[{"data":1,"prerenderedAt":570},["ShallowReactive",2],{"method-legentil2018lidarimucalib":3},{"method":4,"reference":54,"equipment":73,"figures":92,"results":93},{"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":27,"sensors":33,"platform":36,"estimator":38,"association":39,"timeModel":40,"deskew":41,"loopClosure":42,"globalOptimization":43,"mapRepresentation":44,"prior":42,"outputGeometry":45,"compute":46,"codeUrl":47,"codeLicense":48,"relatedVersions":49},"legentil2018lidarimucalib","Le Gentil et al., 2018","Lidar-IMU calibration with upsampled preintegration","3D Lidar-IMU Calibration Based on Upsampled Preintegrated Measurements for Motion Distortion Correction",2018,"recent","C13","sensing_calibration_sync_preprocessing","作者指出 LiDAR 點是逐點取樣而非快照，平台快速移動時會產生運動畸變。本法以高斯過程迴歸對 IMU 讀數上取樣，對每個 LiDAR 點計算預積分量以精確去畸變，並以點到平面距離與 IMU 預積分因子聯合估計外參、IMU 狀態與時間偏移。模擬顯示未使用上取樣預積分時，在快速運動下精度明顯下降。","Gaussian-process upsampled IMU preintegration per LiDAR point enables jointly calibrating LiDAR-IMU extrinsics and time shift while correcting motion distortion.","full_text_reviewed","peer_reviewed_published","background","未在工地測試；作者建議車輛或無人機可改用倉庫或地下停車場的地板、天花板與牆面作為校正平面（Sec. V），表示方法可利用建築物既有平面，但文中未實測。",[20,21],"simulation","controlled_experiment",[23,24,25,26],"Upsampled preintegration improves calibration accuracy, especially under fast motion (Sec. IV-A4, Table I)","Target-less: arbitrary planes detected in the first scan (here a room corner) serve as the calibration target (Sec. I, II-E)","On a 60 s real sequence (442 of 577 scans used) the result differs from a chained Kalibr IMU-camera plus camera-LiDAR calibration by 3.6 cm and 1.45 degrees despite unsynchronized sensors and partial target views (Sec. IV-B)","GP regression and high-frequency preintegration reduce the influence of inertial noise on the estimated calibration (Sec. IV-A1)",[28,29,30,31,32],"Small perturbations of IMU states produced large calibration errors in simulation, so accurate IMU node estimation is needed (Sec. IV-A3)","Real data from a 60 s sequence in front of a room corner only (Sec. IV-B)","LiDAR ranging noise is the dominant error source; accuracy drops strongly once LiDAR noise is simulated even with a noise-free IMU (Sec. IV-A1, V)","The first scan must be static, and the handheld setup reduced the LiDAR FOV to plus or minus 45 degrees (Sec. II-E, III-C)","No ground truth for the real data, so the comparison with the chained calibration is not conclusive (Sec. IV-B)",[34,35],"3D LiDAR (VLP-16)","IMU (Xsens MTi-3)",[37,20],"handheld (moved in front of a room corner)","on-manifold factor-graph optimization of IMU poses, velocities, biases, calibration and time shift","RANSAC (MLESAC) plane detection in every scan; planes tracked between consecutive scans by nearest-neighbour matching of normals after subtracting their centroid; each point assigned to one map plane from the first scan (assumed static) and used in a point-to-plane residual","continuous-time IMU signal via Gaussian-process regression, per-point preintegration","each LiDAR point reprojected with upsampled preintegrated IMU measurements","none","batch","set of planes","extrinsic calibration and time shift","offline batch in Matlab (Manopt trust-region solver on