[{"data":1,"prerenderedAt":356},["ShallowReactive",2],{"method-lv2020licalib":3},{"method":4,"reference":52,"equipment":74,"figures":98,"results":99},{"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":27,"sensors":31,"platform":34,"estimator":36,"association":37,"timeModel":38,"deskew":39,"loopClosure":40,"globalOptimization":41,"mapRepresentation":42,"prior":43,"outputGeometry":44,"compute":45,"codeUrl":46,"codeLicense":47,"relatedVersions":48},"lv2020licalib","Lv et al., 2020","LI-Calib","Targetless Calibration of LiDAR-IMU System Based on Continuous-time Batch Estimation",2020,"recent","C13","sensing_calibration_sync_preprocessing","LI-Calib 以連續時間 B 樣條表示 IMU 軌跡，使每個 LiDAR 點的取樣時刻都能取得位姿，並直接以原始加速度與角速度殘差和點對面元（surfel）距離聯合最佳化外參。流程先對齊 LiDAR 與 IMU 旋轉初始化外參旋轉，再迭代去除運動畸變、重建面元地圖與更新對應。此法假設兩感測器已硬體同步，只估計空間外參。","Targetless LiDAR-IMU extrinsic calibration via continuous-time B-spline batch estimation with point-to-surfel constraints and iterative deskewing.","full_text_reviewed","peer_reviewed_published","main_body","未在工地測試；作者指出平面豐富的室內人造環境校正較佳，這對既有建築室內校正場有參考價值（推論）。",[20,21,22],"simulation","controlled_experiment","independent_reference",[24,25,26],"Simulation: translation error 0.0043 +\u002F- 0.0006 m and rotation error 0.0224 +\u002F- 0.0026 deg over 10 Monte Carlo sequences (Sec. V-A)","Real-world repeatability within a few millimetres and milliradians indoors; IMU-to-IMU relative poses differ from CAD by a few millimetres and under 1 deg (Table I, Table II)","Estimated spline trajectories matched a Vicon motion-capture reference with an average ATE of 0.0183 m over ten sequences of about 15 s (Sec. V-B, Fig. 5)",[28,29,30],"Depends on NDT-based LiDAR odometry in the first iteration; poor initial odometry leads to unreliable correspondences and inaccurate calibration (Sec. VI)","Outdoor repeatability of translation worse than indoor due to uneven planes (Sec. V-B)","Requires sufficient rotational and linear excitation (Sec. V-B)",[32,33],"3D LiDAR (VLP-16)","IMU (three Xsens MTi-100 series)",[35,20],"self-assembled LiDAR-IMU rig (carrier and mode of motion not reported)","continuous-time batch optimization (Levenberg-Marquardt, Kontiki toolkit) with split cubic B-splines in R3 and SO(3), knot spacing 0.02 s; state holds extrinsic rotation and translation, spline control points, gravity alignment and IMU biases; residuals from raw accelerometer and gyroscope readings and point-to-surfel distances","point-to-surfel: map split into cells (0.5 m indoor, 1.0 m outdoor); RANSAC plane fitted in cells whose plane-likeness exceeds 0.6 in the first iteration and 0.7 afterwards; correspondences beyond a distance threshold rejected; raw scans randomly downsampled","continuous-time (B-spline)","points reprojected with spline poses; rotation deskew after initialization, full deskew after each batch iteration","none","iterative batch refinement of surfel map and associations (about 4 iterations to converge)","surfel map (0.5 m cells indoor, 1.0 m outdoor)","hardware time synchronization assumed (spatial calibration only); extrinsic rotation initialized by aligning IMU spline rotations with NDT scan-to-map LiDAR odometry; extrinsic translation not initialized; sufficient rotational and linear excitation required","extrinsic LiDAR-IMU