[{"data":1,"prerenderedAt":193},["ShallowReactive",2],{"method-hahnel2003_compact3d":3},{"method":4,"reference":53,"equipment":74,"figures":89,"results":90},{"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":31,"platform":34,"estimator":37,"association":38,"timeModel":39,"deskew":40,"loopClosure":41,"globalOptimization":41,"mapRepresentation":42,"prior":43,"outputGeometry":44,"compute":45,"codeUrl":46,"codeLicense":47,"relatedVersions":48},"hahnel2003_compact3d","Hähnel et al., 2003b","Compact 3D building models from mobile laser scanning","Learning compact 3D models of indoor and outdoor environments with a mobile robot",2003,"classic","C01","map_representation_or_reconstruction","本文以移動機器人上的雷射測距儀建立室內外建物的精簡 3D 模型。室內機器人以水平雷射做 2D 掃描匹配求位姿，同時以朝上的雷射掃出 3D 結構；室外機器人則以裝在雲台上的單一雷射取得 3D 掃描，並以射線式機率模型對三角網格做 3D 配準。取得的網格全域一致但局部雜訊大，作者以隨機起點的區域成長找出大型平面，把點投影到平面後合併共面多邊形，同時保留門窗等非平面細節；為加速，先由各掃描抽出的線段角度直方圖產生平面候選，再以粗細兩階段的平面掃掠篩選。","Mobile laser 3D modelling that estimates poses by 2D and probabilistic 3D scan matching and then replaces noisy meshes with large planar polygons found by randomized region growing and histogram-guided plane sweeps.","full_text_reviewed","peer_reviewed_published","background","未在營建工地驗證；資料來自 CMU Wean Hall、UW Sieg Hall 走廊與 Freiburg 校園約 40 m 乘 60 m 的建物外部。作者提到建築師與建物管理者可用 3D 模型做設計與使用研究（Sec. 1）。以平面擬合把雜訊網格簡化為牆、天花板與門的平面多邊形，是早期由行動雷射資料產生竣工建物模型的做法；ScienceDirect 的被引用清單也列出 Pătrăucean 等的竣工建模綜述 [patraucean2015asbuiltmodelling]（推論）。",[20],"completed_building",[22,23,24,25],"Reduction ratios between 100:14.2 and 100:4.1 over three data sets (Table 2).","At the same polygon count as QSlim the model kept doors and planar walls that QSlim lost (Sec. 4; Fig. 10).","Histogram-guided plane search gives more than an order of magnitude speed-up over naive plane extraction (Sec. 3.3).","The probabilistic 3D scan matcher uses max-range beams and handles occlusion without special heuristics; the authors observed cases where ICP diverged but their matcher did not (Sec. 2.2; Sec. 5).",[27,28,29,30],"The model is limited to flat surfaces; measurements of non-flat objects are not corrected, so the model remains fairly complex (Sec. 6).","No exploration strategy for full 3D acquisition (Sec. 6).","Most of the mesh ruggedness comes from laser measurement noise, with some residual pose error (Sec. 3).","(inference) Model fidelity is judged visually and by polygon counts; no comparison with surveyed geometry.",[32,33],"2D laser range finders: indoors a horizontal laser for 2D mapping plus an upward-pointing laser for 3D (SICK PLS used in the Wean Hall run; SICK LMS also named); outdoors one laser on a pan\u002Ftilt unit","odometry",[35,36],"wheeled UGV (indoor robot with two lasers)","wheeled UGV (outdoor robot Herbert with a pan\u002Ftilt laser)","incremental maximum-likelihood pose estimation by hill climbing: 2D scan-to-grid alignment that integrates small Gaussian pose errors; for 3D scans a beam likelihood (Gaussian plus uniform mixture approximated by triangular distributions) against a triangle-mesh model via ray tracing (Sec. 2.1-2.2)","no explicit correspondences; beams are ray-cast into the current grid (2D) or triangle mesh (3D), and max-range beams also contribute (Sec. 2.2)","discrete poses","not_reported (indoor robots moved at 10 cm\u002Fs to obtain adequate 3D point density)","none","triangle mesh from neighbouring scan points, simplified into planar polygons by randomized region-growing plane fitting (delta 30 cm, epsilon 2.8, gamma 10 cm) and merging of coplanar neighbouring polygons; plane candidates found from line-angle histograms and plane sweeps at 5 cm then 1 cm (Sec. 3)","none (built structures assumed to contain large flat surfaces)","compact 3D polygonal model: large planar polygons and quads plus residual triangles for non-planar regions (Table 2)","Sieg Hall (1,933,018 points): 51 min 42 s plane extraction and 9 min 43 s polygon merging (Table 2); a naive plane extraction on 200,000 surfaces took over 10 h on a standard PC (Sec. 3.3)",null,"not_applicable",[49],{"relation":50,"title":51,"doi_or_url":52},"earlier_version","Workshop version in Proceedings of the Fourth European Workshop on Advanced Mobile Robots (EUROBOT'01), Lund, September 2001, as cited by Surmann et al. 2003 ref. [2] (not