[{"data":1,"prerenderedAt":524},["ShallowReactive",2],{"method-elasticity_ct2022":3},{"method":4,"reference":65,"equipment":88,"figures":143,"results":144},{"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":29,"sensors":31,"platform":39,"estimator":44,"association":45,"timeModel":46,"deskew":47,"loopClosure":48,"globalOptimization":49,"mapRepresentation":50,"prior":51,"outputGeometry":52,"compute":53,"codeUrl":54,"codeLicense":55,"relatedVersions":56},"elasticity_ct2022","Park et al., 2022","Map-centric dense 3D LiDAR SLAM (ElasticLiDAR++)","Elasticity Meets Continuous-Time: Map-Centric Dense 3D LiDAR SLAM",2022,"recent","C04","full_slam_with_global_correction","本文是 Elastic LiDAR Fusion 的期刊延伸，正式版將系統命名為 ElasticLiDAR++，把以地圖為中心的變形式 SLAM 推廣到旋轉單線與多線 3D LiDAR，並融合 IMU 與相機。局部以 100 Hz 線性內插的連續時間軌跡處理運動畸變，再以 B 樣條控制點在 SE(3) 上估計修正量，同時線上估計 LiDAR 與相機的時間延遲；全域則以變形圖讓整張面元地圖產生彈性變形來閉合迴圈，不保存整條軌跡。面元以常態逆 Wishart 模型遞迴融合，搭配保持表面解析度的匹配規則，使多次掃描融合成不重複的稠密地圖。迴圈的錯位估計結合面元點對面約束與 3D 特徵點對點約束，並在多個位置序列式融合，直到不確定度低於門檻。作者以 CT-SLAM 的全域最佳化軌跡為參考，軌跡差異為 0.173 至 0.552 m，平面補丁雜訊最多約降為三分之一；實作需約 2.1 秒處理 1 秒資料，尚非即時。","Journal extension of Elastic LiDAR Fusion, named ElasticLiDAR++ in the version of record: local continuous-time LiDAR-inertial-visual trajectory optimisation (linear interpolation at 100 Hz, B-spline SE(3) corrections, online time-lag estimation), normal-inverse-Wishart surfel fusion with resolution-preserving matching, and map deformation with sequential 3D-feature-plus-surfel metric loop closure; not real time (2.1 s per 1 s of data).","full_text_reviewed","peer_reviewed_published","background","未在營建工地測試。資料集包括小房間、多樓層建物、辦公室、室內外混合、戶外結構化與非結構化場域，以及四足與輪式機器人在工業區蒐集的資料（Sec. VII-A）。軌跡精度以 CT-SLAM 的全域最佳化軌跡為參考，不是獨立量測的地面真值；表面品質以地板或牆面補丁到平均平面的投影距離評估，平均約 3.2 至 4.5 mm；這是相對於各補丁平均平面的雜訊，不是對獨立參考量測的絕對精度。地圖在 60 m 尺度下扭曲可達 10 cm，若用於竣工量測或尺寸檢核，仍需以獨立控制點驗證（推論）。",[20,21,22],"simulation","cross_site","completed_building",[24,25,26,27,28],"Simulated local trajectory optimisation: 10.3 mm and 1.2e-3 rad final accuracy with 66 states, versus 39.0 mm and 5.0e-3 rad for the linear SO(3)+R3 composition of [3] (Table II)","full-stack simulation: 12.3 mm mean relative trajectory error versus 35.3 mm for [3] (Sec. IV-E)","planar-patch position error 3.2 to 4.5 mm mean versus 8.0 to 9.4 mm for unfused CT-SLAM points (up to three times less noisy, VoR Table V)","sequential metric localisation within 0.06 m and 0.004 rad in the hard case where Open3D and SHOT baselines reach metres of error (Table VI)","structural difference to the baseline within plus or minus 0.02 m in a redundant scan (Fig. 14)",[30],"Map distortion reached 10 cm on a 60 m scale map and is expected to grow with map size; partial-observation problem (Sec. IX); trajectory difference to CT-SLAM grows with map size (0.173 to 0.552 m RMSE), attributed to ignoring gravity when integrating local maps and in the deformation graph (Sec. VII-B, Table IV); overall 0.1 m structural offset; partial revisits without enough overlap are problematic for long-range LiDAR, so fusion range must be limited (Sec. VIII-B); repeated non-Gaussian noise such as mixed pixels is not fused away and objects smaller than the surface resolution are lost (Sec. VIII-B); metric localisation error remains larger than surface