[{"data":1,"prerenderedAt":187},["ShallowReactive",2],{"method-droeschel2018ctslam":3},{"method":4,"reference":55,"equipment":76,"figures":108,"results":109},{"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":27,"sensors":32,"platform":35,"estimator":38,"association":39,"timeModel":40,"deskew":41,"loopClosure":42,"globalOptimization":43,"mapRepresentation":44,"prior":45,"outputGeometry":46,"compute":47,"codeUrl":48,"codeLicense":49,"relatedVersions":50},"droeschel2018ctslam","Droeschel & Behnke, 2018","MRS continuous-time surfel SLAM (Droeschel and Behnke)","Efficient Continuous-Time SLAM for 3D Lidar-Based Online Mapping",2018,"recent","C04","full_slam_with_global_correction","這個方法延續作者的局部多解析度網格地圖：每個 3D 掃描以面元（surfel）配準到以機器人為中心的局部地圖，多個局部地圖再以面元配準連成全域位姿圖。新意在於把每個局部地圖內的掃描位姿建成子圖，形成階層式圖：當地圖累積更多資訊後，可挑選配準不確定性最大的掃描重新對齊並最佳化子圖，再更新上層位姿圖；子圖內以 SE(3) 三次 B 樣條表示連續時間軌跡，內插每條掃描線的位姿以修正掃描期間的運動畸變。作者以平均地圖熵量化點雲清晰度。","Hierarchical LiDAR SLAM built on local multiresolution surfel maps: scans in each local map form a sub-graph that can be re-aligned and re-optimized online when more data arrive, local maps form a g2o pose graph with loop closures, and a cubic B-spline in SE(3) per sub-graph interpolates scan-line poses; map crispness is quantified by mean map entropy.","full_text_reviewed","peer_reviewed_published","background","論文未在施工現場測試；多旋翼機在建築中庭沿建物立面飛行，另一組資料是背包在德意志博物館室內行走。它以平均地圖熵衡量點雲清晰度，而不是以獨立參考量測幾何精度；這類不需真值的點雲品質指標可作為施工點雲品質評估的參考，但這屬推論。",[20,21],"controlled_experiment","completed_building",[23,24,25,26],"Lowest mean map entropy on the Deutsches Museum subset among Cartographer, the authors' previous method and Nuechter et al. continuous-time refinement (Table I)","Refinement corrects misaligned scans that the previous method left in the courtyard map (Sec. V-A; Fig. 7)","Covariance-based scan selection converges faster than refinement without it (Sec. V-B; Fig. 8)","Local, allocentric and refinement stages run independently, allowing online mapping while earlier data are refined (Sec. III)",[28,29,30,31],"Evaluation is qualitative plus mean map entropy; no trajectory or map error against an independent reference (abstract; Sec. V)","Deutsches Museum comparison uses only a selected part of the dataset, following Nuechter et al. (Sec. V-B)","Only refinement iteration times are reported, not end-to-end online runtime (Sec. V-A)","Mean map entropy measures crispness and does not show absolute geometric accuracy (inference)",[33,34],"3D rotating multi-beam LiDAR (Velodyne VLP-16; two VLP-16 on the Deutsches Museum backpack)","optional IMU or wheel odometry used as registration prior and for motion during acquisition (Sec. III); the MAV carried an IMU measuring attitude (Sec. V-A)",[36,37],"UAV (DJI Matrice 600)","backpack (Deutsches Museum dataset)","hierarchical graph optimization in g2o: an allocentric pose graph of local multiresolution maps, a sub-graph of 3D scan poses per local map, and a continuous-time cubic B-spline over scan poses; sub-graphs are refined in parallel and global optimization is triggered by loop closures or when a sub-graph reference pose changes by more than 0.01 m or 1 degree (Sec. IV)","surfel-based registration of each 3D scan to a robot-centric local multiresolution grid map (surfel = sample mean and covariance of the points in a cell), and surfel-based map-to-map registration between local maps; scans with the largest entropy of registration covariance are selected for realignment (Sec. III; Sec. IV-A)","continuous-time: cumulative cubic B-spline in SE(3) with scan nodes as control points, used to interpolate the pose of each scan line (one VLP-16 data packet of 24 firing sequences) (Sec. IV-C; Sec. V-A)","motion during acquisition compensated with IMU or wheel odometry when available, then scan-line poses refined by B-spline interpolation within each sub-graph (Sec. III; Sec. IV-C; Fig. 4)","after each new local map one candidate map node is drawn with a probability that decays with distance and registered by surfel map-to-map alignment; on revisits, scans from neighbouring map nodes enlarge the local window (Sec. IV-B; Sec. IV-D)","pose graph over local maps optimized with g2o; changes are propagated to the sub-graphs and vice versa (Sec. IV; Sec. IV-D)","robot-centric local multiresolution grid maps that store measurements, occupancy (ray casting with an approximated 3D Bresenham) and surfels per cell; allocentric graph of local maps with a new map node every 5 m (Sec. III; Sec. V)","none","refined 3D point cloud map and trajectory (Figs. 5 and 7)","Intel Core i7-6700HQ at 2.6 GHz with 32 GB RAM; one refinement iteration takes 54 ms for a single map node and 380 ms for all 16 map nodes in parallel on the courtyard data, averaged over 10 runs (Sec. V; Sec. V-A)",null,"not_applicable (no public code found)",[51],{"relation":52,"title":53,"doi_or_url":54},"preprint","Efficient Continuous-time SLAM for 3D Lidar-based