transform; undistorted point cloud map","offline batch; converges in about 4 iterations (results reported after 8); runtime and hardware not reported","https:\u002F\u002Fgithub.com\u002FAPRIL-ZJU\u002Flidar_IMU_calib","GPL-3.0",[49],{"relation":50,"title":8,"doi_or_url":51},"preprint","https:\u002F\u002Farxiv.org\u002Fabs\u002F2007.14759",{"id":5,"kind":53,"shortName":7,"title":8,"authors":54,"year":9,"venue":60,"venueType":61,"publisher":62,"volumeIssuePages":63,"doi":64,"arxivId":65,"url":51,"firstPublicDate":66,"publicationStatus":16,"metadataStatus":67,"fulltextStatus":15,"era":10,"classicReason":68,"codeUrl":46,"cluster":11,"topics":69,"mdpi":70,"verification":71,"label":6,"fulltextRoute":72,"versionRead":73,"addedByCensus":70},"method",[55,56,57,58,59],"Jiajun Lv","Jinhong Xu","Kewei Hu","Yong Liu","Xingxing Zuo","2020 IEEE\u002FRSJ International Conference on Intelligent Robots and Systems (IROS)","conference","IEEE","pp. 9968-9975","10.1109\u002Firos45743.2020.9341405","2007.14759","2020-07-29","metadata_verified","not_applicable",[11],false,"confirmed","arXiv","arXiv v1 (2020-07-29, the only arXiv version); IEEE IROS 2020 version of record not compared",[75,82,87,93],{"category":76,"model":77,"canonical":77,"role":78,"dataset":79,"specs":80,"locator":81},"lidar","Velodyne VLP-16","method input",null,"simulated to match the real sensor: 10 Hz scans, 360 deg horizontal and +\u002F-15 deg vertical field of view; hardware synchronized with the IMUs","Sec. V, V-A, Fig. 1",{"category":83,"model":84,"canonical":85,"role":78,"dataset":79,"specs":86,"locator":81},"imu","Xsens MTi-100 series (three units)","Xsens MTi-100","simulated to match the real sensors at 400 Hz",{"category":88,"model":89,"canonical":89,"role":90,"dataset":79,"specs":91,"locator":92},"other","Vicon motion capture system","reference or ground truth","ten sequences of about 15 s used for trajectory ATE","Sec. V-B",{"category":94,"model":95,"canonical":95,"role":78,"dataset":79,"specs":96,"locator":97},"platform","self-assembled LiDAR-IMU sensor rig","one VLP-16 and three Xsens IMUs; an attached camera was not used","Fig. 1, Sec. V",[],{"totalRows":100,"groupCount":101,"groups":102,"others":349},52,5,[103,217,268,300],{"slug":104,"group":105,"sourceId":5,"sourceLabel":6,"table":106,"selfRows":107,"metrics":108,"seqs":124,"entrants":133,"cells":136,"outcomes":211,"locators":212,"hardware":213,"wordings":214,"notes":215},"lv2020licalib-table-i","lv2020licalib:Table I","Table I",36,[109,113,115,117,120,122],{"label":110,"unit":111,"statistic":112,"alignment":40},"SD of x","m","std",{"label":114,"unit":111,"statistic":112,"alignment":40},"SD of y",{"label":116,"unit":111,"statistic":112,"alignment":40},"SD of z",{"label":118,"unit":119,"statistic":112,"alignment":40},"SD of roll","deg",{"label":121,"unit":119,"statistic":112,"alignment":40},"SD of pitch",{"label":123,"unit":119,"statistic":112,"alignment":40},"SD of yaw",[125,129,131],{"dataset":126,"sequence":127,"environment":128},"own real-world