[{"data":1,"prerenderedAt":194},["ShallowReactive",2],{"method-velodyneslam2011":3},{"method":4,"reference":59,"equipment":81,"figures":101,"results":102},{"id":5,"label":6,"shortName":7,"title":7,"year":8,"era":9,"cluster":10,"scope":11,"keyIdeaZh":12,"keyIdeaEn":13,"fulltextStatus":14,"publicationStatus":15,"recommendation":16,"constructionRelevance":17,"validationEnvironment":18,"strengths":20,"limitations":26,"sensors":32,"platform":34,"estimator":36,"association":37,"timeModel":38,"deskew":39,"loopClosure":40,"globalOptimization":41,"mapRepresentation":42,"prior":41,"outputGeometry":43,"compute":44,"codeUrl":45,"codeLicense":46,"relatedVersions":47},"velodyneslam2011","Moosmann & Stiller, 2011","Velodyne SLAM",2011,"classic","C01","odometry_with_local_mapping","Velodyne SLAM 專為 Velodyne HDL-64E 的連續旋轉取樣與較高量測雜訊設計，只使用 LiDAR 資料。每轉一圈的資料排成 870 乘 64 的距離影像，先估計各點的法向量與平面信心，再以位置加法向量的 6D 最近鄰 ICP 將掃描對整張地圖配準，並在配準前後各做一次以線性內插的去畸變。地圖是每格最多保存一個曲面元素的 3D 網格；在平面信心高的區域，新量測會先沿法向量調整再加入，以降低雜訊造成的厚度。作者另提出離線精修步驟，以儲存的原始量測重建更細緻的地圖。","LiDAR-only scan-to-map ICP SLAM for the Velodyne HDL-64E with two-pass linear de-skewing and normal-guided measurement adaptation in a surface-grid map, plus offline map refinement; no loop closure.","full_text_reviewed","peer_reviewed_published","background","論文未在施工或建築環境測試，只在兩段含橋樑的戶外多層車載路線上評估。它把量測點沿法向量移動以壓低平面厚度，並提供離線地圖精修，目標是可作城市模型的細緻點雲；這種降低平面雜訊的做法與施工點雲的平面厚度問題相關，但論文只以目視檢查地圖品質，屬推論。",[19],"controlled_experiment",[21,22,23,24,25],"With mapping, de-skewing and adaptation the end-point error was 2.29 m after the 1.3 km loop and 4.10 m after the 1.1 km loop without loop closure, versus 3.30 m and 2.64 m for the INS (Table I).","Pairwise scan matching without mapping gave 19.60 m and 22.25 m, so scan-to-map matching is the main factor; de-skewing is the second (Table I; Sec. III).","The grid resolution can vary over a wide range with little effect on localization error; only map detail suffers (Sec. III; Fig. 5).","Adaptation and offline refinement produce visibly more detailed surfaces such as road surfaces (Figs. 6-7).","Dataset released publicly (Sec. III).",[27,28,29,30,31],"No loop closure (Sec. IV).","Limited to (nearly) static scenes (Sec. IV).","Map quality is assessed only by visual inspection (Sec. III).","Ground truth is only the end position obtained by matching the last scan to the first, because the INS had larger local errors than the method (Sec. III).","(inference) Runtime is not reported, so real-time capability is not established in the paper.",[33],"3D spinning LiDAR only (Velodyne HDL-64E S2); no wheel-speed, inertial or other information",[35],"vehicle (experimental vehicle AnnieWay)","scan-to-map ICP minimizing point-to-plane distances (Chen-Medioni) with nearest neighbours searched in 6D (position and normal), initialized by constant-motion prediction; 1000 surfaces sampled from the upper and 500 from the lower half of the range image (Sec. II.C)","6D nearest neighbour (px, py, pz, nx, ny, nz) between scan surfaces and map surfaces (Sec. II.C)","one pose at the end of each 360 deg turn; poses within a turn linearly interpolated assuming constant velocity (Sec. II.C)","linear-interpolation de-skewing applied twice, before ICP with the predicted pose and after ICP with the final pose (Sec. II.D)","none (named as future work)","none","3D