reconstruction accuracy (Sec. VIII-C); not real time, 2.1 s per 1 s of data (Sec. VIII-B); trajectory reference is another estimate, not ground truth (Sec. VII-B)",[32,33,34,35,36,37,38],"rotating 2D LiDAR (Hokuyo UTM-30LX with encoder, rotor at 1 rotation\u002Fs, hand-held)","3D LiDAR (Velodyne VLP-16, hand-held and robot-mounted)","IMU (Microstrain 3DM-GX3 in hand-held payloads","model not stated for the robot payload)","RGB camera on the single-beam device","independent GoPro without common clock on the multi-beam hand-held device","camera on robot payload (model not stated)",[40,41,42,43,20],"handheld (single-beam spinning LiDAR)","handheld (multi-beam VLP-16)","legged robot","wheeled ground robot","local composition-type continuous-time trajectory optimisation: discrete poses at 100 Hz corrected by cubic B-spline control points with SE(3) update, minimising surfel-to-surfel, surfel-to-map-prior and IMU residuals over control points, IMU biases and two time lags (LiDAR and camera) (Eq. 3-9); Gauss-Newton deformation-graph optimisation for loop closure (Eq. 21-24); sequential SE(3) pose fusion for metric localisation (Appendix B)","surfel-to-surfel and surfel-to-map-prior constraints; surface-resolution-preservative surfel matching for non-pinhole sensors (Sec. I, III)","continuous-time within a local window: linear interpolation on se(3) between discrete poses generated at 100 Hz for residuals and undistortion, cubic B-spline correction trajectory for the update; LiDAR and camera time lags estimated online","continuous-time trajectory representation (abstract)","detection: the overview describes 2D visual features compared with stored key frames (Sec. III); Sec. VII-D calls the trigger a 'visual place voting method' but cites [52], which is Bosse and Zlot's 3D LiDAR keypoint voting paper; Sec. VII-A states that experiments used a combined 3D and 2D detector [12], [55]; active-inactive sparse-surfel ICP detects moderate misalignment (Sec. V-D, VI-A4). Misalignment is estimated from LiDAR only by tightly combining surfel point-to-plane and 3D sparse-feature (for example FPFH) point-to-point constraints, sequentially fused at several places until the covariance meets a threshold; residual monitoring rejects false positives (Sec. VI-A5, Appendix B)","non-rigid map deformation with surfel uncertainty propagation (Sec. III)","multi-resolution sparse ellipsoid surfels plus fixed-size hexagonal dense surfels with Wishart-based fusion (Sec. III)","none","dense fused surfel map (Sec. III, Fig. 1)","2.1 s average to process 1 s of VLP-16, IMU and camera data on an i7-6700K CPU with 24 GB RAM and a GTX970 GPU; surfel fusion implemented in single-thread MATLAB; authors expect real time after a parallel C++ port (not demonstrated)",null,"not_verified",[57,61],{"relation":58,"title":59,"doi_or_url":60},"conference_version","Elastic LiDAR Fusion: Dense Map-Centric Continuous-Time SLAM","10.1109\u002FICRA.2018.8462915",{"relation":62,"title":63,"doi_or_url":64},"preprint","arXiv 2008.02274 v1 (submitted