[{"data":1,"prerenderedAt":173},["ShallowReactive",2],{"method-thrun2000_3dmapping":3},{"method":4,"reference":51,"equipment":72,"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":21,"limitations":26,"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},"thrun2000_3dmapping","Thrun et al., 2000","Thrun-Burgard-Fox real-time 2D and 3D laser mapping","A real-time algorithm for mobile robot mapping with applications to multi-robot and 3D mapping",2000,"classic","C01","full_slam_with_global_correction","本文把增量式雷射掃描匹配與以樣本表示的位姿後驗結合：後驗的計算方式與蒙地卡羅定位相同，每次掃描以多個樣本作為爬山搜尋的起點，找到最可能的位姿後把掃描加入地圖。當後驗推得的位姿與單純增量估計不一致時，系統判定發生迴圈閉合，先把位姿差按比例分配到迴圈內各位姿，再以梯度下降反覆修正，於兩次量測之間完成反向校正。同一後驗機制讓第二台機器人先在領隊機器人的地圖中全域定位，再共同建圖；另以向前雷射做 2D 定位、向上雷射擷取 3D 資料，產生經多邊形簡化的精簡建物 3D 模型。","Real-time incremental 2D laser mapping that combines maximum-likelihood scan alignment with a sample-based pose posterior for loop detection and backward correction, extended to multi-robot mapping and to compact 3D building models from an upward-pointing laser.","full_text_reviewed","peer_reviewed_published","background","未在營建場域驗證。以水平雷射定位、向上雷射累積 3D 資料並簡化成多邊形模型，是早期以移動平台建立建物室內 3D 模型的做法，作者說明使用者可在 VR 工具中飛越模型遠端檢視建物（Sec. 3.5）。3D 點位精度完全取決於 2D 位姿估計，對工程級竣工量測的適用性未評估（推論）。Surmann 等 [surmann2003_kurt3d] 把本文列為以水平加垂直雙雷射取得 3D 資料的代表；Hähnel 等 [hahnel2003_compact3d] 的 2D 掃描對齊延伸自本文，並指出本文模型的多邊形數與原始掃描數相近（該文 Sec. 1、2.1）。",[20],"completed_building",[22,23,24,25],"Maps of cyclic environments built in real time on a low-end PC, with occasional injected odometry errors of 30 degrees or 1 m (Sec. 3.1).","The same cycle was mapped with odometry removed, with a final result optically equivalent to the odometry-based map (Sec. 3.2; Fig. 8).","The authors report never obtaining a single wrong result with backward correction, which runs between two sensor measurements (Sec. 2.4).","A roughly 60 m cyclic 3D map was reduced from 82,899 to 8,289 polygons with similar appearance and about an order of magnitude faster rendering (Sec. 3.5).",[27,28,29,30,31],"Odometry-free mapping works only with sufficient environmental variation and would fail in a long featureless corridor (Sec. 3.2).","With the Urban Robot's very poor odometry some walls were rotated by about 2 degrees (Sec. 3.3).","Real-time operation comes at the price of increased brittleness compared with EM (Sec. 4).","Multi-robot mapping assumes every robot starts within the team leader's map (Sec. 2.5).","(inference) Map accuracy is judged visually; no metric error against an independent reference is reported.",[33,34],"2D laser range finders: forward-looking for 2D mapping and localization, upward-pointed for 3D (model not reported)","wheel odometry (optional; also run with odometry removed)",[36,37],"wheeled UGV (Pioneer, RWI B21, Nomad Scout)","tracked UGV (Urban Robot)","incremental maximum-likelihood scan alignment by gradient ascent, combined with a sample-based posterior over the current pose computed like Monte Carlo localization; every sample seeds a hill-climbing search (Sec. 2.2-2.3)","no explicit correspondences: perceptual likelihood penalizes obstacles in space previously seen as free (ray-traced), with a map made of past scans and poses (Sec. 2.1)","discrete poses (scans appended every 2 m; all scans used for localization)","not_reported","detected when the posterior-based pose differs from the incremental maximum-likelihood pose; the loop is identified from the scan that caused the adjustment (Sec. 2.4)","backward correction over the loop only: the pose difference is distributed proportionally over the loop poses, then gradient descent is iterated over them (Sec. 2.4); no full batch optimization","collection of 2D scans with poses; 3D model as polygons from the upward laser, outlier-filtered and simplified with a computer-graphics polygon simplification (Sec. 2.6)","none (for multi-robot mapping each robot must start inside the team leader's map)","2D scan map and a simplified 3D polygonal model viewed in a standard virtual reality tool (VRweb) (Sec. 3.5)","real time on a low-end PC; more than 1,000 gradient computations per second (Sec. 2.1; Sec. 3.1)",null,"not_applicable",[],{"id":5,"kind":52,"shortName":7,"title":8,"authors":53,"year":9,"venue":57,"venueType":58,"publisher":59,"volumeIssuePages":60,"doi":61,"arxivId":48,"url":62,"firstPublicDate":63,"publicationStatus":16,"metadataStatus":64,"fulltextStatus":15,"era":10,"classicReason":65,"codeUrl":48,"cluster":11,"topics":66,"mdpi":67,"verification":68,"label":6,"fulltextRoute":69,"versionRead":70,"addedByCensus":71},"method",[54,55,56],"Sebastian Thrun","Wolfram Burgard","Dieter Fox","Proceedings 