[{"data":1,"prerenderedAt":244},["ShallowReactive",2],{"method-sslslam2021":3},{"method":4,"reference":61,"equipment":83,"figures":117,"results":118},{"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":28,"sensors":33,"platform":35,"estimator":39,"association":40,"timeModel":41,"deskew":42,"loopClosure":43,"globalOptimization":43,"mapRepresentation":44,"prior":43,"outputGeometry":45,"compute":46,"codeUrl":47,"codeLicense":48,"relatedVersions":49},"sslslam2021","Wang et al., 2021b","SSL_SLAM","Lightweight 3-D Localization and Mapping for Solid-State LiDAR",2021,"recent","C04","odometry_with_local_mapping","SSL_SLAM 是針對小視野、高頻率固態 LiDAR（Intel L515）設計的輕量 LiDAR 建圖定位。它先把點雲依垂直與水平角度分格並取格內平均，再以鄰域平滑度擷取邊緣與平面特徵，使特徵在大幅旋轉下仍較一致；位姿以掃描對滑動視窗局部地圖的點到邊、點到面殘差，在李群上以高斯牛頓法求解。全域地圖只用關鍵影格更新成八元樹佔據機率地圖。系統沒有迴圈閉合，在嵌入式小電腦上可即時執行。","Lightweight LiDAR-only odometry and mapping for small-FoV solid-state LiDAR (Intel L515): angular-grid binning with smoothness-based edge and plane features, Gauss-Newton scan-to-local-map optimization on the Lie group, and a keyframe-updated octree occupancy map; no loop closure, real time on an embedded mini PC.","full_text_reviewed","peer_reviewed_published","background","論文在倉庫 AGV 與室內手持掃描器上測試，並以兩台作業機台的量測尺寸與實際尺寸比對（相差不超過 3 cm），是少數以實物尺寸檢查地圖的例子，但樣本只有兩台設備。L515 的量測距離只有 0.25 至 9 m，限制了在大型工地的適用範圍；它在 VoxelMap 論文中被列為比較基準 [yuan2022voxelmap]。",[20,21,22],"controlled_experiment","independent_reference","completed_building",[24,25,26,27],"Translational error of 5 cm in a 4 m x 4 m VICON room at 31 ms per frame, where LOAM lost tracking under fast rotation (Sec. IV-B; Fig. 3)","Real time on an Intel NUC (42 ms per frame) on a warehouse AGV moving up to 0.8 m\u002Fs among dynamic operators and robots (Sec. IV-C)","Mapped machine dimensions were within a few centimetres of actual sizes for two machines (Sec. IV-C)","In the rotation test (up to 1.57 rad\u002Fs) it succeeded in 6 of 6 trials versus 1 of 6 for A-LOAM (Table II)",[29,30,31,32],"No loop closure or global optimization (Sec. III) (inference from the described modules)","Quantitative accuracy only from one small VICON room trial and two machine-dimension checks (Sec. IV-B; Sec. IV-C)","The L515 detection range is 0.25 to 9 m, which limits use in large spaces (Table I) (inference)","Hand-held devices suffer vibration and large viewing-angle change that can cause tracking loss (Sec. IV-D)",[34],"solid-state LiDAR only (Intel Realsense L515, 70 x 55 deg FoV, 30 Hz)",[36,37,38],"wheeled UGV (warehouse AGV)","handheld","ground robot in a VICON room (type not stated)","Gauss-Newton minimization of point-to-edge and point-to-plane residuals against a sliding-window local map, with left-perturbation updates on the Lie group and a constant-velocity initial guess (Sec. III-B; Algorithm 1)","points binned into an M x N grid of vertical and horizontal angle cells (cell means); edge and planar features from a local smoothness over a neighbourhood lambda; 2 nearest edge points or 3 nearest planar points found in k-d trees of the local edge and planar maps of the last q frames (Sec. III-A; Sec. III-B)","discrete poses","not described","none","sliding-window local edge and planar feature maps for odometry; global octree with per-cell occupancy probability updated from key frames selected by translation, rotation or elapsed-time thresholds (Sec. III-B; Sec. III-C)","trajectory and dense 3D probabilistic octree map (Figs. 1, 4 and 5)","31 ms per frame on a desktop with an Intel 6-core i7-8700; 42 ms per frame on an Intel