version)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2008.02274",{"id":5,"kind":66,"shortName":7,"title":8,"authors":67,"year":9,"venue":74,"venueType":75,"publisher":76,"volumeIssuePages":77,"doi":78,"arxivId":79,"url":64,"firstPublicDate":80,"publicationStatus":16,"metadataStatus":81,"fulltextStatus":15,"era":10,"classicReason":82,"codeUrl":54,"cluster":11,"topics":83,"mdpi":84,"verification":85,"label":6,"fulltextRoute":86,"versionRead":87,"addedByCensus":84},"method",[68,69,70,71,72,73],"Chanoh Park","Peyman Moghadam","Jason Williams","Soohwan Kim","Sridha Sridharan","Clinton Fookes","IEEE Transactions on Robotics","journal","IEEE","38(2): 978-997","10.1109\u002Ftro.2021.3096650","2008.02274","2020-08-05","metadata_verified","not_applicable",[11],false,"corrected","NTU institutional (Chrome)","version of record: IEEE Xplore full-text HTML of T-RO 38(2): 978-997 (all sections I-IX, Appendices A-B) plus the VoR PDF (20 pages, fetched through NTU institutional access in Chrome) in which the image tables II, IV, V and VI were rendered and read by the second checker; arXiv 2008.02274v1 (2020-08-05, submitted version) also read by the first extractor. VoR Tables I (variables) and III (surfel normal convergence simulation) not read.",[89,95,100,104,108,112,115,117,119,124,128,133,137],{"category":90,"model":91,"canonical":91,"role":92,"dataset":54,"specs":93,"locator":94},"lidar","Hokuyo UTM-30LX","method input","spinning single-beam laser, rotor 1 rotation\u002Fs; datasets moved at 0.9 m\u002Fs and 0.7 rad\u002Fs","VoR Sec. VII-A; Fig. 9a",{"category":96,"model":97,"canonical":97,"role":92,"dataset":54,"specs":98,"locator":99},"other","encoder (spinning single-beam device)","not_reported","VoR Sec. VII-A",{"category":101,"model":102,"canonical":102,"role":92,"dataset":54,"specs":103,"locator":99},"imu","Microstrain 3DM-GX3","used in both hand-held payloads",{"category":105,"model":106,"canonical":106,"role":92,"dataset":54,"specs":107,"locator":99},"camera","RGB camera (single-beam hand-held device, model not stated)","used for colourisation and visual loop detection",{"category":90,"model":109,"canonical":109,"role":92,"dataset":54,"specs":110,"locator":111},"Velodyne VLP-16","multi-beam; hand-held, legged-robot and wheeled-robot payloads","VoR Sec. VII-A; Fig. 9b-d",{"category":105,"model":113,"canonical":113,"role":92,"dataset":54,"specs":114,"locator":99},"Gopro","independent camera without a common clock with the LiDAR",{"category":101,"model":116,"canonical":116,"role":92,"dataset":54,"specs":98,"locator":99},"IMU of the robot payload (model not stated)",{"category":105,"model":118,"canonical":118,"role":92,"dataset":54,"specs":98,"locator":99},"camera of the robot payload (model not stated)",{"category":120,"model":121,"canonical":121,"role":92,"dataset":54,"specs":122,"locator":123},"platform","four-legged robot (model not stated)","dataset 2.5 min, 72 x 42 m, industrial area","VoR Sec. VII-A; Fig. 11a",{"category":120,"model":125,"canonical":125,"role":92,"dataset":54,"specs":126,"locator":127},"wheeled ground robot (model not stated)","dataset 7 min, 38 x 49 m, industrial area","VoR Sec. VII-A; Fig. 11b",{"category":129,"model":130,"canonical":130,"role":92,"dataset":54,"specs":131,"locator":132},"mobile_scanner_device","hand-held single-beam 3D spinning LiDAR device","Hokuyo UTM-30LX, encoder, Microstrain 3DM-GX3, RGB camera","VoR Fig. 