[{"data":1,"prerenderedAt":218},["ShallowReactive",2],{"method-zhao2024deskew":3},{"method":4,"reference":56,"equipment":75,"figures":93,"results":94},{"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":30,"sensors":38,"platform":42,"estimator":45,"association":46,"timeModel":47,"deskew":48,"loopClosure":49,"globalOptimization":50,"mapRepresentation":49,"prior":50,"outputGeometry":51,"compute":52,"codeUrl":53,"codeLicense":54,"relatedVersions":55},"zhao2024deskew","Zhao et al., 2024a","Registration-based deskewing","Registration‐based point cloud deskewing and dynamic lidar simulation",2024,"recent","C13","sensing_calibration_sync_preprocessing","作者以點對面 ICP 配準相鄰兩幀 LiDAR 點雲取得幀間運動，假設單幀掃描期間轉換參數的變化率固定、雷射發射間隔固定，依各點的發射順序線性內插出部分轉換，把每個點轉回該幀起始位姿，因此不需 IMU，也不需每點的實際時間戳記。作者另提出以參考平面為基準的去畸變位移指標 PRDT，處理點到平面距離在運動方向與目標表面平行時無法反映畸變的問題；並把同一模型反向用於動態 LiDAR 模擬器，以雷射發射頻率更新感測器位姿，產生帶運動畸變的合成點雲。室內驗證使用 UGV 搭載 Velodyne HDL-32E，並以 TLS 點雲作為參考。","IMU-free deskewing: the inter-frame motion from point-to-plane ICP between consecutive scans is linearly interpolated over the laser firing order to move each point back to the frame-start pose; adds the plane-referenced deskewing translation (PRDT) metric and a dynamic LiDAR simulator built by reversing the model; validated indoors with a UGV-mounted Velodyne HDL-32E against a TLS reference and in a synthetic street model.","full_text_reviewed","peer_reviewed_published","main_body","未在施工現場測試；室內驗證位於大學工程館樓層，以 TLS 點雲作為參考（資料集另附同一區域的 BIM，但文中未見以 BIM 進行評估）。作者指出室內應用的精度要求較嚴，公分級改善也有意義；對缺少同步 IMU 的建築室內移動掃描，可作為去畸變與評估方法的參考（推論）。",[20,21,22],"completed_building","independent_reference","simulation",[24,25,26,27,28,29],"No IMU and no per-point timestamps required; any registration method can supply the inter-frame motion (Sec. 1, Sec. 3)","UoM indoor data: nearly 50% of point pairs moved more than 3 cm closer to the reference surface (PRDT) and about 5% by 10 cm or more (Sec. 4, Fig. 8)","Frame 63: deskewed cloud about 5 cm closer to the TLS reference at larger cut-off thresholds, and the visible tear is reduced (Sec. 4, Fig. 9 to 10)","About 0.074 s registration plus below 0.001 s coordinate update per ~70,000-point cloud, total below 0.1 s (Sec. 5)","Simulator updates the sensor pose at the laser firing rate and yields skewed and ideally deskewed clouds with exact correspondence; simulated distortion agrees with the theoretical model (Sec. 3, Sec. 4, Fig. 7 and 11)","Source code released (Sec. 1)",[31,32,33,34,35,36,37],"Outdoor conclusions rest on simulation and indoor tests; no real outdoor dataset with a matching TLS reference was available (Sec. 5)","Relies on successful registration of consecutive clouds, which is difficult where distinctive static geometry is lacking in all three dimensions (Sec. 5)","Moving objects may disturb registration and hence deskewing (Sec. 5)","Indoor platform moved slowly (about 1 km\u002Fh), so indoor improvements are around 3 cm (Sec. 5)","Assumes a constant rate of change of motion parameters within a scan and a constant firing interval (Sec. 3)","No comparison with IMU-based deskewing; stated as future work (Sec. 6)","PRDT discards point pairs not fitted to the same plane and excludes angles too close to 90 deg (threshold 10 deg used empirically) (Sec. 3, Sec. 4)",[39,40,41],"3D LiDAR Velodyne HDL-32E (UoM indoor data; up to 72,000 points per cloud, 1187 clouds)","No IMU used by the method","Evaluation reference: TLS point cloud (scanner model not reported); the dataset also includes a BIM of the same area, but no evaluation against the BIM is reported",[43,44],"UGV, indoor (third floor of the University of Melbourne engineering building; average 0.33 m\u002Fs and 14.85 deg\u002Fs)","Simulated vehicle at 40 to 100 km\u002Fh in a synthetic street 3D model (LiDAR at 10 fps)","Pairwise point-to-plane ICP between consecutive clouds gives the inter-frame transformation, followed by per-point linear interpolation of its rotation and translation parameters; no filter, smoother or pose-graph step","Point-to-plane ICP correspondences between consecutive point clouds (Low 