Online Mapping (arXiv v1, posted after the conference)","https:\u002F\u002Farxiv.org\u002Fabs\u002F1810.06802",{"id":5,"kind":56,"shortName":7,"title":8,"authors":57,"year":9,"venue":60,"venueType":61,"publisher":62,"volumeIssuePages":63,"doi":64,"arxivId":65,"url":66,"firstPublicDate":67,"publicationStatus":16,"metadataStatus":68,"fulltextStatus":15,"era":10,"classicReason":69,"codeUrl":48,"cluster":11,"topics":70,"mdpi":71,"verification":72,"label":6,"fulltextRoute":73,"versionRead":74,"addedByCensus":75},"method",[58,59],"David Droeschel","Sven Behnke","2018 IEEE International Conference on Robotics and Automation (ICRA)","conference","IEEE","pp. 5000-5007","10.1109\u002Ficra.2018.8461000","1810.06802","https:\u002F\u002Fdoi.org\u002F10.1109\u002FICRA.2018.8461000","2018-05","metadata_verified","not_applicable",[11],false,"confirmed","arXiv","arXiv v1 (2018-10-16), author version with ICRA 2018 DOI header; IEEE version of record not read",true,[77,84,89,93,99,102],{"category":78,"model":79,"canonical":79,"role":80,"dataset":81,"specs":82,"locator":83},"lidar","Velodyne VLP-16","method input","courtyard MAV flight (own data)","about 300,000 range measurements per second, 16 rings, 30 deg vertical field of view, 100 m maximum range, up to 1200 rpm; a scan line is one data packet of 24 firing sequences of 1.33 ms","Sec. IV; Sec. V-A",{"category":85,"model":86,"canonical":86,"role":80,"dataset":81,"specs":87,"locator":88},"imu","IMU measuring attitude (model not reported)","not_reported","Sec. V-A",{"category":90,"model":91,"canonical":91,"role":80,"dataset":81,"specs":92,"locator":88},"platform","DJI Matrice 600","MAV flown by a human operator along a building front at different heights; 2000 scans in 200 s",{"category":78,"model":94,"canonical":79,"role":95,"dataset":96,"specs":97,"locator":98},"Velodyne VLP-16 (two units)","dataset sensor","Deutsches Museum dataset (Google Cartographer team)","one mounted horizontally and one vertically on a backpack; calibration provided with the data and refined in the graph","Sec. V-B",{"category":90,"model":100,"canonical":100,"role":95,"dataset":96,"specs":101,"locator":98},"backpack (carried through the museum)","parts of the data contain moving persons",{"category":103,"model":104,"canonical":104,"role":105,"dataset":48,"specs":106,"locator":107},"compute","Intel Core i7-6700HQ","compute for runtime","quad-core at 2.6 GHz, 32 GB RAM","Sec. V",[],{"totalRows":110,"groupCount":111,"groups":112,"others":186},3,2,[113,148],{"slug":114,"group":115,"sourceId":5,"sourceLabel":6,"table":116,"selfRows":111,"metrics":117,"seqs":124,"entrants":128,"cells":133,"outcomes":141,"locators":142,"hardware":143,"wordings":145,"notes":146},"droeschel2018ctslam-text-sec-v-a","droeschel2018ctslam:Text Sec. V-A","Text Sec. V-A",[118,122],{"label":119,"unit":120,"statistic":121,"alignment":69},"runtime per iteration for refining a single map node","ms","mean",{"label":123,"unit":120,"statistic":121,"alignment":69},"runtime per iteration for refining all 16 map nodes in parallel",[125],{"dataset":81,"sequence":126,"environment":127},"2000 scans, 200 s","building courtyard, MAV",[129,131],{"name":130,"methodId":5,"linkable":75,"proposed":75,"self":75},"Ours (single map node)",{"name":132,"methodId":5,"linkable":75,"proposed":75,"self":75},"Ours (all 16 map nodes)",[134,138],[135,135,135,136,137,135,135,137,135],0,54,-1,[139,139,135,140,137,135,135,137,135],1,380,[],[88],[144],"Intel Core i7-6700HQ quad-core 2.6 GHz, 32 GB RAM",[],[147],"Courtyard MAV data (16 map nodes); refinement run as post-processing, average over 10 runs",{"slug":149,"group":150,"sourceId":5,"sourceLabel":6,"table":151,"selfRows":139,"metrics":152,"seqs":156,"entrants":161,"cells":171,"outcomes":180,"locators":181,"hardware":182,"wordings":183,"notes":184},"droeschel2018ctslam-table-i","droeschel2018ctslam:Table I","Table I",[153],{"label":154,"unit":155,"statistic":121,"alignment":69},"mean map entropy (MME)","unitless (entropy)",[157],{"dataset":158,"sequence":159,"environment":160},"Deutsches Museum (Cartographer dataset)","selected part (as in [27])","indoor museum, backpack",[162,165,167,169],{"name":163,"methodId":164,"linkable":75,"proposed":71,"self":71},"Cartographer [26]","cartographer2016",{"name":166,"methodId":48,"linkable":71,"proposed":71,"self":71},"Droeschel et al. [8] (previous method)",{"name":168,"methodId":48,"linkable":71,"proposed":71,"self":71},"Nuechter et al. [27]",{"name":170,"methodId":5,"linkable":75,"proposed":75,"self":75},"Ours",[172,174,176,178],[135,135,135,173,137,135,137,137,135],-2.04,[139,135,135,175,137,135,137,137,135],-2.12,[111,135,135,177,137,135,137,137,135],-2.34,[110,135,135,179,137,135,137,137,135],-2.42,[],[151],[],[],[185],"Best mean map entropy (MME, radius 0.5 m, lower is better) on a selected part of the Deutsches Museum backpack dataset, following Nuechter et al. [27]",[],1790510660708]