9a; Sec. VII-A",{"category":129,"model":134,"canonical":134,"role":92,"dataset":54,"specs":135,"locator":136},"hand-held multi-beam LiDAR device","Velodyne VLP-16, Microstrain 3DM-GX3, GoPro","VoR Fig. 9b; Sec. VII-A",{"category":138,"model":139,"canonical":139,"role":140,"dataset":54,"specs":141,"locator":142},"compute","i7-6700K CPU, 24 GB RAM, GTX970 GPU","compute for runtime","2.1 s per 1 s of VLP-16, IMU and camera data","VoR Sec. VIII-B",[],{"totalRows":145,"groupCount":146,"groups":147,"others":511},47,6,[148,287,410,459],{"slug":149,"group":150,"sourceId":5,"sourceLabel":6,"table":151,"selfRows":152,"metrics":153,"seqs":164,"entrants":179,"cells":186,"outcomes":280,"locators":281,"hardware":283,"wordings":284,"notes":285},"elasticity-ct2022-table-v","elasticity_ct2022:Table V","Table V",24,[154,158,160,163],{"label":155,"unit":156,"statistic":157,"alignment":82},"Position Err. (projective distance)","mm","mean",{"label":155,"unit":156,"statistic":159,"alignment":82},"std",{"label":161,"unit":162,"statistic":157,"alignment":82},"Normal Err.","rad",{"label":161,"unit":162,"statistic":159,"alignment":82},[165,169,171,173,175,177],{"dataset":166,"sequence":167,"environment":168},"authors' real datasets","patch a","planar floor or wall patch",{"dataset":166,"sequence":170,"environment":168},"patch b",{"dataset":166,"sequence":172,"environment":168},"patch c",{"dataset":166,"sequence":174,"environment":168},"patch d",{"dataset":166,"sequence":176,"environment":168},"patch f",{"dataset":166,"sequence":178,"environment":168},"patch g",[180,184],{"name":181,"methodId":182,"linkable":183,"proposed":84,"self":84},"CT-SLAM [3] (raw points)","zebedee2012",true,{"name":185,"methodId":5,"linkable":183,"proposed":183,"self":183},"Proposed (fused surfels)",[187,191,194,197,200,202,204,206,208,210,212,214,216,218,220,222,224,226,228,230,231,232,234,235,236,238,240,241,243,245,247,249,251,254,256,258,260,261,262,264,265,268,270,272,274,276,278,279],[188,188,188,189,190,188,190,190,188],0,8.2,-1,[188,192,188,193,190,188,190,190,188],1,14.8,[188,195,188,196,190,188,190,190,188],2,0.101,[188,198,188,199,190,188,190,190,188],3,0.082,[192,188,188,201,190,188,190,190,188],3.4,[192,192,188,203,190,188,190,190,188],4.7,[192,195,188,205,190,188,190,190,188],0.069,[192,198,188,207,190,188,190,190,188],0.075,[188,188,192,209,190,188,190,190,188],9.3,[188,192,192,211,190,188,190,190,188],16.8,[188,195,192,213,190,188,190,190,188],0.12,[188,198,192,215,190,188,190,190,188],0.116,[192,188,192,217,190,188,190,190,188],3.2,[192,192,192,219,190,188,190,190,188],4.4,[192,195,192,221,190,188,190,190,188],0.085,[192,198,192,223,190,188,190,190,188],0.076,[188,188,195,225,190,188,190,190,188],8.9,[188,192,195,227,190,188,190,190,188],17.3,[188,195,195,229,190,188,190,190,188],0.092,[188,198,195,213,190,188,190,190,188],[192,188,195,201,190,188,190,190,188],[192,192,195,233,190,188,190,190,188],6.2,[192,195,195,223,190,188,190,190,188],[192,198,195,199,190,188,190,190,188],[188,188,198,237,190,188,190,190,188],9.4,[188,192,198,239,190,188,190,190,188],17,[188,195,198,221,190,188,190,190,188],[188,198,198,242,190,188,190,190,188],0.099,[192,188,198,244,190,188,190,190,188],3.9,[192,192,198,246,190,188,190,190,188],5.3,[192,195,198,248,190,188,190,190,188],