923,"hardware":924,"wordings":927,"notes":928},"mulls2021-table-ii","mulls2021:Table II","mulls2021","Pan et al., 2021","Table II",16,[672,675,677],{"label":673,"unit":124,"statistic":125,"alignment":674},"ATE [%] (average translation error)","not_reported",{"label":676,"unit":128,"statistic":125,"alignment":674},"ARE [deg\u002F100m] (average rotation error)",{"label":678,"unit":679,"statistic":674,"alignment":40},"time (s)\u002Fframe","s",[681,684,687,690,692,694,696,698,700,702,704,706,709,711],{"dataset":83,"sequence":682,"environment":683},"00","urban",{"dataset":83,"sequence":685,"environment":686},"01","highway",{"dataset":83,"sequence":688,"environment":689},"02","country",{"dataset":83,"sequence":691,"environment":689},"03",{"dataset":83,"sequence":693,"environment":689},"04",{"dataset":83,"sequence":695,"environment":689},"05",{"dataset":83,"sequence":697,"environment":683},"06",{"dataset":83,"sequence":699,"environment":683},"07",{"dataset":83,"sequence":701,"environment":683},"08",{"dataset":83,"sequence":703,"environment":689},"09",{"dataset":83,"sequence":705,"environment":689},"10",{"dataset":83,"sequence":707,"environment":708},"00-10 mean","urban, highway, country",{"dataset":83,"sequence":710,"environment":708},"11-21 mean (online test set)",{"dataset":83,"sequence":712,"environment":708},"per frame",[714,716,718,720,722,724,727,729,731,733,735,737,739,741,743,745],{"name":715,"methodId":472,"linkable":152,"proposed":73,"self":73},"LOAM [10]",{"name":717,"methodId":45,"linkable":73,"proposed":73,"self":73},"IMLS-SLAM [11]",{"name":719,"methodId":45,"linkable":73,"proposed":73,"self":73},"MC2SLAM [13]",{"name":721,"methodId":45,"linkable":73,"proposed":73,"self":73},"S4-SLAM [26]*",{"name":723,"methodId":45,"linkable":73,"proposed":73,"self":73},"PSF-LO [27]",{"name":725,"methodId":726,"linkable":152,"proposed":73,"self":73},"SUMA++ [16]*","sumapp2019",{"name":728,"methodId":45,"linkable":73,"proposed":73,"self":73},"LiTAMIN2 [51]*",{"name":730,"methodId":5,"linkable":152,"proposed":73,"self":152},"LO-Net [18]",{"name":732,"methodId":45,"linkable":73,"proposed":73,"self":73},"FALO [25]",{"name":734,"methodId":45,"linkable":73,"proposed":73,"self":73},"LoDoNet [28]",{"name":736,"methodId":667,"linkable":152,"proposed":152,"self":73},"MULLS-LO(mc)",{"name":738,"methodId":667,"linkable":152,"proposed":152,"self":73},"MULLS-SLAM(mc)*",{"name":740,"methodId":667,"linkable":152,"proposed":152,"self":73},"MULLS-LO(s1)",{"name":742,"methodId":667,"linkable":152,"proposed":152,"self":73},"MULLS-SLAM(m1)*",{"name":744,"methodId":667,"linkable":152,"proposed":152,"self":73},"MULLS-SLAM(m5)*",{"name":746,"methodId":667,"linkable":152,"proposed":152,"self":73},"MULLS-SLAM(s5m5)*",[748,749,750,751,752,753,754,755,756,757,758,759,761,763,765,768,769,770,772,774,776,778,779,780,781,782,783,785,786,788,789,790,791,792,793,794,796,798,800,801,803,804,805,806,808,809,810,811,812,814,815,816,817,818,819,821,822,824,825,827,828,829,830,831,833,835,837,838,839,840,842,843,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,871,873,875,877,878,879,880,881,882,883,884,886,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,904,905,907,908,910,912,915,916,918,920,921],[171,171,171,260,173,171,173,173,171],[171,171,175,618,173,171,173,173,171],[171,171,182,283,173,171,173,173,171],[171,171,187,291,173,171,173,173,171],[171,171,192,627,173,171,173,173,171],[171,171,197,318,173,171,173,173,171],[171,171,202,264,173,171,173,173,171],[171,171,207,373,173,171,173,173,171],[171,171,493,580,173,171,173,173,171],[171,171,496,252,173,171,173,173,171],[171,171,114,377,173,171,173