manifolds; gpml toolbox for GP regression); points capped per plane per scan to shorten processing; runtime not reported; a C++ implementation is planned",null,"not_verified",[50],{"relation":51,"title":52,"doi_or_url":53},"preprint","author accepted manuscript (UTS OPUS)","https:\u002F\u002Fopus.lib.uts.edu.au\u002Fbitstream\u002F10453\u002F122728\u002F4\u002FOCC-113281_AM.pdf",{"id":5,"kind":55,"shortName":7,"title":8,"authors":56,"year":9,"venue":60,"venueType":61,"publisher":62,"volumeIssuePages":63,"doi":64,"arxivId":47,"url":53,"firstPublicDate":65,"publicationStatus":16,"metadataStatus":66,"fulltextStatus":15,"era":10,"classicReason":67,"codeUrl":47,"cluster":11,"topics":68,"mdpi":69,"verification":70,"label":6,"fulltextRoute":71,"versionRead":72,"addedByCensus":69},"method",[57,58,59],"Cedric Le Gentil","Teresa Vidal-Calleja","Shoudong Huang","2018 IEEE International Conference on Robotics and Automation (ICRA)","conference","IEEE","pp. 2149-2155","10.1109\u002Ficra.2018.8460179","2018-05","metadata_verified","not_applicable",[11],false,"confirmed","author copy","accepted manuscript OCC-113281_AM.pdf in UTS OPUS repository (8 pages, IEEE copyright notice on page 1); ICRA 2018 VoR not compared",[74,80,86],{"category":75,"model":76,"canonical":76,"role":77,"dataset":47,"specs":78,"locator":79},"lidar","Velodyne VLP-16","method input","simulated as 16 channels (plus or minus 15 deg), 10 Hz, 240k points per second; FOV reduced to plus or minus 45 deg for the handheld rig; read with the snark driver","Sec. III-C, IV-A, IV-B, Fig. 8",{"category":81,"model":82,"canonical":83,"role":77,"dataset":47,"specs":84,"locator":85},"imu","Xsens MTi3","Xsens MTi-3","low-cost IMU; simulated at 100 Hz; read with the ROS Xsens driver","Sec. IV-A, IV-B, V, Fig. 8",{"category":87,"model":88,"canonical":88,"role":89,"dataset":47,"specs":90,"locator":91},"rgbd","RGB-D camera (RealSense colour camera; exact model not stated)","reference or ground truth","rolling-shutter colour camera; not used by the proposed method, only for the chained Kalibr and camera-LiDAR reference calibration","Sec. IV-B, Fig. 8",[],{"totalRows":94,"groupCount":95,"groups":96,"others":558},66,6,[97,303,437,513],{"slug":98,"group":99,"sourceId":100,"sourceLabel":101,"table":102,"selfRows":103,"metrics":104,"seqs":111,"entrants":145,"cells":151,"outcomes":296,"locators":297,"hardware":298,"wordings":300,"notes":301},"legentil2020gpm-table-iii","legentil2020gpm:Table III","legentil2020gpm","Le Gentil et al., 2020","Table III",32,[105,109],{"label":106,"unit":107,"statistic":108,"alignment":67},"average hyper-parameter training time per preintegrated measurement","ms","mean",{"label":110,"unit":107,"statistic":108,"alignment":67},"average inference time per preintegrated measurement",[112,115,117,119,121,123,125,127,129,131,133,135,137,139,141,143],{"dataset":113,"sequence":114,"environment":20},"simulated IMU trajectories","1D rotations, interval 0.05 s",{"dataset":113,"sequence":116,"environment":20},"1D rotations, interval 0.1 s",{"dataset":113,"sequence":118,"environment":20},"1D rotations, interval 0.5 s",{"dataset":113,"sequence":120,"environment":20},"1D rotations, interval 1 s",{"dataset":113,"sequence":122,"environment":20},"1D