2000 IEEE International Conference on Robotics and Automation (ICRA 2000), San Francisco, CA","conference","IEEE","vol. 1, pp. 321-328","10.1109\u002Frobot.2000.844077","https:\u002F\u002Fdoi.org\u002F10.1109\u002FROBOT.2000.844077","2000-04","metadata_verified","principle reused: real-time incremental laser mapping with a sample-based pose posterior for loop detection and backward correction, plus the horizontal-plus-upward dual-laser scheme for 3D building capture; Surmann et al. [surmann2003_kurt3d] cite it for that scheme, and Hähnel et al. [hahnel2003_compact3d] extend its 2D scan alignment and contrast their compact models with its polygon-heavy ones.",[11],false,"corrected","NTU institutional (curl)","IEEE Xplore version of record (scanned PDF with OCR text layer, 8 pp.)",true,[73,79,83,86,88],{"category":74,"model":75,"canonical":75,"role":76,"dataset":48,"specs":77,"locator":78},"platform","Pioneer","method input","robots used for multi-robot mapping; one Pioneer carries two laser range finders for 3D mapping","Fig. 1 caption",{"category":74,"model":80,"canonical":80,"role":76,"dataset":48,"specs":81,"locator":82},"Urban Robot","tracked skid-steering robot for indoor and outdoor exploration with extremely poor odometry","Fig. 1 caption; Sec. 3.3",{"category":74,"model":84,"canonical":84,"role":76,"dataset":48,"specs":41,"locator":85},"RWI B21","Sec. 1",{"category":74,"model":87,"canonical":87,"role":76,"dataset":48,"specs":41,"locator":85},"Nomad Scout",{"category":89,"model":90,"canonical":90,"role":76,"dataset":48,"specs":91,"locator":92},"lidar","2D laser range finders, forward-looking and upward-pointed (model not reported)","two lasers on the 3D-mapping Pioneer: the forward-looking one for 2D mapping and localization, the upward-pointed one for 3D data","Sec. 2.6; Fig. 1b caption",[],{"totalRows":95,"groupCount":96,"groups":97,"others":172},3,2,[98,140],{"slug":99,"group":100,"sourceId":5,"sourceLabel":6,"table":101,"selfRows":96,"metrics":102,"seqs":110,"entrants":118,"cells":123,"outcomes":130,"locators":132,"hardware":135,"wordings":136,"notes":137},"thrun2000-3dmapping-text-sec-3-3-3-5","thrun2000_3dmapping:Text Sec. 3.3, 3.5","Text Sec. 3.3, 3.5",[103,107],{"label":104,"unit":105,"statistic":41,"alignment":106},"number of polygons of the simplified model","polygons","none",{"label":108,"unit":109,"statistic":41,"alignment":106},"rotation of walls that are not aligned perfectly (approximately)","deg",[111,115],{"dataset":112,"sequence":113,"environment":114},"authors' dual-laser Pioneer data","cyclic indoor map (about 60 m)","indoor building",{"dataset":116,"sequence":41,"environment":117},"DARPA demonstration run (Urban Robot)","not_reported (DARPA demonstration; site type not stated)",[119,121],{"name":120,"methodId":5,"linkable":71,"proposed":71,"self":71},"simplified polygonal model",{"name":122,"methodId":5,"linkable":71,"proposed":71,"self":71},"proposed incremental mapping",[124,128],[125,125,125,126,127,125,127,127,125],0,8289,-1,[129,129,129,96,125,129,127,127,129],1,[131],"approximate",[133,134],"Sec. 3.5; Fig. 11","Sec. 3.3; Fig. 9",[],[],[138,139],"Same map after polygon simplification (Garland-Heckbert fusion); appearance similar and rendering about an order of magnitude faster","Autonomous exploration with the Urban Robot (odometry errors often as large as 100%); authors call it the worst map generated with their approach",{"slug":141,"group":142,"sourceId":143,"sourceLabel":144,"table":145,"selfRows":129,"metrics":146,"seqs":149,"entrants":154,"cells":159,"outcomes":162,"locators":165,"hardware":167,"wordings":168,"notes":169},"hahnel2003-gridfastslam-text-sec-iv","hahnel2003_gridfastslam:Text Sec. IV","hahnel2003_gridfastslam","Hähnel et al., 2003a","Text Sec. IV",[147],{"label":148,"unit":49,"statistic":41,"alignment":106},"loop closure outcome",[150],{"dataset":151,"sequence":152,"environment":153},"B21r simulator, Wean Hall","simulated run","simulation",[155,157],{"name":156,"methodId":5,"linkable":71,"proposed":67,"self":71},"particle filter strategy of Thrun et al. [20], [19] (single map)",{"name":158,"methodId":143,"linkable":71,"proposed":71,"self":67},"proposed RBPF with scan-matching-corrected odometry",[160,161],[125,125,125,48,125,125,127,127,125],[129,125,125,48,129,125,127,127,129],[163,164],"failed: inconsistencies after closing the loop","consistent map",[166],"Sec. IV.B; Fig. 10",[],[],[170,171],"Simulated Wean Hall (32 m x 10 m, 251 m, noise added); single-map posterior approach keeping only the best particle at loop closure","Simulated Wean Hall, proposed method",[],1790510661962]