2004); the authors state that any registration method could be substituted","Constant rate of change of the transformation parameters within one frame and a constant interval between consecutive laser firings; each point's fraction of the frame motion comes from its firing-order index, so actual timestamps are not required","IMU-free, registration-based: the ICP transformation between consecutive frames is linearly interpolated per point by firing order and each point is transformed back to the frame-start pose; the reverse process is used by the simulator to create distorted clouds","not_applicable","none","Deskewed point clouds; simulated skewed point clouds together with matching ideally deskewed clouds that serve as exact point-to-point ground truth","For clouds of about 70,000 points: average registration 0.074 s, coordinate update below 0.001 s, total below 0.1 s per frame, which the authors state allows real-time use with a 10 Hz LiDAR; hardware not reported",null,"not_verified",[],{"id":5,"kind":57,"shortName":7,"title":8,"authors":58,"year":9,"venue":62,"venueType":63,"publisher":64,"volumeIssuePages":65,"doi":66,"arxivId":53,"url":67,"firstPublicDate":68,"publicationStatus":16,"metadataStatus":69,"fulltextStatus":15,"era":10,"classicReason":49,"codeUrl":53,"cluster":11,"topics":70,"mdpi":71,"verification":72,"label":6,"fulltextRoute":73,"versionRead":74,"addedByCensus":71},"method",[59,60,61],"Yuan Zhao","Kourosh Khoshelham","Amir Khodabandeh","The Photogrammetric Record","journal","Wiley","39(188), pp. 831-844","10.1111\u002Fphor.12516","https:\u002F\u002Fdoi.org\u002F10.1111\u002Fphor.12516","2024-08-15","metadata_verified",[11],false,"confirmed","publisher OA","version of record, The Photogrammetric Record 39(188):831-844, Wiley Online Library HTML full text (first published online 2024-08-15, issue December 2024 per Crossref), open access CC BY 4.0",[76,83,87],{"category":77,"model":78,"canonical":78,"role":79,"dataset":80,"specs":81,"locator":82},"lidar","Velodyne HDL-32E","dataset sensor","UoM indoor data","up to 72,000 points per cloud; average 67,573 points over 1187 clouds; mounted on a moving UGV","Sec. 4 Data (UoM indoor data)",{"category":84,"model":85,"canonical":85,"role":79,"dataset":80,"specs":86,"locator":82},"platform","UGV (Unmanned Ground Vehicle), model not reported","average linear speed 0.33 m\u002Fs, average turn rate 14.85 deg\u002Fs; frame 63 captured at 0.33 m\u002Fs and 10.23 deg\u002Fs",{"category":88,"model":89,"canonical":89,"role":90,"dataset":80,"specs":91,"locator":92},"tls_scanner","TLS (Terrestrial Laser Scanner), model not reported","reference or ground truth","described as highly precise; 10 RANSAC planes manually selected from the TLS cloud for plane fitting","Sec. 3 Evaluation metrics, Sec. 4 Data, Fig. 4 to 5",[],{"totalRows":95,"groupCount":96,"groups":97,"others":217},8,3,[98,165,195],{"slug":99,"group":100,"sourceId":5,"sourceLabel":6,"table":101,"selfRows":102,"metrics":103,"seqs":120,"entrants":130,"cells":140,"outcomes":154,"locators":158,"hardware":161,"wordings":162,"notes":163},"zhao2024deskew-text-sec-5-discussion","zhao2024deskew:Text Sec.5 Discussion","Text Sec.5 Discussion",5,[104,108,112,116,118],{"label":105,"unit":106,"statistic":107,"alignment":107},"PRDT values around 3 cm (indoor improvement)","cm","not_reported",{"label":109,"unit":110,"statistic":111,"alignment":107},"improvement of mapping accuracy 'as large as 3 m' outdoors, 'up to 3.0 m' in Conclusions","m","max",{"label":113,"unit":114,"statistic":115,"alignment":107},"average time for point cloud registration","s","mean",{"label":117,"unit":114,"statistic":115,"alignment":107},"average time for calculating new point coordinates (\u003C 0.001 s)",{"label":119,"unit":114,"statistic":107,"alignment":107},"total time for the complete deskewing process (\u003C 0.1 s), real-time for a 10 Hz lidar",[121,124,128],{"dataset":80,"sequence":122,"environment":123},"all clouds","indoor building floor, platform at about 1 km\u002Fh",{"dataset":125,"sequence":126,"environment":127},"not_reported (author statement for outdoor use; Sec. 5 says outdoor results are interpreted from virtual environments, i.e. the synthetic street model, and indoor tests)","not_reported (depends on vehicle speed)","simulated outdoor street",{"dataset":129,"sequence":107,"environment":107},"not_reported (point clouds of about 70,000 points)",[131,134,136,138],{"name":132,"methodId":5,"linkable":133,"proposed":133,"self":133},"proposed point cloud deskewing method",true,{"name":135,"methodId":5,"linkable":133,"proposed":133,"self":133},"proposed deskewing: registration step (point-to-plane ICP)",{"name":137,"methodId":5,"linkable":133,"proposed":133,"self":133},"proposed deskewing: coordinate update step",{"name":139,"methodId":5,"linkable":133,"proposed":133,"self":133},"proposed deskewing: complete process",[141,144,146,149,151],[142,142,142,96,142,142,143,143,142],0,-1,[142,145,145,96,145,145,143,143,142],1,[145,147,147,148,143,142,143,143,142],2,0.074,[147,96,147,150,147,142,143,143,142],0.001,[96,152,147,153,147,142,143,143,142],4,0.1,[155,156,157],"other: approximate ('around')","other: author claim ('as large as 3 m' depending on vehicle speed; 'up to 3.0 m' in Conclusions); no deskewed-versus-skewed outdoor map comparison is reported, Sec. 4 mapping error is computed only for maps from skewed simulated clouds","other: reported as an upper bound (\u003C)",[159,160],"Sec. 5 Discussion","Sec. 5 Discussion, Sec. 6 Conclusions",[],[],[164],"Summary statements in the Discussion on indoor improvement, simulated outdoor improvement and runtime for about 70,000-point clouds",{"slug":166,"group":167,"sourceId":5,"sourceLabel":6,"table":168,"selfRows":147,"metrics":169,"seqs":175,"entrants":179,"cells":182,"outcomes":186,"locators":189,"hardware":191,"wordings":192,"notes":193},"zhao2024deskew-text-sec-4-indoor-evaluation","zhao2024deskew:Text Sec.4 indoor evaluation","Text Sec.4 indoor evaluation",[170,173],{"label":171,"unit":172,"statistic":107,"alignment":107},"share of point pairs moved more than 3 cm closer to the reference surface (PRDT above 3 cm), stated as 'nearly 50%'","%",{"label":174,"unit":172,"statistic":107,"alignment":107},"share of point pairs improved by 10 cm or more (PRDT of at least 10 cm), stated as 'around 5%'",[176],{"dataset":80,"sequence":177,"environment":178},"all 1187 clouds","indoor building floor, University of Melbourne engineering building",[180],{"name":181,"methodId":5,"linkable":133,"proposed":133,"self":133},"proposed registration-based deskewing (point-to-plane ICP)",[183,185],[142,142,142,184,142,142,143,143,142],50,[142,145,142,102,145,142,143,143,142],[187,188],"other: approximate value ('nearly') stated in text","other: approximate value ('around') stated in text",[190],"Sec. 4 (Evaluation of deskewing on indoor dataset), Fig. 8",[],[],[194],"PRDT computed for every skewed and deskewed point pair in the UoM indoor dataset with angle threshold 10 deg; percentages stated in text describing the cumulative distribution in Fig. 8",{"slug":196,"group":197,"sourceId":5,"sourceLabel":6,"table":198,"selfRows":145,"metrics":199,"seqs":202,"entrants":205,"cells":207,"outcomes":209,"locators":211,"hardware":213,"wordings":214,"notes":215},"zhao2024deskew-text-sec-4-frame-63","zhao2024deskew:Text Sec.4 frame 63","Text Sec.4 frame 63",[200],{"label":201,"unit":106,"statistic":115,"alignment":107},"reduction of mean point-to-point distance to TLS at larger cut-off thresholds (deskewed 'about 5 cm closer')",[203],{"dataset":80,"sequence":204,"environment":178},"frame 63 (0.33 m\u002Fs, 10.23 deg\u002Fs)",[206],{"name":181,"methodId":5,"linkable":133,"proposed":133,"self":133},[208],[142,142,142,102,142,142,143,143,142],[210],"other: approximate improvement relative to the skewed cloud, not an absolute distance",[212],"Sec. 4 (Evaluation of deskewing on indoor dataset), Fig. 10",[],[],[216],"Mean point-to-point distance to the TLS reference cloud against cut-off threshold for frame 63; difference between skewed and deskewed clouds at larger cut-off thresholds stated in text",[],1790510661924]