rotations, interval 2 s",{"dataset":113,"sequence":124,"environment":20},"1D rotations, interval 3 s",{"dataset":113,"sequence":126,"environment":20},"1D rotations, interval 4 s",{"dataset":113,"sequence":128,"environment":20},"1D rotations, interval 5 s",{"dataset":113,"sequence":130,"environment":20},"3D rotations, interval 0.05 s",{"dataset":113,"sequence":132,"environment":20},"3D rotations, interval 0.1 s",{"dataset":113,"sequence":134,"environment":20},"3D rotations, interval 0.5 s",{"dataset":113,"sequence":136,"environment":20},"3D rotations, interval 1 s",{"dataset":113,"sequence":138,"environment":20},"3D rotations, interval 2 s",{"dataset":113,"sequence":140,"environment":20},"3D rotations, interval 3 s",{"dataset":113,"sequence":142,"environment":20},"3D rotations, interval 4 s",{"dataset":113,"sequence":144,"environment":20},"3D rotations, interval 5 s",[146,149],{"name":147,"methodId":5,"linkable":148,"proposed":69,"self":148},"UPM (upsampled preintegration [17])",true,{"name":150,"methodId":100,"linkable":148,"proposed":148,"self":69},"GPM",[152,156,159,161,163,165,167,169,171,174,176,178,180,183,185,187,189,192,194,196,198,201,203,205,207,209,211,213,215,218,220,222,224,227,229,231,233,236,238,240,242,245,247,249,251,254,256,258,260,263,265,267,269,272,274,276,278,281,283,285,287,290,292,294],[153,153,153,154,155,153,153,155,153],0,294,-1,[153,157,153,158,155,153,153,155,153],1,18.7,[157,153,153,160,155,153,153,155,153],356,[157,157,153,162,155,153,153,155,153],1.3,[153,153,157,164,155,153,153,155,153],335,[153,157,157,166,155,153,153,155,153],25.6,[157,153,157,168,155,153,153,155,153],399,[157,157,157,170,155,153,153,155,153],1.8,[153,153,172,173,155,153,153,155,153],2,392,[153,157,172,175,155,153,153,155,153],67.3,[157,153,172,177,155,153,153,155,153],396,[157,157,172,179,155,153,153,155,153],4.2,[153,153,181,182,155,153,153,155,153],3,498,[153,157,181,184,155,153,153,155,153],113,[157,153,181,186,155,153,153,155,153],502,[157,157,181,188,155,153,153,155,153],8.1,[153,153,190,191,155,153,153,155,153],4,1088,[153,157,190,193,155,153,153,155,153],279,[157,153,190,195,155,153,153,155,153],1085,[157,157,190,197,155,153,153,155,153],28.2,[153,153,199,200,155,153,153,155,153],5,2349,[153,157,199,202,155,153,153,155,153],534,[157,153,199,204,155,153,153,155,153],2332,[157,157,199,206,155,153,153,155,153],68.9,[153,153,95,208,155,153,153,155,153],4159,[153,157,95,210,155,153,153,155,153],824,[157,153,95,212,155,153,153,155,153],4144,[157,157,95,214,155,153,153,155,153],127,[153,153,216,217,155,153,153,155,153],7,6939,[153,157,216,219,155,153,153,155,153],1244,[157,153,216,221,155,153,153,155,153],6832,[157,157,216,223,155,153,153,155,153],212,[153,153,225,226,155,153,153,155,153],8,269,[153,157,225,228,155,153,153,155,153],16.9,[157,153,225,230,155,153,153,155,153],277,[157,157,225,232,155,153,153,155,153],10.2,[153,153,234,235,155,153,153,155,153],9,291,[153,157,234,237,155,153,153,155,153],22,[157,153,234,239,155,153,153,155,153],298,[157,157,234,241,155,153,153,155,153],11.1,[153,153,243,244,155,153,153,155,153],10,362,[153,157,243,246,155,153,153,155,153],60.2,[157,153,243,248,155,153,153,155,153],367,[157,157,243,250,155,153,153,155,153],24.8,[153,153,252,253,155