[{"data":1,"prerenderedAt":399},["ShallowReactive",2],{"method-rehder2016spatiotemporal":3},{"method":4,"reference":57,"equipment":77,"figures":107,"results":108},{"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":28,"sensors":34,"platform":39,"estimator":42,"association":43,"timeModel":44,"deskew":45,"loopClosure":45,"globalOptimization":46,"mapRepresentation":45,"prior":47,"outputGeometry":48,"compute":49,"codeUrl":50,"codeLicense":51,"relatedVersions":52},"rehder2016spatiotemporal","Rehder et al., 2016","General spatiotemporal calibration","A General Approach to Spatiotemporal Calibration in Multisensor Systems",2016,"classic","C13","sensing_calibration_sync_preprocessing","作者把感測器時間戳記與實際量測時刻之間的固定偏移視為確定性誤差，在連續時間 B 樣條批次最大概似估計中與空間外參一起求解。文中推導相機與 IMU、相機與 IMU 與 2D 雷射測距儀、以及立體相機與雷射測距儀等多種估計器；雷射部分以自動平面偵測與沿光束方向的距離模型建立約束。實驗顯示時間偏移可估到遠小於最短取樣間隔，且經軟體去除抖動與時鐘偏斜後的結果接近硬體同步。","Sensor-agnostic continuous-time spatiotemporal calibration, demonstrated for camera-IMU and stereo-laser pairs.","full_text_reviewed","peer_reviewed_published","background","未在工地測試。Fig. 1 顯示手持掃描裝置的雷射時間戳記若帶有固定偏移，重建點雲會明顯變形，正確處理時間關係後重建較準確；文中也指出以抵達時間打戳記的抖動會使空間率定的散布大幅增加。對使用手持或背負式 LiDAR 建立工地點雲的系統，時間偏移應與外參一起率定（推論）。",[20,21],"simulation","controlled_experiment",[23,24,25,26,27],"More repeatable and accurate than prior methods; millimetre-level spatial precision and sub-interval temporal offsets (abstract)","Camera-IMU offset versus exposure time: fitted slope 0.498 against a theoretical 0.5, all residuals within +\u002F-0.2 ms; RMS 0.054 ms for estimator J versus 0.165 to 0.572 ms for subset estimators, separated calibration and TD-ICP (Sec. IV-D, Fig. 7, Fig. 9)","LRF offset with hardware synchronization 2.603 +\u002F- 0.045 ms, close to the 2.725 ms product specification; software-synchronized results comparable to hardware synchronization (Table III, Sec. IV-E)","LRF orientation repeatability 0.096 deg for estimator L and 0.121 deg for C (Sec. IV-E)","From perturbed initial values, 92 of 100 runs of J and 79 of 100 runs of L converged correctly (Sec. IV-F)",[29,30,31,32,33],"The cumulative range bias reflects the calibration environment and is not suited to correct measurements elsewhere (Sec. III-B5)","Simplified LRF model: no beam-direction error and range-independent Gaussian noise (Sec. III-B5)","Automatic plane detection fails for badly wrong initial estimates, and d_l was biased by about 2 ms at 50 ms initial corruption (Sec. IV-F)","Timestamps assigned on arrival inflate the spread: translation SD up to 12.1 mm and orientation 0.854 deg (Sec. IV-G)","Offline procedure requiring deliberate excitation of all rotational degrees of freedom; demonstrated only with a 2D LRF (scope observation, Sec. III-B, IV-A)",[35,36,37,38],"Aptina MT9V034 WVGA global-shutter cameras at 20 Hz (one in Setup I, two in Setup II)","Analog Devices ADIS16488 (Setup I) and ADIS16448 (Setup II) IMUs at 200 Hz","Hokuyo UTM-30LX 2D laser range finder (Setup II), 270 deg scans at 40 Hz","FPGA-based visual-inertial sensor assigning hardware timestamps",[40,41,20],"hand-guided sensor head moved in front of a checkerboard (Setups I and II)","hand-held scanning device (Setup II; reconstructions in Fig. 1)","continuous-time batch maximum-likelihood estimation solved by Levenberg-Marquardt, with a