,173,171],[171,171,760,505,173,171,173,173,171],11,[171,171,762,489,173,171,173,173,171],12,[171,175,762,764,173,171,173,173,171],0.13,[171,182,766,767,173,171,171,173,171],13,0.1,[175,171,171,306,173,171,173,173,171],[175,171,175,615,173,171,173,173,171],[175,171,182,771,173,171,173,173,171],0.53,[175,171,187,773,173,171,173,173,171],0.68,[175,171,192,775,173,171,173,173,171],0.33,[175,171,197,777,173,171,173,173,171],0.32,[175,171,202,775,173,171,173,173,171],[175,171,207,775,173,171,173,173,171],[175,171,493,498,173,171,173,173,171],[175,171,496,489,173,171,173,173,171],[175,171,114,771,173,171,173,173,171],[175,171,760,784,173,171,173,173,171],0.52,[175,171,762,304,173,171,173,173,171],[175,175,762,787,173,171,173,173,171],0.18,[175,182,766,513,173,171,171,173,171],[182,171,171,322,173,171,173,173,171],[182,171,175,377,173,171,173,173,171],[182,171,182,329,173,171,173,173,171],[182,171,187,264,173,171,173,173,171],[182,171,192,310,173,171,173,173,171],[182,171,197,795,173,171,173,173,171],0.27,[182,171,202,797,173,171,173,173,171],0.31,[182,171,207,799,173,171,173,173,171],0.34,[182,171,493,505,173,171,173,173,171],[182,171,496,802,173,171,173,173,171],0.46,[182,171,114,784,173,171,173,173,171],[182,171,760,784,173,171,173,173,171],[182,171,762,304,173,171,173,173,171],[182,175,762,807,173,171,173,173,171],0.16,[182,182,766,767,173,171,171,173,171],[187,171,760,283,173,171,173,173,171],[187,171,762,369,173,171,173,173,171],[187,175,762,394,173,171,173,173,171],[187,182,766,813,173,171,171,173,171],0.2,[192,171,760,509,173,171,173,173,171],[192,171,762,615,173,171,173,173,171],[192,175,762,777,173,171,173,173,171],[192,182,766,813,173,171,171,173,171],[197,171,171,243,173,171,173,173,171],[197,171,175,820,173,171,173,173,171],1.6,[197,171,182,175,173,171,173,173,171],[197,171,187,823,173,171,173,173,171],0.67,[197,171,192,563,173,171,173,173,171],[197,171,197,826,173,171,173,173,171],0.4,[197,171,202,802,173,171,173,173,171],[197,171,207,799,173,171,173,173,171],[197,171,493,406,173,171,173,173,171],[197,171,496,566,173,171,173,173,171],[197,171,114,832,173,171,173,173,171],0.66,[197,171,760,834,173,171,173,173,171],0.7,[197,175,760,836,173,171,173,173,171],0.29,[197,171,762,556,173,171,173,173,171],[197,175,762,799,173,171,173,173,171],[197,182,766,767,173,171,171,173,171],[202,171,760,841,173,171,173,173,171],0.85,[202,175,760,775,173,171,173,173,171],[202,182,766,844,173,171,171,173,171],0.01,[207,171,171,260,173,171,173,173,171],[207,171,175,214,173,171,173,173,171],[207,171,182,494,173,171,173,173,171],[207,171,187,276,173,171,173,173,171],[207,171,192,386,173,171,173,173,171],[207,171,197,256,173,171,173,173,171],[207,171,202,489,173,171,173,173,171],[207,171,207,386,173,171,173,173,171],[207,171,493,389,173,171,173,173,171],[207,171,496,252,173,171,173,173,171],[207,171,114,283,173,171,173,173,171],[207,171,760,402,173,171,173,173,171],[207,175,760,404,173,171,173,173,171],[207,171,762,375,173,171,173,173,171],[207,175,762,377,173,171,173,173,171],[207,182,766,767,173,171,171,173,171],[493,171,760,175,173,171,173,173,171],[493,175,760,559,173,171,173,173,171],[493,182,766,767,173,171,171,173,171],[496,171,760,576,173,171,173,173,171],[496,175,760,832,173,171,173,173,171],[114,171,171,322,173,171,173,173,171],[114,171,175,256,173,171,173,173,171],[114,171,182,489,173,171,173,173,171],[114,171,187,870,173,171,173,173,171],0.61,[114,171,192,872,173,171,173,173,171],0.35,[114,171,197,874,173,171,173,173,171],0.28,[114,171,202,876,173,171,173,173,171],0.24,[114,171,207,836,173,171,173,173,171],[114,171,493,498,173,171,173,173,171],[114,171,496,333,173,171