,153,153,155,153],11,530,[153,157,252,255,155,153,153,155,153],121,[157,153,252,257,155,153,153,155,153],529,[157,157,252,259,155,153,153,155,153],51.9,[153,153,261,262,155,153,153,155,153],12,1148,[153,157,261,264,155,153,153,155,153],297,[157,153,261,266,155,153,153,155,153],1110,[157,157,261,268,155,153,153,155,153],138,[153,153,270,271,155,153,153,155,153],13,2347,[153,157,270,273,155,153,153,155,153],538,[157,153,270,275,155,153,153,155,153],2281,[157,157,270,277,155,153,153,155,153],270,[153,153,279,280,155,153,153,155,153],14,4352,[153,157,279,282,155,153,153,155,153],875,[157,153,279,284,155,153,153,155,153],4243,[157,157,279,286,155,153,153,155,153],482,[153,153,288,289,155,153,153,155,153],15,7295,[153,157,288,291,155,153,153,155,153],1392,[157,153,288,293,155,153,153,155,153],7102,[157,157,288,295,155,153,153,155,153],817,[],[102],[299],"laptop with Intel i5-6300U CPU at 2.40 GHz and 24 GiB RAM; single-thread Matlab code, not optimised",[],[302],"Average computation time over 50 trials for different integration interval lengths; IMU 100 Hz, UPM upsampled to 1 kHz; for UPM and GPM the hyper-parameter training time and the inference time are listed separately",{"slug":304,"group":305,"sourceId":100,"sourceLabel":101,"table":306,"selfRows":307,"metrics":308,"seqs":317,"entrants":334,"cells":340,"outcomes":431,"locators":432,"hardware":433,"wordings":434,"notes":435},"legentil2020gpm-table-ii","legentil2020gpm:Table II","Table II",16,[309,312,315],{"label":310,"unit":311,"statistic":108,"alignment":42},"average absolute rotation error of preintegrated measurement","mrad",{"label":313,"unit":314,"statistic":108,"alignment":42},"average absolute position error of preintegrated measurement","mm",{"label":310,"unit":316,"statistic":108,"alignment":42},"rad (as printed; the 1D block uses mrad)",[318,320,322,324,326,328,330,332],{"dataset":113,"sequence":319,"environment":20},"1D rotations, query rate 20 Hz",{"dataset":113,"sequence":321,"environment":20},"1D rotations, query rate 10 Hz",{"dataset":113,"sequence":323,"environment":20},"1D rotations, query rate 2 Hz",{"dataset":113,"sequence":325,"environment":20},"1D rotations, query rate 1 Hz",{"dataset":113,"sequence":327,"environment":20},"3D rotations, query rate 20 Hz",{"dataset":113,"sequence":329,"environment":20},"3D rotations, query rate 10 Hz",{"dataset":113,"sequence":331,"environment":20},"3D rotations, query rate 2 Hz",{"dataset":113,"sequence":333,"environment":20},"3D rotations, query rate 1 Hz",[335,338,339],{"name":336,"methodId":337,"linkable":148,"proposed":69,"self":69},"PM (on-manifold preintegration [14])","forster2017preint",{"name":147,"methodId":5,"linkable":148,"proposed":69,"self":148},{"name":150,"methodId":100,"linkable":148,"proposed":148,"self":69},[341,343,345,347,349,351,353,355,357,359,361,363,365,367,369,371,373,375,377,379,381,383,385,386,387,389,391,393,395,396,397,398,400,402,404,406,408,410,412,414,416,418,420,422,424,426,428,429],[153,153,153,342,155,153,155,155,153],2.41,[153,157,153,344,155,153,155,155,153],5.61,[157,153,153,346,155,153,155,155,153],0.28,[157,157,153,348,155,153,155,155,153],0.6,[172,153,153,350,155,153,155,155,153],0.1,[172,157,153,352,155,153,155,155,153],0.02,[153,153,157