sixth-order B-spline IMU pose and cubic B-spline biases; constant time offsets folded into measurement times with analytic Jacobians; five estimators (J, G, A, L, C) for different sensor subsets; estimator J released in kalibr","camera: checkerboard corner reprojection; LRF: range points associated with planes found by RANSAC (threshold 60 mm), region growing and eigenvalue checks, using a beam-direction range model with a cumulative range bias and a Blake-Zisserman robust cost","continuous-time B-splines; constant temporal offsets d_c and d_l estimated relative to IMU time; assumes prior clock synchronization by hardware or software","not_applicable","batch","checkerboard of known geometry roughly aligned with gravity; environment partly planar for LRF calibration; initial extrinsics and offsets within hand-measurable accuracy; IMU noise parameters from Allan variance","temporal offsets, camera-IMU and LRF-IMU transforms, gravity direction, IMU bias trajectories, plane parameters and a cumulative LRF range bias","offline batch; LRF ranges subsampled to about 15% to limit run time; runtime and hardware not reported",null,"not_verified",[53],{"relation":54,"title":55,"doi_or_url":56},"related_prior_work_same_group (conference-version relation not verified)","Unified temporal and spatial calibration for multi-sensor systems (IROS 2013; see furgale2013unifiedcalib)","https:\u002F\u002Fdoi.org\u002F10.1109\u002FIROS.2013.6696514",{"id":5,"kind":58,"shortName":7,"title":8,"authors":59,"year":9,"venue":63,"venueType":64,"publisher":65,"volumeIssuePages":66,"doi":67,"arxivId":50,"url":68,"firstPublicDate":69,"publicationStatus":16,"metadataStatus":70,"fulltextStatus":15,"era":10,"classicReason":71,"codeUrl":50,"cluster":11,"topics":72,"mdpi":73,"verification":74,"label":6,"fulltextRoute":75,"versionRead":76,"addedByCensus":73},"method",[60,61,62],"Joern Rehder","Roland Siegwart","Paul Furgale","IEEE Transactions on Robotics","journal","IEEE","32(2), pp. 383-398","10.1109\u002Ftro.2016.2529645","https:\u002F\u002Fdoi.org\u002F10.1109\u002FTRO.2016.2529645","2016-04","metadata_verified","evaluation-calibration method: sensor-agnostic formulation of temporal offsets as deterministic error sources, demonstrated also between a stereo camera and a laser range finder.",[11],false,"corrected","NTU institutional (curl)","Version of record, IEEE Transactions on Robotics 32(2):383-398, April 2016 (IEEE Xplore PDF)",[78,84,89,94,97,102],{"category":79,"model":80,"canonical":80,"role":81,"dataset":50,"specs":82,"locator":83},"camera","Aptina MT9V034","method input","WVGA global-shutter image sensor, 20 Hz, fixed exposure; single camera used in Setup I","Sec. IV-A, IV-B, Fig. 4(a)",{"category":85,"model":86,"canonical":86,"role":81,"dataset":50,"specs":87,"locator":88},"stereo_camera","Aptina MT9V034 (two cameras)","both cameras used in Setup II, 20 Hz","Sec. IV-A, Fig. 4(b)",{"category":90,"model":91,"canonical":91,"role":81,"dataset":50,"specs":92,"locator":93},"imu","Analog Devices ADIS16488","Setup I; 200 Hz; noise parameters from Allan variance","Sec. IV-A, IV-B",{"category":90,"model":95,"canonical":95,"role":81,"dataset":50,"specs":96,"locator":93},"Analog Devices ADIS16448","Setup II; 200 Hz; noise parameters from Allan