,173,173,171],[114,171,114,870,173,171,173,173,171],[114,171,760,333,173,171,173,173,171],[114,175,760,807,173,171,173,173,171],[114,171,762,264,173,171,173,173,171],[114,175,762,885,173,171,173,173,171],0.19,[114,182,766,887,173,171,175,173,171],0.08,[760,171,171,329,173,171,173,173,171],[760,171,175,256,173,171,173,173,171],[760,171,182,304,173,171,173,173,171],[760,171,187,870,173,171,173,173,171],[760,171,192,872,173,171,173,173,171],[760,171,197,836,173,171,173,173,171],[760,171,202,836,173,171,173,173,171],[760,171,207,795,173,171,173,173,171],[760,171,493,402,173,171,173,173,171],[760,171,496,322,173,171,173,173,171],[760,171,114,870,173,171,173,173,171],[760,171,760,784,173,171,173,173,171],[760,175,760,764,173,171,173,173,171],[760,182,766,767,173,171,175,173,171],[762,171,760,903,173,171,173,173,171],2.57,[762,175,760,639,173,171,173,173,171],[762,182,766,906,173,171,175,173,171],0.03,[766,171,760,252,173,171,173,173,171],[766,175,760,909,173,171,173,173,171],0.25,[766,182,766,911,173,171,175,173,171],0.05,[913,171,760,914,173,171,173,173,171],14,0.6,[913,175,760,787,173,171,173,173,171],[913,182,766,917,173,171,175,173,171],0.07,[919,171,760,870,173,171,173,173,171],15,[919,175,760,787,173,171,173,173,171],[919,182,766,887,173,171,175,173,171],[],[669],[925,926],"not_reported (value taken from original paper or KITTI leaderboard)","Intel Core i7-7700HQ @2.80GHz",[],[929],"KITTI odometry ATE [%] and ARE [deg\u002F100m], averaged over 100-800 m segments; baseline values from original papers and KITTI leaderboard; * = with loop closure; time in s per frame",{"slug":931,"group":932,"sourceId":933,"sourceLabel":934,"table":119,"selfRows":766,"metrics":935,"seqs":943,"entrants":968,"cells":989,"outcomes":1169,"locators":1170,"hardware":1171,"wordings":1172,"notes":1173},"pwclonet2021-table-1","pwclonet2021:Table 1","pwclonet2021","Wang et al., 2021c",[936,939,941],{"label":937,"unit":124,"statistic":938,"alignment":71},"trel (average translational RMSE, %)","RMSE",{"label":940,"unit":124,"statistic":938,"alignment":71},"Mean on 07-10, trel",{"label":942,"unit":128,"statistic":938,"alignment":71},"Mean on 07-10, rrel (deg\u002F100m)",[944,946,948,950,952,954,956,958,960,962,964,966],{"dataset":83,"sequence":945,"environment":431},"00* (training)",{"dataset":83,"sequence":947,"environment":431},"01* (training)",{"dataset":83,"sequence":949,"environment":431},"02* (training)",{"dataset":83,"sequence":951,"environment":431},"03* (training)",{"dataset":83,"sequence":953,"environment":431},"04* (training)",{"dataset":83,"sequence":955,"environment":431},"05* (training)",{"dataset":83,"sequence":957,"environment":431},"06* (training)",{"dataset":83,"sequence":959,"environment":431},"07 (test)",{"dataset":83,"sequence":961,"environment":431},"08 (test)",{"dataset":83,"sequence":963,"environment":431},"09 (test)",{"dataset":83,"sequence":965,"environment":431},"10 (test)",{"dataset":83,"sequence":967,"environment":431},"mean on 07-10 (test)",[969,971,973,975,977,979,981,983,985,987],{"name":970,"methodId":163,"linkable":152,"proposed":73,"self":73},"Full LOAM [31]",{"name":972,"methodId":151,"linkable":152,"proposed":73,"self":73},"ICP-po2po",{"name":974,"methodId":155,"linkable":152,"proposed":73,"self":73},"ICP-po2pl",{"name":976,"methodId":158,"linkable":152,"proposed":73,"self":73},"GICP [19]",{"name":978,"methodId":45,"linkable":73,"proposed":73,"self":73},"CLS [21]",{"name":980,"methodId":45,"linkable":73,"proposed":73,"self":73},"Velas et al. [22]",{"name":982,"methodId":5,"linkable":152,"proposed":73,"self":152},"LO-Net [10]",{"name":984,"methodId":45,"linkable":73,"proposed":73,"self":73},"DMLO [11]",{"name":986,"methodId":472,"linkable":152,"proposed":73,"self":73},"LOAM w\u002Fo mapping (published code run by authors)",{"name":988,"methodId":933,"linkable":152,"proposed":152,"self":73},"Ours (PWCLO-Net)",[990,991,993,995,997,999,1000,1001,1002,1003,1004,1005,1007,1009,1011,1013,1015,1017,1019,1021,1022,1023,1024,1025,1026,1028,1030,1032,1034,1035,1037,1039,1041,1042,1043,1044,1045,1046,1048,1050,1051,1053,1055,1056,1058,1060,1061,1062,1063,1064,1065,1067,1069,1070,1072,1073,1074,1076,1078,1079,1080,1081,1082,1083,1085,1087,1089,1091,1093,1094,1095,1097,1098,1099,1100,1101,1102,1104,1105,1106,1107,1108,1109,1110,1111,1112,1113,1114,1115,1117,1119,1120,1121,1122,1123,1125,1127,1129,1131,1133,1135,1137,1139,1140,1142,1144,1146,1148,1150,1152,1153,1154,1155,1157,1158,1159,1160,1161,1163,1164,1166,1168],[171,171,171,406,173,171,173,173,171],[171,171,175,992,173,171,173,173,171],2.79,[171,171,182,994,173,171,173,173,171],1.54,[171,171,187,996,173,171,173,173,171],1.13,[171,171,192,998,173,171,173,173,171],1.45,[171,171,197,249,173,171,173,173,171],[171,171,202,553,173,171,173,173,171],[171,171,207,304,173,171,173,173,171],[171,171,493,308,173,171,173,173,171],[171,171,496,312,173,171,173,173,171],[171,171,114,316,173,171,173,173,171],[171,175,760,1006,173,171,173,173,171],1.145,[171,182,760,1008,173,171,173,173,171],0.498,[175,171,171,1010,173,171,173,173,171],6.88,[175,171,175,1012,173,171,173,173,171],11.21,[175,171,182,1014,173,171,173,173,171],8.21,[175,171,187,1016,173,171,173,173,171],11.07,[175,171,192,1018,173,171,173,173,171],6.64,[175,171,197,1020,173,171,173,173,171],3.97,[175,171,202,281,173,171,173,173,171],[175,171,207,172,173,171,173,173,171],[175,171,493,178,173,171,173,173,171],[175,171,496,183,173,171,173,173,171],[175,171,114,188,173,171,173,173,171],[175,175,760,1027,173,171,173,173,171],7.763,[175,182,760,1029,173,171,173,173,171],3.978,[182,171,171,1031,173,171,173,173,171],3.8,[182,171,175,1033,173,171,173,173,171],13.53,[182,171,182,496,173,171,173,173,171],[182,171,187,1036,173,171,173,173,171],2.72,[182,171,192,1038,173,171,173,173,171],2.96,[182,171,197,1040,173,171,173,173,171],2.29,[182,171,202,335,173,171,173,173,171],[182,171,207,212,173,171,173,173,171],[182,171,493,216,173,171,173,173,171],[182,171,496,220,173,171,173,173,171],[182,171,114,224,173,171,173,173,171],[182,175,760,1047,173,171,173,173,171],4.013,[182,182,760,1049,173,171,173,173,171],1.968,[187,171,171,409,173,171,173,173,171],[187,171,175,1052,173,171,173,173,171],4.39,[187,171,182,1054,173,171,173,173,171],2.53,[187,171,187,327,173,171,173,173,171],[187,171,192,1057,173,171,173,173,171],3.76,[187,171,197,1059,173,171,173,173,171],1.02,[187,171,202,283,173,171,173,173,171],[187,171,207,243,173,171,173,173,171],[187,171,493,247,173,171,173,173,171],[187,171,496,234,173,171,173,173,171],[187,171,114,254,173,171,173,173,171],[187,175,760,1066,173,171,173,173,171],1.375,[187,182,760,1068,173,171,173,173,171],0.648,[192,171,171,289,173,171,173,173,171],[192,171,175,1071,173,171,173,173,171],4.22,[192,171,182,1040,173,171,173,173,171],[192,171,187,539,173,171,173,173,171],[192,171,192,1075,173,171,173,173,171],1.59,[192,171,197,1077,173,171,173,173,171],1.98,[192,171,202,283,173,171,173,173,171],[192,171,207,274,173,171,173,173,171],[192,171,493,218,173,171,173,173,171],[192,171,496,281,173,171,173,173,171],[192,171,114,285,173,171,173,173,171],[192,175,760,1084,173,171,173,173,171],2.148,[192,182,760,1086,173,171,173,173,171],0.995,[197,171,171,1088,173,171,173,173,171],3.02,[197,171,175,1090,173,171,173,173,171],4.44,[197,171,