[{"data":1,"prerenderedAt":855},["ShallowReactive",2],{"method-trajlo2024":3},{"method":4,"reference":58,"equipment":78,"figures":133,"results":134},{"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":23,"limitations":28,"sensors":34,"platform":36,"estimator":40,"association":41,"timeModel":42,"deskew":43,"loopClosure":44,"globalOptimization":44,"mapRepresentation":45,"prior":44,"outputGeometry":46,"compute":47,"codeUrl":48,"codeLicense":49,"relatedVersions":50},"trajlo2024","Zheng & Zhu, 2024","Traj-LO","Traj-LO: In Defense of LiDAR-Only Odometry Using an Effective Continuous-Time Trajectory",2024,"recent","C05","odometry_with_local_mapping","Traj-LO 把 LiDAR 量測視為高頻串流點，以由多段線性插值組成的連續時間軌跡描述感測器運動，並在滑動視窗內同時最小化點到平面幾何誤差與軌跡平滑（運動學）約束。由於每個點都用其時間戳查詢對應位姿，因此不需另外做運動補償。作者主張僅靠 LiDAR 也能在快速運動與 IMU 飽和情境下運作，並支援多 LiDAR。","LiDAR-only odometry with a piecewise-linear continuous-time trajectory optimized in a sliding window, coupling point-to-plane geometry with smoothness constraints so no separate deskewing is needed.","full_text_reviewed","peer_reviewed_published","background","作者描述其使用的 Hilti 2021 資料集包含辦公室、實驗室與施工環境之室內序列及施工現場之室外序列，地面真值來自 Hilti PLT 300 全測站或動作擷取系統。表 III 的 Cons2 依 Hilti 2021 官方序列名稱推定為 Construction Site Outdoor 2（推論），該序列官方地面真值為全測站量測的 3 自由度稀疏點位；Traj-LO 在 Cons2 的 ATE 為 0.065 m（OS0-64）、0.135 m（MID70）與 0.063 m（兩者併用）。僅有軌跡層級評估，未評估點雲幾何。",[20,21,22],"public_benchmark","real_construction_site","independent_reference",[24,25,26,27],"On Hilti 2021 handheld sequences continuous-time methods outperform discrete-time LO with the non-repetitive Livox MID70 even without IMU, but MID70 accuracy is lower than with the Ouster OS0-64 and Traj-LO with MID70 alone diverged on the Lab sequence (Sec. IV-D; Table III)","Adding the vertical LiDAR (L1+L2) removes the large z-direction errors on the tnp sequences (ATE 0.049, 0.040, 0.049 m versus 0.505, 0.607, 0.101 m with L1 only) and on spms lowers ATE and avoids the spms_02 divergence (Sec. IV-C; Fig. 4; Table II)","KITTI online benchmark 0.58% translation and 0.0014 deg\u002Fm rotation error (Sec. IV-B)","Maps aggressive motion beyond the IMU range on the Point-LIO rotating platform where FAST-LIO fails (Sec. IV-E; Fig. 5)",[29,30,31,32,33],"Single horizontal LiDAR shows larger ATE during jerky take-off and landing phases (arXiv v1 Sec. IV-C and Fig. 4 on spms_03; this paragraph is not retained in the version of record)","Constant velocity within segments may not hold for fast motion, requiring short segments (Sec. III-B) (author-stated assumption)","Assumes the odometry starts from a stationary state and uses the first 0.3 s of points to initialize the map (Sec. III-E)","Single-LiDAR Traj-LO diverged on NTU VIRAL spms_02 and has ATE of 0.505 m and 0.607 m on tnp_01 and tnp_02 (Table II)","Odometry only; a complete LiDAR-only SLAM is left for future work (Sec. V)",[35],"3D LiDAR only (single or multiple, spinning and non-repetitive)",[37,38,39],"vehicle","UAV","handheld","Sliding-window nonlinear least squares (Gauss-Newton with analytic SE(3) Jacobians) over K+1 control poses of a piecewise-linear continuous-time trajectory (K = 4 segments of 0.03 s in experiments), with point-to-plane terms, a smoothness term penalizing velocity change between consecutive segments, and a Schur-complement marginalization prior with first-estimate Jacobians; marginalization lowered ATE in all four ablation settings (Table IV)","point-to-plane to map