variance",{"category":98,"model":99,"canonical":99,"role":81,"dataset":50,"specs":100,"locator":101},"lidar","Hokuyo UTM-30LX","2D laser range finder, full 270 deg scans at 40 Hz, device timestamps quantized to 1 ms, range noise SD set to 7.5 mm, ranges subsampled to about 15%","Sec. IV-A, IV-B, IV-G",{"category":103,"model":104,"canonical":104,"role":81,"dataset":50,"specs":105,"locator":106},"other","FPGA-based visual-inertial sensor (Nikolic et al.)","routes all sensor data through an FPGA so hardware timestamps are assigned concurrently, including the LRF trigger output","Sec. IV-A",[],{"totalRows":109,"groupCount":110,"groups":111,"others":382},65,7,[112,222,287,326],{"slug":113,"group":114,"sourceId":5,"sourceLabel":6,"table":115,"selfRows":116,"metrics":117,"seqs":138,"entrants":143,"cells":151,"outcomes":216,"locators":217,"hardware":218,"wordings":219,"notes":220},"rehder2016spatiotemporal-table-iii","rehder2016spatiotemporal:Table III","Table III",30,[118,123,125,127,129,130,131,134,135,137],{"label":119,"unit":120,"statistic":121,"alignment":122},"spatial displacement t_l_i x","mm","mean","none",{"label":124,"unit":120,"statistic":121,"alignment":122},"spatial displacement t_l_i y",{"label":126,"unit":120,"statistic":121,"alignment":122},"spatial displacement t_l_i z",{"label":119,"unit":120,"statistic":128,"alignment":122},"std",{"label":124,"unit":120,"statistic":128,"alignment":122},{"label":126,"unit":120,"statistic":128,"alignment":122},{"label":132,"unit":133,"statistic":121,"alignment":122},"temporal offset d_l","ms",{"label":132,"unit":133,"statistic":128,"alignment":122},{"label":136,"unit":120,"statistic":121,"alignment":122},"cumulative range bias b_l",{"label":136,"unit":120,"statistic":128,"alignment":122},[139],{"dataset":140,"sequence":141,"environment":142},"Setup II, 30 runs","all 30 runs","mostly empty room",[144,147,149],{"name":145,"methodId":5,"linkable":146,"proposed":146,"self":146},"estimator L, hardware synchronized",true,{"name":148,"methodId":5,"linkable":146,"proposed":146,"self":146},"estimator L, software synchronized",{"name":150,"methodId":5,"linkable":146,"proposed":146,"self":146},"estimator C, software synchronized",[152,156,159,162,165,168,170,172,174,176,177,179,181,183,185,187,188,189,191,194,196,198,200,201,203,206,208,210,212,214],[153,153,153,154,155,153,155,155,153],0,70,-1,[153,157,153,158,155,153,155,155,153],1,-40.9,[153,160,153,161,155,153,155,155,153],2,-67.4,[153,163,153,164,155,153,155,155,153],3,1.4,[153,166,153,167,155,153,155,155,153],4,1.5,[153,169,153,157,155,153,155,155,153],5,[157,153,153,171,155,153,155,155,153],69.8,[157,157,153,173,155,153,155,155,153],-40.8,[157,160,153,175,155,153,155,155,153],-67.3,[157,163,153,164,155,153,155,155,153],[157,166,153,178,155,153,155,155,153],1.7,[157,169,153,180,155,153,155,155,153],0.9,[160,153,153,182,155,153,155,155,153],71.2,[160,157,153,184,155,153,155,155,153],-41.8,[160,160,153,186,155,153,155,155,153],-66.9,[160,163,153,160,155,153,155,155,153],[160,166,153,164,155,153,155,155,153],[160,169,153,190,155,153,155,155,153],1.1,[153,192,153,193,155,153,155,155,153],6,2.603,[153,110,153,195,155,153,155,155,153],0.045,[157,192,153,197,155,153,155,155,153],-0.023,[157,110,153,199,155,153,155,155,153],0.106,[160,192,153,199,155,153,155,155,153],[160,110,153,202,155,153,155,155,153],0.088,[153,204,153,205,155,153,155,155,153],8,-16.6,[153,207,153,166,155,153,155,155,153],9,[157,204,153,209,155,153,155,155,153],-17.4,[157,207,153,211,155,153,155,155,153],4.2,[160,204,153,213,155,153,155,155,153],-21.8,[160,207,153,215,155,153,155,155,153],7.1,[],[115],[],[],[221],"LRF