sequences","IMU1","indoor",{"dataset":126,"sequence":130,"environment":128},"IMU2",{"dataset":126,"sequence":132,"environment":128},"IMU3",[134],{"name":7,"methodId":5,"linkable":135,"proposed":135,"self":135},true,[137,141,144,147,150,153,154,156,158,160,162,164,166,167,169,171,173,175,176,178,180,182,184,186,188,190,192,194,196,198,200,202,204,206,207,209],[138,138,138,139,140,138,140,140,138],0,0.002,-1,[138,142,138,143,140,138,140,140,138],1,0.0053,[138,145,138,146,140,138,140,140,138],2,0.0014,[138,148,138,149,140,138,140,140,138],3,0.18,[138,151,138,152,140,138,140,140,138],4,0.28,[138,101,138,149,140,138,140,140,138],[138,138,142,155,140,138,140,140,138],0.0015,[138,142,142,157,140,138,140,140,138],0.0035,[138,145,142,159,140,138,140,140,138],0.0023,[138,148,142,161,140,138,140,140,138],0.06,[138,151,142,163,140,138,140,140,138],0.21,[138,101,142,165,140,138,140,140,138],0.11,[138,138,145,139,140,138,140,140,138],[138,142,145,168,140,138,140,140,138],0.0062,[138,145,145,170,140,138,140,140,138],0.0039,[138,148,145,172,140,138,140,140,138],0.17,[138,151,145,174,140,138,140,140,138],0.2,[138,101,145,172,140,138,140,140,138],[138,138,138,177,140,138,140,140,138],0.0034,[138,142,138,179,140,138,140,140,138],0.0031,[138,145,138,181,140,138,140,140,138],0.014,[138,148,138,183,140,138,140,140,138],0.07,[138,151,138,185,140,138,140,140,138],0.29,[138,101,138,187,140,138,140,140,138],0.12,[138,138,142,189,140,138,140,140,138],0.0052,[138,142,142,191,140,138,140,140,138],0.0047,[138,145,142,193,140,138,140,140,138],0.0114,[138,148,142,195,140,138,140,140,138],0.15,[138,151,142,197,140,138,140,140,138],0.36,[138,101,142,199,140,138,140,140,138],0.1,[138,138,145,201,140,138,140,140,138],0.0042,[138,142,145,203,140,138,140,140,138],0.0049,[138,145,145,205,140,138,140,140,138],0.012,[138,148,145,185,140,138,140,140,138],[138,151,145,208,140,138,140,140,138],0.26,[138,101,145,210,140,138,140,140,138],0.13,[],[106],[],[],[216],"SD of IMU-to-LiDAR extrinsic estimates over five trials per scene after eight iterations (means omitted)",{"slug":218,"group":219,"sourceId":5,"sourceLabel":6,"table":220,"selfRows":221,"metrics":222,"seqs":227,"entrants":244,"cells":246,"outcomes":262,"locators":263,"hardware":264,"wordings":265,"notes":266},"lv2020licalib-table-ii","lv2020licalib:Table II","Table II",8,[223,226],{"label":224,"unit":111,"statistic":225,"alignment":40},"Difference to CAD reference","mean",{"label":224,"unit":119,"statistic":225,"alignment":40},[228,230,232,234,236,238,240,242],{"dataset":126,"sequence":229,"environment":128},"IMU2 X",{"dataset":126,"sequence":231,"environment":128},"IMU2 Y",{"dataset":126,"sequence":233,"environment":128},"IMU2 Z",{"dataset":126,"sequence":235,"environment":128},"IMU3 Z",{"dataset":126,"sequence":237,"environment":128},"IMU2 Roll",{"dataset":126,"sequence":239,"environment":128},"IMU2 Pitch",{"dataset":126,"sequence":241,"environment":128},"IMU2 Yaw",{"dataset":126,"sequence":243,"environment":128},"IMU3 Yaw",[245],{"name":7,"methodId":5,"linkable":135,"proposed":135,"self":135},[247,249,251,252,254,255,256,259],[138,138,138,248,140,138,140,140,138],0.0012,[138,138,142,250,140,138,140,140,138],0.0017,[138,138,145,248,140,138,140,140,138],[138,138,148,253,140,138,140,140,138],0.0007,[138,142,151,183,140,138,140,140,138],[138,142,101,172,140,138,140,140,138],[138,142,257,258,140,138,140,140,138],6,0.67,[138,142,260,261,140,138,140,140,138],7,0.95,[],[220],[],[],[267],"Difference between calibrated relative pose of IMU2 or IMU3 w.r.t. IMU1 (mean over