neighbours, normals from PCA of five closest points; no feature selection","continuous-time piecewise-linear segments in SE(3) (e.g., 0.01 s segments for an indoor aggressive sequence)","not required: each point is registered with the pose queried at its own timestamp","none","Spatial hashing voxel map (following CT-ICP and KISS-ICP) storing up to 20 points per voxel, 7 nearest voxels searched, new points dropped when a voxel is full, points farther than 100 m removed; voxel size 0.4 m indoor, 0.8 m outdoor and 0.2 m for the Point-LIO indoor sequence","odometry and voxel point map; export format not_reported","AMD Ryzen 9 5900X CPU; on NTU VIRAL nya01 (395 s) total processing 116.2 s with four segments and marginalization (173.2 s without) and 49.7 s with one segment, versus KISS-ICP 47.7 s, CT-ICP 152.8 s and FLOAM 25.6 s; memory capped at 208 MB because the map keeps points within a fixed radius","https:\u002F\u002Fgithub.com\u002Fkevin2431\u002FTraj-LO","MIT (LICENSE file checked)",[51,55],{"relation":52,"title":53,"doi_or_url":54},"preprint","Traj-LO (arXiv v1)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.13842",{"relation":56,"title":57,"doi_or_url":48},"code_release","kevin2431\u002FTraj-LO",{"id":5,"kind":59,"shortName":7,"title":8,"authors":60,"year":9,"venue":63,"venueType":64,"publisher":65,"volumeIssuePages":66,"doi":67,"arxivId":68,"url":69,"firstPublicDate":70,"publicationStatus":16,"metadataStatus":71,"fulltextStatus":15,"era":10,"classicReason":72,"codeUrl":48,"cluster":11,"topics":73,"mdpi":74,"verification":75,"label":6,"fulltextRoute":76,"versionRead":77,"addedByCensus":74},"method",[61,62],"Xin Zheng","Jianke Zhu","IEEE Robotics and Automation Letters","journal","IEEE","9(2):1961-1968","10.1109\u002Flra.2024.3352360","2309.13842","https:\u002F\u002Fapi.crossref.org\u002Fworks\u002F10.1109\u002FLRA.2024.3352360","2023-09-25","metadata_verified","not_applicable",[11],false,"corrected","NTU institutional (Chrome)","Version of record, IEEE RA-L 9(2):1961-1968 (IEEE Xplore HTML and PDF through NTU access); arXiv v1 also read",[79,87,92,96,101,106,110,114,117,121,126],{"category":80,"model":81,"canonical":82,"role":83,"dataset":84,"specs":85,"locator":86},"lidar","Velodyne HDL-64E S2","Velodyne HDL-64E","dataset sensor","KITTI odometry","KITTI points are motion-corrected, per-point time discarded","Sec. IV-A",{"category":80,"model":88,"canonical":88,"role":83,"dataset":89,"specs":90,"locator":91},"two 16-channel Ouster LiDARs (horizontal and vertical OS1-16)","NTU VIRAL","10 Hz with per-point relative timestamps","Sec. IV-A; Table II footnote",{"category":93,"model":94,"canonical":94,"role":83,"dataset":89,"specs":95,"locator":91},"imu","9-axis IMU (external IMU)","385 Hz; used only by the LIO baselines",{"category":97,"model":98,"canonical":98,"role":99,"dataset":89,"specs":100,"locator":86},"total_station","Leica Nova MS60","reference or ground truth","ground truth trajectory",{"category":80,"model":102,"canonical":102,"role":83,"dataset":103,"specs":104,"locator":105},"Ouster OS0-64","Hilti 2021","360 deg FoV at 10 Hz, handheld","Sec. IV-A; Table III footnote",{"category":80,"model":107,"canonical":107,"role":83,"dataset":103,"specs":108,"locator":109},"Livox MID70","70 deg circular FoV, non-repetitive Risley-prism scan, 10 Hz","Sec. IV-A; Sec. IV-D",{"category":93,"model":111,"canonical":111,"role":83,"dataset":103,"specs":112,"locator":113},"IMU