[{"data":1,"prerenderedAt":96},["ShallowReactive",2],{"method-laconte2019lidarbias":3},{"method":4,"reference":49,"equipment":69,"figures":95,"results":43},{"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":32,"platform":35,"estimator":38,"association":39,"timeModel":39,"deskew":39,"loopClosure":39,"globalOptimization":40,"mapRepresentation":39,"prior":40,"outputGeometry":41,"compute":42,"codeUrl":43,"codeLicense":44,"relatedVersions":45},"laconte2019lidarbias","Laconte et al., 2019","Incidence-angle LiDAR bias model","Lidar Measurement Bias Estimation via Return Waveform Modelling in a Context of 3D Mapping",2019,"recent","C13","sensing_calibration_sync_preprocessing","作者質疑 LiDAR 量測為零均值高斯雜訊的常見假設，指出與入射角及距離相關的偏差會造成可預期的定位漂移，例如直線隧道的地圖會依靠近哪一側牆而彎曲。本文以回波波形建模解釋此偏差，於實驗裝置量測三款 LiDAR 的偏差，發現高入射角下可達 20 cm，並用模型修正量測以改善地圖與漂移。","Models incidence-angle and range dependent LiDAR bias via return-waveform physics, measures it for three LiDARs (up to 20 cm), and corrects maps and drift.","full_text_reviewed","peer_reviewed_published","main_body","未在工地測試；隧道與長走廊的高入射角量測與營建地下工程及室內走廊高度相關（推論）。",[20,21,22],"controlled_experiment","underground_or_tunnel","independent_reference",[24,25,26],"Bias correction reduced map bending and localization drift in a long straight underground corridor example (Sec. I, Fig. 1)","A physical waveform model with only two per-sensor scale factors was fitted to bench data at depths 1 to 10 m and incidence angles 0 to 85 degrees for three LiDARs (Sec. IV, V, Table I)","Unlike the empirical model of Pfister et al., the analytic model captures the angle-dependent slope of bias with depth, and it needs fewer samples and extrapolates better than a non-parametric kernel fit (Sec. V)",[28,29,30,31],"Normals used for incidence angle should be estimated iteratively with bias taken into account (Sec. VI, future work)","Map improvements are shown only qualitatively; no quantitative drift or map error is reported (Sec. V, Figs. 1 and 9)","3D corrections were noisy at long range because normals from sparse HDL-32E data give unreliable incidence angles (Sec. V, Fig. 9)","Bench measurements beyond 10 m were unreliable because the beam exceeded the 0.6 m board at high angles, and residual setup misalignment may remain (Sec. IV)",[33,34],"2D LiDAR (SICK LMS151)","3D LiDAR (Velodyne HDL-32E, Robosense RS-LiDAR-16)",[36,37],"mobile robot carrying the LiDAR in a university tunnel (robot type not stated in the text read)","controlled test bench (linear motion table)","not_applicable (measurement model and correction)","not_applicable","none","bias-corrected range measurements and maps","closed-form correction implemented in libpointmatcher; authors state the computation is lightweight and does not affect mapping performance; no timings reported",null,"not_verified",[46],{"relation":47,"title":8,"doi_or_url":48},"preprint","https:\u002F\u002Farxiv.org\u002Fabs\u002F1810.01619",{"id":5,"kind":50,"shortName":7,"title":8,"authors":51,"year":9,"venue":56,"venueType":57,"publisher":58,"volumeIssuePages":59,"doi":60,"arxivId":61,"url":48,"firstPublicDate":62,"publicationStatus":16,"metadataStatus":63,"fulltextStatus":15,"era":10,"classicReason":39,"codeUrl":43,"cluster":11,"topics":64,"mdpi":65,"verification":66,"label":6,"fulltextRoute":67,"versionRead":68,"addedByCensus":65},"component",[52,53,54,55],"Johann Laconte","Simon-Pierre Deschenes","Mathieu Labussiere","Francois Pomerleau","2019 International Conference on Robotics and Automation (ICRA)","conference","IEEE","pp. 8100-8106","10.1109\u002Ficra.2019.8793671","1810.01619","2018-10-03","metadata_verified",[11],false,"corrected","arXiv","arXiv v2 (2019-08-28), IEEE copyright notice in arXiv comments; VoR (ICRA 2019) not compared",[70,77,81,85,91],{"category":71,"model":72,"canonical":73,"role":74,"dataset":43,"specs":75,"locator":76},"lidar","Sick LMS151","SICK LMS-151","method input","2D LiDAR; fitted aperture half-angle 0.43 deg (Table I)","Sec. IV, V, Table I, Fig. 1",{"category":71,"model":78,"canonical":78,"role":74,"dataset":43,"specs":79,"locator":80},"Velodyne HDL-32E","3D LiDAR; fitted aperture half-angle 0.085 deg (Table I); mounted on the robot for the 3D tunnel map","Sec. IV, V, Table I, Fig. 9",{"category":71,"model":82,"canonical":82,"role":74,"dataset":43,"specs":83,"locator":84},"Robosense RS-LiDAR-16","3D LiDAR lent by Robosense; fitted aperture half-angle 0.085 deg (Table I)","Sec. IV, V, Table I",{"category":86,"model":87,"canonical":87,"role":88,"dataset":43,"specs":89,"locator":90},"other","Renishaw XL-80 interferometer","reference or ground truth","ground-truth board distance with accuracy of the order of 1 micrometre","Sec. IV, Figs. 5-6",{"category":86,"model":92,"canonical":92,"role":88,"dataset":43,"specs":93,"locator":94},"linear motion table (rail) with rotating wooden board","rail of about 30 m; board 0.6 x 0.6 m on a graduated disc; 12 board orientations (0 to 60 deg in 10 deg steps, 65 to 85 deg in 5 deg steps) at 8 depths from 1 to 10 m; 45 s per setting; metrology room at 20 C","Sec. IV, Fig. 5",[],1790510661451]