grid of resolution g (5 cm default) holding at most one surface (point, normal, normal confidence) per cell; in flat regions new measurements are moved along their normals to fit neighbouring surfaces before insertion; cells are replaced by closer or more confident measurements and never erased (Sec. II.E)","vehicle trajectory and a detailed 3D point cloud of surfaces; an offline refinement builds a new map from the stored original measurements (Sec. II.F)","not_reported (the authors state that a low grid value gives precise maps and a higher value faster on-line processing)","https:\u002F\u002Fwww.mrt.kit.edu\u002Fz\u002Fpubl\u002Fdownload\u002Fvelodynetracking\u002Fcode.html","GNU General Public License (version not stated on the code page)",[48,52,56],{"relation":49,"title":50,"doi_or_url":51},"author_copy","Moosmann_IV11.pdf on the KIT MRT server (not read; VoR read instead)","http:\u002F\u002Fwww.mrt.kit.edu\u002Fz\u002Fpubl\u002Fdownload\u002FMoosmann_IV11.pdf",{"relation":53,"title":54,"doi_or_url":55},"dataset","Velodyne SLAM dataset (scenario 1 and 2, results of the paper without loop closure)","https:\u002F\u002Fwww.mrt.kit.edu\u002Fz\u002Fpubl\u002Fdownload\u002Fvelodyneslam\u002Fdataset.html",{"relation":57,"title":58,"doi_or_url":45},"code_release","Source code for joint self-localization and tracking of generic objects in 3D range data (SLAM + DATMO), linked from the Velodyne SLAM page as the complete development source code",{"id":5,"kind":60,"shortName":7,"title":7,"authors":61,"year":8,"venue":64,"venueType":65,"publisher":66,"volumeIssuePages":67,"doi":68,"arxivId":69,"url":70,"firstPublicDate":71,"publicationStatus":15,"metadataStatus":72,"fulltextStatus":14,"era":9,"classicReason":73,"codeUrl":45,"cluster":10,"topics":74,"mdpi":76,"verification":77,"label":6,"fulltextRoute":78,"versionRead":79,"addedByCensus":80},"method",[62,63],"Frank Moosmann","Christoph Stiller","2011 IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, Germany","conference","IEEE","pp. 393-398","10.1109\u002Fivs.2011.5940396",null,"https:\u002F\u002Fdoi.org\u002F10.1109\u002FIVS.2011.5940396","2011-06","metadata_verified","necessary technical node: pre-LOAM multi-beam LiDAR-only SLAM that treats continuous spinning acquisition by two-pass linear de-skewing and suppresses Velodyne range noise by adapting measurements along normals in a surface-grid map, with a public dataset; IMLS-SLAM [imlsslam2018] cites it as the HDL-64 SLAM that de-skews along the trajectory.",[10,75],"C04",false,"confirmed","author copy","author institute copy (KIT MRT) of the IEEE IV 2011 paper in proceedings layout with IEEE copyright line, pp. 393-398",true,[82,90,95],{"category":83,"model":84,"canonical":85,"role":86,"dataset":87,"specs":88,"locator":89},"lidar","Velodyne HDL-64E S2","Velodyne HDL-64E","method input","Velodyne SLAM dataset (AnnieWay, two scenarios)","64 laser diodes over about 26 deg pitch, continuous 360 deg rotation at about 10 Hz; each turn arranged as an 870 x 64 range image; relatively high measurement noise","Sec. I; Sec. II-A; Sec. II-D",{"category":91,"model":92,"canonical":92,"role":86,"dataset":87,"specs":93,"locator":94},"platform","experimental vehicle AnnieWay","scanner mounted on top of the vehicle; scenario 1 about 1.3 km and scenario 2 about 1.1 km, both with