indoor datasets) and CAD reference",{"slug":269,"group":270,"sourceId":5,"sourceLabel":6,"table":271,"selfRows":151,"metrics":272,"seqs":278,"entrants":282,"cells":284,"outcomes":293,"locators":294,"hardware":296,"wordings":297,"notes":298},"lv2020licalib-text-sec-v-a","lv2020licalib:Text Sec. V-A","Text Sec. V-A",[273,276],{"label":274,"unit":111,"statistic":275,"alignment":40},"translational error of calibrated extrinsic vs ground truth","not_reported",{"label":277,"unit":119,"statistic":275,"alignment":40},"orientational error of calibrated extrinsic vs ground truth",[279],{"dataset":20,"sequence":280,"environment":281},"10 Monte Carlo sequences","simulated three orthogonal planes",[283],{"name":7,"methodId":5,"linkable":135,"proposed":135,"self":135},[285,287,289,291],[138,138,138,286,140,138,140,140,138],0.0043,[138,138,138,288,140,138,140,140,138],0.0006,[138,142,138,290,140,138,140,140,138],0.0224,[138,142,138,292,140,138,140,140,138],0.0026,[],[295],"Sec. V-A",[],[],[299],"Monte Carlo simulation, 10 sequences of 10 s, three orthogonal planes, sinusoidal IMU motion; the text gives each result as value +\u002F- value without defining the +\u002F- term, so mean and SD are not asserted",{"slug":301,"group":302,"sourceId":303,"sourceLabel":304,"table":305,"selfRows":148,"metrics":306,"seqs":314,"entrants":319,"cells":326,"outcomes":340,"locators":342,"hardware":344,"wordings":346,"notes":347},"zhu2022liinit-table-iv","zhu2022liinit:Table IV","zhu2022liinit","Zhu et al., 2022b","Table IV",[307,309,312],{"label":308,"unit":111,"statistic":225,"alignment":40},"Translation(m)",{"label":310,"unit":311,"statistic":275,"alignment":68},"Time(s), total calibration time","s",{"label":313,"unit":119,"statistic":225,"alignment":40},"Rotation(deg), taken from LI-Calib coarse calibration because refinement failed",[315],{"dataset":316,"sequence":317,"environment":318},"two PandarXT plus Pixhawk sequences from Sec. IV-B.1","first 40 s of each sequence","calibration scene of Fig. 7(e) (not described further)",[320,322,324],{"name":321,"methodId":303,"linkable":135,"proposed":135,"self":70},"Proposed",{"name":323,"methodId":5,"linkable":135,"proposed":70,"self":135},"LI-Calib [14]",{"name":325,"methodId":79,"linkable":70,"proposed":70,"self":70},"Target-Free [15]",[327,329,331,333,334,336,338],[138,138,138,328,140,138,140,140,138],0.0162,[138,142,138,330,140,138,138,140,138],10.2,[142,145,138,332,140,142,140,140,138],1.0375,[142,138,138,79,138,142,140,140,138],[142,142,138,335,140,138,138,140,138],332.6,[145,138,138,337,140,138,140,140,138],0.0187,[145,142,138,339,140,138,138,140,138],115.7,[341],"diverged (refinement fails; authors attribute it to missing gravity initialization)",[305,343],"Table IV and note",[345],"desktop Intel i7-10700 @2.90 GHz, 32 GB RAM",[],[348],"Extrinsic calibration comparison on two Hesai PandarXT plus Pixhawk sequences, first 40 s (400 scans) each; average relative IMU pose error against CAD; time offset pre-compensated for the baselines; default baseline parameters; all on the i7-10700 desktop",[350],{"group":351,"slug":352,"sourceLabel":6,"table":353,"selfRows":142,"datasets":354},"lv2020licalib:Text Sec. V-B","lv2020licalib-text-sec-v-b","Text Sec. V-B",[355],"own Vicon-room sequences",1790510664140]