embedded in the OS0-64","used only by LIO baselines","Table III footnote",{"category":97,"model":115,"canonical":115,"role":99,"dataset":103,"specs":116,"locator":86},"Hilti PLT 300 automated total station","millimeter-accurate ground truth (or MoCap)",{"category":118,"model":119,"canonical":119,"role":99,"dataset":103,"specs":120,"locator":86},"other","motion capture (MoCap) system","alternative ground truth source",{"category":80,"model":122,"canonical":122,"role":83,"dataset":123,"specs":124,"locator":125},"Livox Avia","Point-LIO dataset","rotating-platform and swinging-rope sequences beyond the IMU range","Sec. IV-A; Sec. IV-E",{"category":127,"model":128,"canonical":128,"role":129,"dataset":130,"specs":131,"locator":132},"compute","AMD Ryzen 9 5900X CPU","compute for runtime",null,"all experiments; runtime and memory in Table V","VoR Sec. IV",[],{"totalRows":135,"groupCount":136,"groups":137,"others":832},73,8,[138,377,574,706],{"slug":139,"group":140,"sourceId":141,"sourceLabel":142,"table":143,"selfRows":144,"metrics":145,"seqs":151,"entrants":189,"cells":202,"outcomes":369,"locators":371,"hardware":373,"wordings":374,"notes":375},"resple2025-table-ii-ntu-viral","resple2025:Table II (NTU VIRAL)","resple2025","Cao et al., 2025","Table II (NTU VIRAL)",18,[146],{"label":147,"unit":148,"statistic":149,"alignment":150},"APE (RMSE, meters)","m","RMSE","not_reported",[152,155,157,159,161,163,165,167,169,171,173,175,177,179,181,183,185,187],{"dataset":89,"sequence":153,"environment":154},"eee_01","campus indoor and outdoor, 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LiDAR-inertial)",[203,207,210,213,216,219,222,224,227,229,232,234,236,239,241,244,247,250,253,255,257,259,261,263,265,267,269,270,271,272,273,275,277,279,280,281,282,284,285,287,289,290,292,294,296,298,300,302,304,305,307,309,310,312,314,316,318,320,322,324,326,328,330,332,334,336,337,338,339,341,342,343,344,345,347,348,350,352,353,354,356,358,359,361,362,363,364,366,367,368],[204,204,204,205,206,204,206,206,204],0,0.055,-1,[204,204,208,209,206,204,206,206,204],1,0.039,[204,204,211,212,206,204,206,206,204],2,0.035,[204,204,214,215,206,204,206,206,204],3,0.047,[204,204,217,218,206,204,206,206,204],4,0.052,[204,204,220,221,206,204,206,206,204],5,0.05,[204,204,223,221,206,204,206,206,204],6,[204,204,225,226,206,204,206,206,204],7,0.058,[204,204,136,228,206,204,206,206,204],0.057,[204,204,230,231,206,204,206,206,204],9,0.048,[204,204,233,209,206,204,206,206,204],10,[204,204,235,209,206,204,206,206,204],11,[204,204,237,238,206,204,206,206,204],12,0.121,[204,204,240,130,204,204,206,206,204],13,[204,204,242,243,206,204,206,206,204],14,0.103,[204,204,245,246,206,204,206,206,204],15,0.505,[204,204,248,249,206,204,206,206,204],16,0.607,[204,204,251,252,206,204,206,206,204],17,0.101,[208,204,204,254,206,204,206,206,204],0.08,[208,204,208,256,206,204,206,206,204],0.07,[208,204,211,258,206,204,206,206,204],0.12,[208,204,214,260,206,204,206,206,204],0.06,[208,204,217,262,206,204,206,206,204],0.09,[208,204,220,264,206,204,206,206,204],0.1,[208,204,223,266,206,204,206,206,204],0.13,[208,204,225,268,206,204,206,206,204],0.14,[208,204,136,268,206,204,206,206,204],[208,204,230,262,206,204,206,206,204],[208,204,233,254,206,204,206,206,204],[208,204,235,262,206,204,206,206,204],[208,204,237,274,206,204,206,206,204],0.21,[208,204,240,276,206,204,206,206,204],0.33,[208,204,242,278,206,204,206,206,204