,354,155,153,155,155,153],4.91,[153,157,157,356,155,153,155,155,153],12.1,[157,153,157,358,155,153,155,155,153],1.61,[157,157,157,360,155,153,155,155,153],1.32,[172,153,157,362,155,153,155,155,153],1.4,[172,157,157,364,155,153,155,155,153],0.16,[153,153,172,366,155,153,155,155,153],21.3,[153,157,172,368,155,153,155,155,153],61.3,[157,153,172,370,155,153,155,155,153],3.09,[157,157,172,372,155,153,155,155,153],7.19,[172,153,172,374,155,153,155,155,153],0.2,[172,157,172,376,155,153,155,155,153],0.61,[153,153,181,378,155,153,155,155,153],24.9,[153,157,181,380,155,153,155,155,153],125,[157,153,181,382,155,153,155,155,153],2.51,[157,157,181,384,155,153,155,155,153],12.6,[172,153,181,346,155,153,155,155,153],[172,157,181,170,155,153,155,155,153],[153,172,190,388,155,153,155,155,153],5.48,[153,157,190,390,155,153,155,155,153],6.06,[157,172,190,392,155,153,155,155,153],0.8,[157,157,190,394,155,153,155,155,153],0.65,[172,172,190,392,155,153,155,155,153],[172,157,190,352,155,153,155,155,153],[153,172,199,241,155,153,155,155,153],[153,157,199,399,155,153,155,155,153],13.1,[157,172,199,401,155,153,155,155,153],7.53,[157,157,199,403,155,153,155,155,153],2.27,[172,172,199,405,155,153,155,155,153],7.18,[172,157,199,407,155,153,155,155,153],0.48,[153,172,95,409,155,153,155,155,153],35.6,[153,157,95,411,155,153,155,155,153],65,[157,172,95,413,155,153,155,155,153],4.72,[157,157,95,415,155,153,155,155,153],7.29,[172,172,95,417,155,153,155,155,153],3.56,[172,157,95,419,155,153,155,155,153],1.74,[153,172,216,421,155,153,155,155,153],37.5,[153,157,216,423,155,153,155,155,153],159,[157,172,216,425,155,153,155,155,153],3.75,[157,157,216,427,155,153,155,155,153],15.9,[172,172,216,425,155,153,155,155,153],[172,157,216,430,155,153,155,155,153],10.3,[],[306],[],[],[436],"Simulated fast trajectories, 100 trials, fixed query rates of 1 to 20 Hz; average absolute error of the preintegrated measurement (the paper calls it absolute pose error); units as printed: mrad and mm for 1D rotations, 'rad' and mm for 3D rotations",{"slug":438,"group":439,"sourceId":100,"sourceLabel":101,"table":440,"selfRows":225,"metrics":441,"seqs":447,"entrants":456,"cells":460,"outcomes":507,"locators":508,"hardware":509,"wordings":510,"notes":511},"legentil2020gpm-table-i","legentil2020gpm:Table I","Table I",[442,445],{"label":443,"unit":444,"statistic":108,"alignment":42},"average relative rotation error of preintegrated measurement","%",{"label":446,"unit":444,"statistic":108,"alignment":42},"average relative position error of preintegrated measurement",[448,450,452,454],{"dataset":113,"sequence":449,"environment":20},"1D rotations, slow motion",{"dataset":113,"sequence":451,"environment":20},"1D rotations, fast motion",{"dataset":113,"sequence":453,"environment":20},"3D rotations, slow motion",{"dataset":113,"sequence":455,"environment":20},"3D rotations, fast