NUC with an i5-10210U on the AGV; C++ with ROS Melodic on Ubuntu 18.04 (Sec. IV-A to IV-C)","https:\u002F\u002Fgithub.com\u002Fwh200720041\u002Fssl_slam","GPL-3.0 (LICENSE file read)",[50,54,58],{"relation":51,"title":52,"doi_or_url":53},"preprint","Lightweight 3-D Localization and Mapping for Solid-State LiDAR (arXiv v2, RA-L accepted preprint)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.03800",{"relation":55,"title":56,"doi_or_url":57},"accepted_manuscript","NTU repository handle 10356\u002F223176 (listed by OpenAlex; not opened)","https:\u002F\u002Fhdl.handle.net\u002F10356\u002F223176",{"relation":59,"title":60,"doi_or_url":47},"code_release","wh200720041\u002Fssl_slam",{"id":5,"kind":62,"shortName":7,"title":8,"authors":63,"year":9,"venue":67,"venueType":68,"publisher":69,"volumeIssuePages":70,"doi":71,"arxivId":72,"url":73,"firstPublicDate":74,"publicationStatus":16,"metadataStatus":75,"fulltextStatus":15,"era":10,"classicReason":76,"codeUrl":47,"cluster":11,"topics":77,"mdpi":78,"verification":79,"label":6,"fulltextRoute":80,"versionRead":81,"addedByCensus":82},"method",[64,65,66],"Han Wang","Chen Wang","Lihua Xie","IEEE Robotics and Automation Letters","journal","IEEE","6(2):1801-1807","10.1109\u002Flra.2021.3060392","2102.03800","https:\u002F\u002Fdoi.org\u002F10.1109\u002FLRA.2021.3060392","2021-02-07","metadata_verified","not_applicable",[11],false,"corrected","arXiv","arXiv v2 (2021-02-17), RA-L accepted preprint; IEEE version of record not read",true,[84,92,98,103,108,114],{"category":85,"model":86,"canonical":87,"role":88,"dataset":89,"specs":90,"locator":91},"lidar","Intel Realsense L515","Intel RealSense L515","method input",null,"solid-state, 30 Hz, 70 x 55 deg FoV, 0.07 deg horizontal and vertical resolution, 0.25-9 m range, 1.4 cm accuracy, 61 x 26 mm, 95 g","Table I; Sec. IV-A",{"category":93,"model":94,"canonical":94,"role":95,"dataset":89,"specs":96,"locator":97},"other","VICON system (motion capture)","reference or ground truth","ground truth in a 4 m x 4 m room","Sec. IV-A; Sec. IV-B",{"category":99,"model":100,"canonical":100,"role":88,"dataset":89,"specs":101,"locator":102},"platform","industrial AGV with a robot arm for gripping and transporting materials","solid-state LiDAR mounted at the front; maximum speed 0.8 m\u002Fs","Sec. IV-C; Fig. 4",{"category":104,"model":105,"canonical":105,"role":88,"dataset":89,"specs":106,"locator":107},"mobile_scanner_device","hand-held scanner (perception module, rotation platform, computing module)","less than 500 g; walked at normal speed indoors","Sec. IV-D; Fig. 5",{"category":109,"model":110,"canonical":110,"role":111,"dataset":89,"specs":112,"locator":113},"compute","Intel i7-8700","compute for runtime","6-core desktop CPU (VICON experiment)","Sec. IV-A",{"category":109,"model":115,"canonical":115,"role":111,"dataset":89,"specs":116,"locator":113},"Intel NUC with Intel i5-10210U","mini computer on the AGV and hand-held device",[],{"totalRows":119,"groupCount":120,"groups":121,"others":243},9,3,[122,180,214],{"slug":123,"group":124,"sourceId":5,"sourceLabel":6,"table":125,"selfRows":126,"metrics":127,"seqs":141,"entrants":151,"cells":156,"outcomes":172,"locators":173,"hardware":175,"wordings":177,"notes":178},"sslslam2021-text-sec-iv-c","sslslam2021:Text Sec. IV-C","Text Sec. IV-C",5,[128,132,135,137,139],{"label":129,"unit":130,"statistic":131,"alignment":76},"average computing time","ms","mean",{"label":133,"unit":134,"statistic":131,"alignment":76},"measured first