0.078,[192,198,198,250,190,188,190,190,188],0.08,[188,188,252,253,190,188,190,190,188],4,8,[188,192,252,255,190,188,190,190,188],13.7,[188,195,252,257,190,188,190,190,188],0.096,[188,198,252,259,190,188,190,190,188],0.087,[192,188,252,201,190,188,190,190,188],[192,192,252,203,190,188,190,190,188],[192,195,252,263,190,188,190,190,188],0.083,[192,198,252,250,190,188,190,190,188],[188,188,266,267,190,188,190,190,188],5,9.1,[188,192,266,269,190,188,190,190,188],16,[188,195,266,271,190,188,190,190,188],0.106,[188,198,266,273,190,188,190,190,188],0.115,[192,188,266,275,190,188,190,190,188],4.5,[192,192,266,277,190,188,190,190,188],6.5,[192,195,266,248,190,188,190,190,188],[192,198,266,199,190,188,190,190,188],[],[282],"Table V (VoR, p. 991; identical to arXiv v1 Table IV)",[],[],[286],"known planar patches; position error = projective distance to patch mean plane (mm), normal error in rad; CT-SLAM [3] cloud is undistorted by its globally optimised trajectory but unfused; no ground truth. Values read from the VoR Table V by the second checker",{"slug":288,"group":289,"sourceId":5,"sourceLabel":6,"table":290,"selfRows":291,"metrics":292,"seqs":301,"entrants":310,"cells":321,"outcomes":403,"locators":404,"hardware":406,"wordings":407,"notes":408},"elasticity-ct2022-table-vi","elasticity_ct2022:Table VI","Table VI",12,[293,297,298,300],{"label":294,"unit":295,"statistic":296,"alignment":82},"e_t translation error","m","RMSE",{"label":294,"unit":295,"statistic":159,"alignment":82},{"label":299,"unit":162,"statistic":296,"alignment":82},"e_r rotation error (rotation vector norm)",{"label":299,"unit":162,"statistic":159,"alignment":82},[302,306,308],{"dataset":303,"sequence":304,"environment":305},"authors' mixed indoor and outdoor point clouds","Easy initial guess","indoor and outdoor mixed",{"dataset":303,"sequence":307,"environment":305},"Medium initial guess",{"dataset":303,"sequence":309,"environment":305},"Hard initial guess",[311,314,317,319],{"name":312,"methodId":313,"linkable":183,"proposed":84,"self":84},"(a) Sparse surfel ICP (configuration of previous work [2])","elasticlidarfusion2018",{"name":315,"methodId":316,"linkable":183,"proposed":84,"self":84},"(b) Open3D global registration (FPFH + RANSAC) [60]","zhou2018open3d",{"name":318,"methodId":54,"linkable":84,"proposed":84,"self":84},"(c) SHOT initialisation + point-to-plane ICP [61]",{"name":320,"methodId":5,"linkable":183,"proposed":183,"self":183},"(d) Proposed sequential metric localisation",[322,324,325,327,328,330,332,334,336,338,340,342,343,344,345,347,349,351,353,355,357,359,361,362,364,366,368,370,372,373,375,377,378,380,382,384,385,387,389,390,392,394,396,397,399,400,401,402],[188,188,188,323,190,188,190,190,188],0.04,[188,192,188,323,190,188,190,190,188],[188,195,188,326,190,188,190,190,188],0.01,[188,198,188,326,190,188,190,190,188],[192,188,188,329,190,188,190,190,188],0.3,[192,192,188,331,190,188,190,190,188],0.65,[192,195,188,333,190,188,190,190,188],0.03,[192,198,188,335,190,188,190,190,188],0.06,[195,188,188,337,190,188,190,190,188],1.49,[195,192,188,339,190,188,190,190,188],1.52,[195,195,188,341,190,188,190,190,188],0.07,[195,198,188,213,190,188,190,190,188],[198,188,188,333,190,188,190,190,188],[198,192,188,326,190,