spatiotemporal calibration over 30 one-minute Setup II runs with simulated offsets -5, 0, +5 ms; hand-measured reference displacement [69, -42, -65] mm; spec offset 2.725 ms (trigger) and 0 ms (device timestamps)",{"slug":223,"group":224,"sourceId":5,"sourceLabel":6,"table":225,"selfRows":226,"metrics":227,"seqs":247,"entrants":251,"cells":254,"outcomes":280,"locators":281,"hardware":283,"wordings":284,"notes":285},"rehder2016spatiotemporal-text-sec-iv-c","rehder2016spatiotemporal:Text Sec. IV-C","Text Sec. IV-C",12,[228,230,232,234,235,236,237,240,242,244,245,246],{"label":229,"unit":120,"statistic":121,"alignment":122},"estimated camera-IMU displacement x",{"label":231,"unit":120,"statistic":121,"alignment":122},"estimated camera-IMU displacement y",{"label":233,"unit":120,"statistic":121,"alignment":122},"estimated camera-IMU displacement z",{"label":229,"unit":120,"statistic":128,"alignment":122},{"label":231,"unit":120,"statistic":128,"alignment":122},{"label":233,"unit":120,"statistic":128,"alignment":122},{"label":238,"unit":239,"statistic":121,"alignment":122},"estimated yaw","deg",{"label":241,"unit":239,"statistic":121,"alignment":122},"estimated pitch",{"label":243,"unit":239,"statistic":121,"alignment":122},"estimated roll",{"label":238,"unit":239,"statistic":128,"alignment":122},{"label":241,"unit":239,"statistic":128,"alignment":122},{"label":243,"unit":239,"statistic":128,"alignment":122},[248],{"dataset":20,"sequence":249,"environment":250},"500 runs","simulated planar landmark grid",[252],{"name":253,"methodId":5,"linkable":146,"proposed":146,"self":146},"estimator J",[255,257,259,261,263,265,267,269,271,272,274,277],[153,153,153,256,155,153,155,155,153],103.73,[153,157,153,258,155,153,155,155,153],-15.18,[153,160,153,260,155,153,155,155,153],-9.98,[153,163,153,262,155,153,155,155,153],0.38,[153,166,153,264,155,153,155,155,153],0.98,[153,169,153,266,155,153,155,155,153],0.17,[153,192,153,268,155,153,155,155,153],179.999,[153,110,153,270,155,153,155,155,153],-0.01,[153,204,153,153,155,153,155,155,153],[153,207,153,273,155,153,155,155,153],0.003,[153,275,153,276,155,153,155,155,153],10,0.009,[153,278,153,279,155,153,155,155,153],11,0.007,[],[282],"Sec. IV-C",[],[],[286],"Simulation of estimator J: 500 runs of 90 s, delays -8 to 8 ms; true displacement [103, -15, -10] mm and 180 deg rotation about the optical axis",{"slug":288,"group":289,"sourceId":5,"sourceLabel":6,"table":290,"selfRows":110,"metrics":291,"seqs":300,"entrants":302,"cells":305,"outcomes":319,"locators":320,"hardware":322,"wordings":323,"notes":324},"rehder2016spatiotemporal-text-sec-iv-g","rehder2016spatiotemporal:Text Sec. IV-G","Text Sec. IV-G",[292,293,294,295,296,297,298],{"label":119,"unit":120,"statistic":121,"alignment":122},{"label":124,"unit":120,"statistic":121,"alignment":122},{"label":126,"unit":120,"statistic":121,"alignment":122},{"label":119,"unit":120,"statistic":128,"alignment":122},{"label":124,"unit":120,"statistic":128,"alignment":122},{"label":126,"unit":120,"statistic":128,"alignment":122},{"label":299,"unit":239,"statistic":128,"alignment":122},"orientation