dimension of operating machine (actual 1.15 m)","m",{"label":136,"unit":134,"statistic":131,"alignment":76},"measured second dimension of operating machine (actual 1.85 m)",{"label":138,"unit":134,"statistic":131,"alignment":76},"measured first dimension of operating machine (actual 1.16 m)",{"label":140,"unit":134,"statistic":131,"alignment":76},"measured second dimension of operating machine (actual 1.95 m)",[142,146,149],{"dataset":143,"sequence":144,"environment":145},"own warehouse AGV data","warehouse run","warehouse with operators and robots, AGV",{"dataset":143,"sequence":147,"environment":148},"operating machine in Fig. 4 (b)","warehouse, AGV",{"dataset":143,"sequence":150,"environment":148},"operating machine in Fig. 4 (d)",[152,154],{"name":153,"methodId":5,"linkable":82,"proposed":82,"self":82},"proposed method (SSL_SLAM)",{"name":155,"methodId":5,"linkable":82,"proposed":82,"self":82},"proposed method (SSL_SLAM) map",[157,161,164,167,169],[158,158,158,159,160,158,158,160,158],0,42,-1,[162,162,162,163,160,158,160,160,158],1,1.17,[162,165,162,166,160,158,160,160,158],2,1.88,[162,120,165,168,160,158,160,160,158],1.16,[162,170,165,171,160,158,160,160,158],4,1.94,[],[174],"Sec. IV-C",[176],"Intel NUC with i5-10210U",[],[179],"Warehouse AGV at up to 0.8 m\u002Fs; mapped machine size = average Euclidean distance between picked edge points; actual sizes: machine (b) 1.15 m x 1.85 m, machine (d) 1.16 m x 1.95 m",{"slug":181,"group":182,"sourceId":5,"sourceLabel":6,"table":183,"selfRows":165,"metrics":184,"seqs":191,"entrants":196,"cells":202,"outcomes":208,"locators":209,"hardware":210,"wordings":211,"notes":212},"sslslam2021-table-ii","sslslam2021:Table II","Table II",[185,189],{"label":186,"unit":187,"statistic":188,"alignment":76},"Success (count)","count","not_reported",{"label":190,"unit":187,"statistic":188,"alignment":76},"Tracking loss (count)",[192],{"dataset":193,"sequence":194,"environment":195},"own rotation test","rotation trials","hand-held sensor, indoor",[197,199],{"name":198,"methodId":5,"linkable":82,"proposed":82,"self":82},"The proposed method",{"name":200,"methodId":201,"linkable":82,"proposed":78,"self":78},"A-LOAM","aloam_software",[203,205,206,207],[158,158,158,204,160,158,160,160,158],6,[158,162,158,158,160,158,160,160,158],[162,158,158,162,160,158,160,160,158],[162,162,158,126,160,158,160,160,158],[],[183],[],[],[213],"Rotation test: L515 rotated randomly from horizontal at up to 1.57 rad\u002Fs and returned; tracking loss when final angle deviation exceeds 10 deg",{"slug":215,"group":216,"sourceId":5,"sourceLabel":6,"table":217,"selfRows":165,"metrics":218,"seqs":224,"entrants":229,"cells":231,"outcomes":235,"locators":236,"hardware":238,"wordings":240,"notes":241},"sslslam2021-text-sec-iv-b","sslslam2021:Text Sec. IV-B","Text Sec. IV-B",[219,222],{"label":220,"unit":221,"statistic":188,"alignment":188},"translational error (statistic not stated)","cm",{"label":223,"unit":130,"statistic":131,"alignment":76},"average computing time per frame",[225],{"dataset":226,"sequence":227,"environment":228},"own VICON room data","VICON room trial","indoor room, ground robot",[230],{"name":153,"methodId":5,"linkable":82,"proposed":82,"self":82},[232,233],[158,158,158,126,160,158,160,160,158],[158,162,158,234,160,158,158,160,158],31,[],[237],"Sec. IV-B",[239],"desktop PC, Intel 6-core i7-8700",[],[242],"Manually driven robot in a 4 m x 4 m VICON room; LOAM configured with the L515 angles and unchanged feature numbers",[],1790510655341]