188,190,190,188],[198,195,188,346,190,188,190,190,188],0.001,[198,198,188,348,190,188,190,190,188],0.0005,[188,188,192,350,190,188,190,190,188],0.4,[188,192,192,352,190,188,190,190,188],0.51,[188,195,192,354,190,188,190,190,188],0.19,[188,198,192,356,190,188,190,190,188],0.31,[192,188,192,358,190,188,190,190,188],1.64,[192,192,192,360,190,188,190,190,188],2.39,[192,195,192,329,190,188,190,190,188],[192,198,192,363,190,188,190,190,188],0.38,[195,188,192,365,190,188,190,190,188],1.53,[195,192,192,367,190,188,190,190,188],1.55,[195,195,192,369,190,188,190,190,188],0.11,[195,198,192,371,190,188,190,190,188],0.29,[198,188,192,323,190,188,190,190,188],[198,192,192,374,190,188,190,190,188],0.02,[198,195,192,376,190,188,190,190,188],0.004,[198,198,192,346,190,188,190,190,188],[188,188,195,379,190,188,190,190,188],2.52,[188,192,195,381,190,188,190,190,188],0.87,[188,195,195,383,190,188,190,190,188],2.42,[188,198,195,367,190,188,190,190,188],[192,188,195,386,190,188,190,190,188],13.8,[192,192,195,388,190,188,190,190,188],22.4,[192,195,195,363,190,188,190,190,188],[192,198,195,391,190,188,190,190,188],0.63,[195,188,195,393,190,188,190,190,188],7.59,[195,192,195,395,190,188,190,190,188],18.56,[195,195,195,329,190,188,190,190,188],[195,198,195,398,190,188,190,190,188],0.71,[198,188,195,335,190,188,190,190,188],[198,192,195,323,190,188,190,190,188],[198,195,195,376,190,188,190,190,188],[198,198,195,346,190,188,190,190,188],[],[405],"Table VI (VoR, p. 993; identical to arXiv v1 Table V)",[],[],[409],"loop-closure misalignment estimation on mixed indoor and outdoor data; ground truth from the globally optimised trajectory; 10 locations x 50 random initial guesses (500 triggers) per level; Easy sigma_theta_z 10 deg, sigma_theta_xy 1 deg, sigma_t 0.5 m; Medium 50, 5, 5; Hard 100, 20, 50; text calls the values RMSE, caption calls them error norms with std in parentheses. Values read from the VoR Table VI by the second checker",{"slug":411,"group":412,"sourceId":5,"sourceLabel":6,"table":413,"selfRows":146,"metrics":414,"seqs":417,"entrants":436,"cells":439,"outcomes":452,"locators":453,"hardware":455,"wordings":456,"notes":457},"elasticity-ct2022-table-iv","elasticity_ct2022:Table IV","Table IV",[415],{"label":416,"unit":295,"statistic":296,"alignment":98},"Traj Error (m), absolute trajectory RMSE vs CT-SLAM [3]",[418,422,425,427,430,433],{"dataset":419,"sequence":420,"environment":421},"authors' hand-held datasets","(a) Small room, 130 m, 6.1 min, 10x6 m, single-beam","indoor small room",{"dataset":419,"sequence":423,"environment":424},"(b) Multiple Floors, 300 m, 11.4 min, 55x20 m, single-beam","indoor multi-floor building",{"dataset":419,"sequence":426,"environment":305},"(c) In\u002Foutdoor, 360 m, 9.1 min, 60x25 m, single-beam",{"dataset":419,"sequence":428,"environment":429},"(d) Outdoor, 210 m, 3.5 min, 60x69 m, multi-beam","outdoor structured",{"dataset":419,"sequence":431,"environment":432},"(e) Unstructured, 110 m, 4.1 min, 47x17 m, multi-beam","outdoor unstructured",{"dataset":419,"sequence":434,"environment":435},"(f) Office, 330 m, 14.6 min, 20x20 m, single-beam","indoor office",[437],{"name":438,"methodId":5,"linkable":183,"proposed":183,"self":183},"Proposed (ElasticLiDAR++, deformed