repeatability (sqrt variance about Frechet mean)",[301],{"dataset":140,"sequence":141,"environment":142},[303],{"name":304,"methodId":5,"linkable":146,"proposed":146,"self":146},"estimator L, timestamps on arrival",[306,308,310,311,313,315,317],[153,153,153,307,155,153,155,155,153],64.1,[153,157,153,309,155,153,155,155,153],-40.3,[153,160,153,175,155,153,155,155,153],[153,163,153,312,155,153,155,155,153],12.1,[153,166,153,314,155,153,155,155,153],6.6,[153,169,153,316,155,153,155,155,153],3.2,[153,192,153,318,155,153,155,155,153],0.854,[],[321],"Sec. IV-G",[],[],[325],"Estimator L repeated on the Setup II dataset with range timestamps assigned on arrival (no jitter correction)",{"slug":327,"group":328,"sourceId":5,"sourceLabel":6,"table":329,"selfRows":192,"metrics":330,"seqs":338,"entrants":343,"cells":354,"outcomes":375,"locators":376,"hardware":378,"wordings":379,"notes":380},"rehder2016spatiotemporal-fig-9-table","rehder2016spatiotemporal:Fig. 9 table","Fig. 9 table",[331,335],{"label":332,"unit":333,"statistic":334,"alignment":122},"slope of temporal offset vs exposure time","ms\u002Fms","not_reported",{"label":336,"unit":133,"statistic":337,"alignment":122},"RMS error to slope-0.5 line","RMSE",[339],{"dataset":340,"sequence":341,"environment":342},"Setup I, 40 runs","4 exposure series","lab, checkerboard",[344,346,348,350,352],{"name":345,"methodId":5,"linkable":146,"proposed":146,"self":146},"J (joint estimation)",{"name":347,"methodId":5,"linkable":146,"proposed":146,"self":146},"G (gyroscopes only)",{"name":349,"methodId":50,"linkable":73,"proposed":73,"self":73},"S (separated estimation, Mair et al.)",{"name":351,"methodId":50,"linkable":73,"proposed":73,"self":73},"T (TD-ICP, Kelly et al.)",{"name":353,"methodId":5,"linkable":146,"proposed":146,"self":146},"A (accelerometer only)",[355,357,359,361,363,365,367,369,371,373],[153,153,153,356,155,153,155,155,153],0.498,[153,157,153,358,155,153,155,155,153],0.054,[157,153,153,360,155,153,155,155,153],0.493,[157,157,153,362,155,153,155,155,153],0.165,[160,153,153,364,155,153,155,155,153],0.531,[160,157,153,366,155,153,155,155,153],0.344,[163,153,153,368,155,153,155,155,153],0.515,[163,157,153,370,155,153,155,155,153],0.467,[166,153,153,372,155,153,155,155,153],0.553,[166,157,153,374,155,153,155,155,153],0.572,[],[377],"Fig. 9 (embedded table)",[],[],[381],"Camera-IMU temporal offset vs exposure time, 40 hand-guided runs in 4 exposure series (Setup I, hardware sync); slope of best-fit line (theory 0.5) and RMS to a slope-0.5 line",[383,388,393],{"group":384,"slug":385,"sourceLabel":6,"table":386,"selfRows":192,"datasets":387},"rehder2016spatiotemporal:Text Sec. IV-D","rehder2016spatiotemporal-text-sec-iv-d","Text Sec. IV-D",[340],{"group":389,"slug":390,"sourceLabel":6,"table":391,"selfRows":160,"datasets":392},"rehder2016spatiotemporal:Text Sec. IV-E","rehder2016spatiotemporal-text-sec-iv-e","Text Sec. IV-E",[140],{"group":394,"slug":395,"sourceLabel":6,"table":396,"selfRows":160,"datasets":397},"rehder2016spatiotemporal:Text Sec. IV-F","rehder2016spatiotemporal-text-sec-iv-f","Text Sec. IV-F",[398],"Setup II",1790510658152]