a bridge","Sec. I; Sec. III; Fig. 4",{"category":96,"model":97,"canonical":97,"role":98,"dataset":87,"specs":99,"locator":100},"gnss","integrated navigation system (INS) fusing GPS, wheel speed sensors and inertial measurements (model not reported)","compared device","local errors higher than those of the proposed method; not usable as ground truth","Sec. III; Table I",[],{"totalRows":103,"groupCount":104,"groups":105,"others":193},16,1,[106],{"slug":107,"group":108,"sourceId":5,"sourceLabel":6,"table":109,"selfRows":103,"metrics":110,"seqs":115,"entrants":122,"cells":141,"outcomes":187,"locators":188,"hardware":189,"wordings":190,"notes":191},"velodyneslam2011-table-i","velodyneslam2011:Table I","Table I",[111],{"label":112,"unit":113,"statistic":114,"alignment":41},"End-point error","m","not_reported",[116,120],{"dataset":117,"sequence":118,"environment":119},"Velodyne SLAM dataset (AnnieWay)","Scenario 1 (1.3 km)","outdoor multilayer scene with a bridge (vehicle)",{"dataset":117,"sequence":121,"environment":119},"Scenario 2 (1.1 km)",[123,125,127,129,131,133,135,137,139],{"name":124,"methodId":69,"linkable":76,"proposed":76,"self":76},"IMU (integrated navigation system: GPS, wheel speed, inertial)",{"name":126,"methodId":5,"linkable":80,"proposed":80,"self":80},"Setting 1 (mapping no, de-skewing no, adaptation no)",{"name":128,"methodId":5,"linkable":80,"proposed":80,"self":80},"Setting 2 (mapping no, de-skewing no, adaptation yes)",{"name":130,"methodId":5,"linkable":80,"proposed":80,"self":80},"Setting 3 (mapping no, de-skewing yes, adaptation no)",{"name":132,"methodId":5,"linkable":80,"proposed":80,"self":80},"Setting 4 (mapping no, de-skewing yes, adaptation yes)",{"name":134,"methodId":5,"linkable":80,"proposed":80,"self":80},"Setting 5 (mapping yes, de-skewing no, adaptation no)",{"name":136,"methodId":5,"linkable":80,"proposed":80,"self":80},"Setting 6 (mapping yes, de-skewing no, adaptation yes)",{"name":138,"methodId":5,"linkable":80,"proposed":80,"self":80},"Setting 7 (mapping yes, de-skewing yes, adaptation no)",{"name":140,"methodId":5,"linkable":80,"proposed":80,"self":80},"Setting 8 (mapping yes, de-skewing yes, adaptation yes)",[142,146,148,151,154,157,160,163,166,169,171,173,175,177,179,181,183,185],[143,143,143,144,145,143,145,145,143],0,3.3,-1,[104,143,143,147,145,143,145,145,143],19.6,[149,143,143,150,145,143,145,145,143],2,19.33,[152,143,143,153,145,143,145,145,143],3,27.41,[155,143,143,156,145,143,145,145,143],4,27.21,[158,143,143,159,145,143,145,145,143],5,4.47,[161,143,143,162,145,143,145,145,143],6,4.13,[164,143,143,165,145,143,145,145,143],7,2.9,[167,143,143,168,145,143,145,145,143],8,2.29,[143,143,104,170,145,143,145,145,143],2.64,[104,143,104,172,145,143,145,145,143],22.25,[149,143,104,174,145,143,145,145,143],22.09,[152,143,104,176,145,143,145,145,143],18.58,[155,143,104,178,145,143,145,145,143],18.89,[158,143,104,180,145,143,145,145,143],8.08,[161,143,104,182,145,143,145,145,143],7.36,[164,143,104,184,145,143,145,145,143],4.81,[167,143,104,186,145,143,145,145,143],4.1,[],[109],[],[],[192],"End-point error (Euclidean distance between estimated end position and the end position obtained by ICP of the last scan to the first scan) on two loop scenarios; no loop closure; map parts beyond 50 m discarded; g = 5 cm",[],1790510663492]