182,1092,173,171,173,173,171],3.42,[197,171,187,340,173,171,173,173,171],[197,171,192,335,173,171,173,173,171],[197,171,197,1096,173,171,173,173,171],2.35,[197,171,202,501,173,171,173,173,171],[197,171,207,335,173,171,173,173,171],[197,171,493,185,173,171,173,173,171],[197,171,496,340,173,171,173,173,171],[197,171,114,343,173,171,173,173,171],[197,175,760,1103,173,171,173,173,171],3.218,[202,171,171,272,173,171,173,173,171],[202,171,175,516,173,171,173,173,171],[202,171,182,637,173,171,173,173,171],[202,171,187,639,173,171,173,173,171],[202,171,192,322,173,171,173,173,171],[202,171,197,274,173,171,173,173,171],[202,171,202,627,173,171,173,173,171],[202,171,207,356,173,171,173,173,171],[202,171,493,360,173,171,173,173,171],[202,171,496,363,173,171,173,173,171],[202,171,114,367,173,171,173,173,171],[202,175,760,1116,173,171,173,173,171],1.748,[202,182,760,1118,173,171,173,173,171],0.793,[207,171,207,276,173,171,173,173,171],[207,171,493,389,173,171,173,173,171],[207,171,496,406,173,171,173,173,171],[207,171,114,580,173,171,173,173,171],[207,175,760,1124,173,171,173,173,171],1.008,[207,182,760,1126,173,171,173,173,171],0.538,[493,171,171,1128,173,171,173,173,171],15.99,[493,171,175,1130,173,171,173,173,171],3.43,[493,171,182,1132,173,171,173,173,171],9.4,[493,171,187,1134,173,171,173,173,171],18.18,[493,171,192,1136,173,171,173,173,171],9.59,[493,171,197,1138,173,171,173,173,171],9.16,[493,171,202,188,173,171,173,173,171],[493,171,207,1141,173,171,173,173,171],10.87,[493,171,493,1143,173,171,173,173,171],12.72,[493,171,496,1145,173,171,173,173,171],8.1,[493,171,114,1147,173,171,173,173,171],12.67,[493,175,760,1149,173,171,173,173,171],11.09,[493,182,760,1151,173,171,173,173,171],6.405,[496,171,171,260,173,171,173,173,171],[496,171,175,823,173,171,173,173,171],[496,171,182,291,173,171,173,173,171],[496,171,187,1156,173,171,173,173,171],0.76,[496,171,192,563,173,171,173,173,171],[496,171,197,245,173,171,173,173,171],[496,171,202,795,173,171,173,173,171],[496,171,207,914,173,171,173,173,171],[496,171,493,1162,173,171,173,173,171],1.26,[496,171,496,377,173,171,173,173,171],[496,171,114,1165,173,171,173,173,171],1.69,[496,175,760,1167,173,171,173,173,171],1.085,[496,182,760,333,173,171,173,173,171],[],[119],[],[],[1174],"KITTI odometry, trained on 00-06 (marked *) and tested on 07-10; trel = average translational RMSE (%) over 100-800 m subsequences; rows other than LOAM w\u002Fo mapping and Ours are copied from LO-Net [10]; LOAM is a full system with mapping, others are odometry only",[1176,1183,1188,1193,1199,1205],{"group":1177,"slug":1178,"sourceLabel":1179,"table":1180,"selfRows":762,"datasets":1181},"lim2024quatropp:Table 6","lim2024quatropp-table-6","Lim et al., 2024","Table 6",[1182],"KITTI",{"group":1184,"slug":1185,"sourceLabel":1186,"table":669,"selfRows":762,"datasets":1187},"nubert2021delora:Table II","nubert2021delora-table-ii","Nubert et al., 2021",[83],{"group":1189,"slug":1190,"sourceLabel":6,"table":1191,"selfRows":192,"datasets":1192},"lonet2019:Table 4","lonet2019-table-4","Table 4",[83],{"group":1194,"slug":1195,"sourceLabel":1196,"table":1197,"selfRows":192,"datasets":1198},"nerfloam2023:Table 5","nerfloam2023-table-5","Deng et al., 2023","Table 5",[83],{"group":1200,"slug":1201,"sourceLabel":1202,"table":1203,"selfRows":182,"datasets":1204},"zhang2024_3dlidarslam_survey:Table 8","zhang2024-3dlidarslam-survey-table-8","Zhang et al., 2024a","Table 8",[83],{"group":1206,"slug":1207,"sourceLabel":1202,"table":1208,"selfRows":182,"datasets":1209},"zhang2024_3dlidarslam_survey:Table 9","zhang2024-3dlidarslam-survey-table-9","Table 9",[83],1790510663070]