motion",[457,458,459],{"name":336,"methodId":337,"linkable":148,"proposed":69,"self":69},{"name":147,"methodId":5,"linkable":148,"proposed":69,"self":148},{"name":150,"methodId":100,"linkable":148,"proposed":148,"self":69},[461,463,465,467,469,471,473,475,477,479,481,483,485,487,489,491,493,494,496,498,500,502,504,505],[153,153,153,462,155,153,155,155,153],0.253,[153,157,153,464,155,153,155,155,153],3.54,[157,153,153,466,155,153,155,155,153],0.038,[157,157,153,468,155,153,155,155,153],0.381,[172,153,153,470,155,153,155,155,153],0.028,[172,157,153,472,155,153,155,155,153],0.143,[153,153,157,474,155,153,155,155,153],0.221,[153,157,157,476,155,153,155,155,153],2.9,[157,153,157,478,155,153,155,155,153],0.024,[157,157,157,480,155,153,155,155,153],0.301,[172,153,157,482,155,153,155,155,153],0.008,[172,157,157,484,155,153,155,155,153],0.073,[153,153,172,486,155,153,155,155,153],0.316,[153,157,172,488,155,153,155,155,153],4.79,[157,153,172,490,155,153,155,155,153],0.035,[157,157,172,492,155,153,155,155,153],0.493,[172,153,172,490,155,153,155,155,153],[172,157,172,495,155,153,155,155,153],0.311,[153,153,181,497,155,153,155,155,153],0.308,[153,157,181,499,155,153,155,155,153],4.35,[157,153,181,501,155,153,155,155,153],0.031,[157,157,181,503,155,153,155,155,153],0.433,[172,153,181,501,155,153,155,155,153],[172,157,181,506,155,153,155,155,153],0.396,[],[440],[],[],[512],"Simulated random sinusoidal trajectories, integration interval 1 to 5 s, 100 trials; average relative error with respect to travelled linear or angular distance; IMU 100 Hz, accelerometer noise sd 0.02 m\u002Fs2, gyroscope noise sd 0.002 rad\u002Fs, UPM upsampled to 1 kHz",{"slug":514,"group":515,"sourceId":5,"sourceLabel":6,"table":440,"selfRows":190,"metrics":516,"seqs":523,"entrants":529,"cells":534,"outcomes":551,"locators":552,"hardware":553,"wordings":554,"notes":555},"legentil2018lidarimucalib-table-i","legentil2018lidarimucalib:Table I",[517,520],{"label":518,"unit":519,"statistic":108,"alignment":42},"translational calibration error ep","m",{"label":521,"unit":522,"statistic":108,"alignment":42},"rotational calibration error eR","deg",[524,527],{"dataset":20,"sequence":525,"environment":526},"normal motion","simulated planes",{"dataset":20,"sequence":528,"environment":526},"fast motion",[530,532],{"name":531,"methodId":5,"linkable":148,"proposed":148,"self":148},"(i) with upsampled preintegrated measurements",{"name":533,"methodId":47,"linkable":69,"proposed":69,"self":69},"(ii) without upsampled preintegrated measurements",[535,537,539,541,543,545,547,549],[153,153,153,536,155,153,155,155,153],0.00057,[153,153,157,538,155,153,155,155,157],0.00095,[153,157,153,540,155,153,155,155,157],0.016,[153,157,157,542,155,153,155,155,157],0.013,[157,153,153,544,155,153,155,155,157],0.01,[157,153,157,546,155,153,155,155,157],0.0797,[157,157,153,548,155,153,155,155,157],0.23,[157,157,157,550,155,153,155,155,157],0.34,[],[440],[],[],[556,557],"Simulation with exact known IMU poses and noise-free measurements; mean calibration error over 10 Monte Carlo runs; normal motion 0.2 to 0.53 Hz sines, fast motion 1.53 Hz","Simulation with exact IMU poses and noise-free data",[559,564],{"group":560,"slug":561,"sourceLabel":6,"table":562,"selfRows":190,"datasets":563},"legentil2018lidarimucalib:Text Sec. IV-A3","legentil2018lidarimucalib-text-sec-iv-a3","Text Sec. IV-A3",[20],{"group":565,"slug":566,"sourceLabel":6,"table":567,"selfRows":172,"datasets":568},"legentil2018lidarimucalib:Text Sec. IV-B","legentil2018lidarimucalib-text-sec-iv-b","Text Sec. IV-B",[569],"own handheld room-corner sequence",1790510658518]