],0.2,[208,204,245,262,206,204,206,206,204],[208,204,248,262,206,204,206,206,204],[208,204,251,264,206,204,206,206,204],[211,204,204,283,206,204,206,206,204],0.069,[211,204,208,283,206,204,206,206,204],[211,204,211,286,206,204,206,206,204],0.111,[211,204,214,288,206,204,206,206,204],0.053,[211,204,217,262,206,204,206,206,204],[211,204,220,291,206,204,206,206,204],0.108,[211,204,223,293,206,204,206,206,204],0.125,[211,204,225,295,206,204,206,206,204],0.131,[211,204,136,297,206,204,206,206,204],0.137,[211,204,230,299,206,204,206,206,204],0.086,[211,204,233,301,206,204,206,206,204],0.078,[211,204,235,303,206,204,206,206,204],0.076,[211,204,237,274,206,204,206,206,204],[211,204,240,306,206,204,206,206,204],0.336,[211,204,242,308,206,204,206,206,204],0.217,[211,204,245,262,206,204,206,206,204],[211,204,248,311,206,204,206,206,204],0.11,[211,204,251,313,206,204,206,206,204],0.089,[214,204,204,315,206,204,206,206,204],0.044,[214,204,208,317,206,204,206,206,204],0.023,[214,204,211,319,206,204,206,206,204],0.046,[214,204,214,321,206,204,206,206,204],0.033,[214,204,217,323,206,204,206,206,204],0.036,[214,204,220,325,206,204,206,206,204],0.037,[214,204,223,327,206,204,206,206,204],0.059,[214,204,225,329,206,204,206,206,204],0.071,[214,204,136,331,206,204,206,206,204],0.054,[214,204,230,333,206,204,206,206,204],0.04,[214,204,233,335,206,204,206,206,204],0.034,[214,204,235,323,206,204,206,206,204],[214,204,237,291,206,204,206,206,204],[214,204,240,266,206,204,206,206,204],[214,204,242,340,206,204,206,206,204],0.216,[214,204,245,218,206,204,206,206,204],[214,204,248,256,206,204,206,206,204],[214,204,251,221,206,204,206,206,204],[217,204,204,323,206,204,206,206,204],[217,204,208,346,206,204,206,206,204],0.022,[217,204,211,321,206,204,206,206,204],[217,204,214,349,206,204,206,206,204],0.03,[217,204,217,351,206,204,206,206,204],0.032,[217,204,220,349,206,204,206,206,204],[217,204,223,218,206,204,206,206,204],[217,204,225,355,206,204,206,206,204],0.049,[217,204,136,357,206,204,206,206,204],0.056,[217,204,230,335,206,204,206,206,204],[217,204,233,360,206,204,206,206,204],0.031,[217,204,235,321,206,204,206,206,204],[217,204,237,293,206,204,206,206,204],[217,204,240,238,206,204,206,206,204],[217,204,242,365,206,204,206,206,204],0.109,[217,204,245,355,206,204,206,206,204],[217,204,248,215,206,204,206,206,204],[217,204,251,319,206,204,206,206,204],[370],"failed",[372],"Table II",[],[],[376],"NTU VIRAL drone sequences, horizontal OS1-16 for RESPLE; APE RMSE with the official NTU VIRAL evaluation script; T-LO, C-MLO (two LiDARs) and F-LIO2 values are copied from refs. [13] (Traj-LO), [16] (CTE-MLO) and [18] (Nguyen et al. 2024, not the FAST-LIO2 paper), respectively, per the Table II footnote; x marks failure",{"slug":378,"group":379,"sourceId":5,"sourceLabel":6,"table":380,"selfRows":144,"metrics":381,"seqs":384,"entrants":399,"cells":432,"outcomes":566,"locators":569,"hardware":570,"wordings":571,"notes":572},"trajlo2024-table-iii","trajlo2024:Table III","Table III",[382],{"label":383,"unit":148,"statistic":150,"alignment":150},"ATE (m)",[385,389,391,393,395,397],{"dataset":386,"sequence":387,"environment":388},"Hilti 2021 SLAM challenge","RPG","Hilti 2021 handheld; per-sequence environment not stated in the paper (dataset described as indoor offices, labs and construction environments and outdoor construction sites and parking 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