trajectory)",[440,442,444,446,448,450],[188,188,188,441,190,188,190,190,188],0.173,[188,188,192,443,190,188,190,190,188],0.245,[188,188,195,445,190,188,190,190,188],0.233,[188,188,198,447,190,188,190,190,188],0.552,[188,188,252,449,190,188,190,190,188],0.301,[188,188,266,451,190,188,190,190,188],0.189,[],[454],"Table IV (VoR, p. 990)",[],[],[458],"absolute trajectory RMSE between the deformed trajectory of the proposed method and the globally optimised CT-SLAM [3] trajectory (reference is another estimate, not ground truth); length, duration, size and beam type in sequence field",{"slug":460,"group":461,"sourceId":5,"sourceLabel":6,"table":462,"selfRows":195,"metrics":463,"seqs":469,"entrants":473,"cells":484,"outcomes":504,"locators":505,"hardware":507,"wordings":508,"notes":509},"elasticity-ct2022-table-ii","elasticity_ct2022:Table II","Table II",[464,466],{"label":465,"unit":156,"statistic":98,"alignment":98},"Final t accuracy (absolute trajectory accuracy after optimisation)",{"label":467,"unit":468,"statistic":98,"alignment":98},"Final r accuracy","1e-3 rad",[470],{"dataset":471,"sequence":472,"environment":20},"simulation (local trajectory optimisation)","5 s window",[474,476,478,480,482],{"name":475,"methodId":182,"linkable":183,"proposed":84,"self":84},"Composition model, Linear interpolation, Linear Composition, SO(3)+R3 update, 11 compositions, 66 states [3]",{"name":477,"methodId":5,"linkable":183,"proposed":183,"self":183},"Composition model, Linear se(3) interpolation, Spline Composition, SE(3) update, 11 controls, 66 states (Ours)",{"name":479,"methodId":54,"linkable":84,"proposed":84,"self":84},"Approximation model, Spline Direct, SE(3), 11 controls, 66 states [9]",{"name":481,"methodId":54,"linkable":84,"proposed":84,"self":84},"Approximation model, Spline Direct, SE(3), 51 controls, 306 states [9]",{"name":483,"methodId":54,"linkable":84,"proposed":84,"self":84},"Approximation model, Spline Direct, SE(3), 101 controls, 606 states [9]",[485,487,488,490,492,494,496,498,500,502],[188,188,188,486,190,188,190,190,188],39,[188,192,188,266,190,188,190,190,188],[192,188,188,489,190,188,190,190,188],10.3,[192,192,188,491,190,188,190,190,188],1.2,[195,188,188,493,190,188,190,190,188],103,[195,192,188,495,190,188,190,190,188],93.7,[198,188,188,497,190,188,190,190,188],21.8,[198,192,188,499,190,188,190,190,188],10.8,[252,188,188,501,190,188,190,190,188],23.1,[252,192,188,503,190,188,190,190,188],5.1,[],[506],"Table II (VoR; identical in arXiv v1)",[],[],[510],"simulation: 5 s local window, simulated angular velocity and linear acceleration at 100 Hz with bias and Gaussian noise, 1000 random timestamped sparse surfel features; accuracy = absolute trajectory accuracy after optimisation against simulated ground truth; composition models keep 500 discrete poses",[512,518],{"group":513,"slug":514,"sourceLabel":6,"table":515,"selfRows":195,"datasets":516},"elasticity_ct2022:Text Sec. IV-E","elasticity-ct2022-text-sec-iv-e","Text Sec. IV-E",[517],"simulation (full sensor stack)",{"group":519,"slug":520,"sourceLabel":6,"table":521,"selfRows":192,"datasets":522},"elasticity_ct2022:Text Sec. VIII-B","elasticity-ct2022-text-sec-viii-b","Text Sec. VIII-B",[523],"authors' VLP-16 datasets",1790510658881]