read)","not_verified",{"id":5,"kind":54,"shortName":7,"title":8,"authors":55,"year":9,"venue":59,"venueType":60,"publisher":61,"volumeIssuePages":62,"doi":63,"arxivId":46,"url":64,"firstPublicDate":65,"publicationStatus":16,"metadataStatus":66,"fulltextStatus":15,"era":10,"classicReason":67,"codeUrl":46,"cluster":11,"topics":68,"mdpi":69,"verification":70,"label":6,"fulltextRoute":71,"versionRead":72,"addedByCensus":73},"method",[56,57,58],"Dirk Hähnel","Wolfram Burgard","Sebastian Thrun","Robotics and Autonomous Systems (special issue: Best Papers of the Eurobot '01 Workshop)","journal","Elsevier","44(1):15-27","10.1016\u002Fs0921-8890(03)00007-1","https:\u002F\u002Fdoi.org\u002F10.1016\u002FS0921-8890(03)00007-1","2003-07","metadata_verified","principle reused: early automated simplification of mobile-laser building data into planar walls, ceilings and doors with reported reduction ratios (Table 2), preceded by 2D and probabilistic 3D scan matching; an early precedent for as-built modelling from mobile laser data.",[11],false,"corrected","NTU institutional (Chrome)","ScienceDirect HTML full text of the version of record (Robotics and Autonomous Systems 44(1):15-27)",true,[75,81,84],{"category":76,"model":77,"canonical":77,"role":78,"dataset":46,"specs":79,"locator":80},"lidar","SICK PLS","method input","measurement error below 20 cm; range resolution 1 cm","Sec. 4",{"category":76,"model":82,"canonical":82,"role":78,"dataset":46,"specs":83,"locator":80},"SICK LMS","measurement error below 5 cm; range resolution 1 cm",{"category":85,"model":86,"canonical":86,"role":78,"dataset":46,"specs":87,"locator":88},"platform","Herbert","outdoor robot with one laser on a pan\u002Ftilt unit, angular resolution 0.25 deg","Sec. 2.3; Sec. 4; Fig. 4",[],{"totalRows":91,"groupCount":92,"groups":93,"others":192},18,1,[94],{"slug":95,"group":96,"sourceId":5,"sourceLabel":6,"table":97,"selfRows":91,"metrics":98,"seqs":117,"entrants":129,"cells":132,"outcomes":177,"locators":178,"hardware":179,"wordings":180,"notes":190},"hahnel2003-compact3d-table-2","hahnel2003_compact3d:Table 2","Table 2",[99,103,105,108,111,114],{"label":100,"unit":101,"statistic":102,"alignment":41},"Time (min) plane extraction (","s","not_reported",{"label":104,"unit":101,"statistic":102,"alignment":41},"Time (min) polygon merging (",{"label":106,"unit":107,"statistic":102,"alignment":41},"Reduced model number of polygons","polygons",{"label":109,"unit":110,"statistic":102,"alignment":41},"Reduced model number of quads","quads",{"label":112,"unit":113,"statistic":102,"alignment":41},"Reduced model number of triangles","triangles",{"label":115,"unit":116,"statistic":102,"alignment":41},"Reduction ratio (","% (100:x)",[118,122,125],{"dataset":119,"sequence":120,"environment":121},"CMU Wean Hall corridor (10 m traveled, SICK PLS)","Wean Hall","indoor corridor",{"dataset":123,"sequence":124,"environment":121},"UW Sieg Hall corridor","Sieg Hall",{"dataset":126,"sequence":127,"environment":128},"University of Freiburg campus buildings (40 m x 60 m, robot Herbert)","Campus","outdoor building exteriors",[130],{"name":131,"methodId":5,"linkable":73,"proposed":73,"self":73},"planar approximation and polygon merging (proposed)",[133,137,139,142,145,148,151,153,155,157,159,161,162,164,166,168,171,174],[134,134,134,135,136,134,136,134,134],0,376,-1,[134,134,92,138,136,134,136,92,134],3102,[134,134,140,141,136,134,136,140,134],2,434,[134,92,134,143,136,134,136,144,134],20,3,[134,92,92,146,136,134,136,147,134],583,4,[134,92,140,149,136,134,136,150,134],48,5,[134,140,134,152,136,134,136,136,134],2626,[134,140,92,154,136,134,136,136,134],2471,[134,140,140,156,136,134,136,136,134],255,[134,144,134,158,136,134,136,136,134],987,[134,144,92,160,136,134,136,136,134],242,[134,144,140,91,136,134,136,136,134],[134,147,134,163,136,134,136,136,134],34227,[134,147,92,165,136,134,136,136,134],151624,[134,147,140,167,136,134,136,136,134],33312,[134,150,134,169,136,134,136,170,134],14.2,6,[134,150,92,172,136,134,136,173,134],4.1,7,[134,150,140,175,136,134,136,176,134],8.9,8,[],[97],[],[181,182,183,184,185,186,187,188,189],"Time (min) plane extraction (as written 6:16)","Time (min) plane extraction (as written 51:42)","Time (min) plane extraction (as written 7:14)","Time (min) polygon merging (as written 0:20)","Time (min) polygon merging (as written 9:43)","Time (min) polygon merging (as written 0:48)","Reduction ratio (as written 100:14.2)","Reduction ratio (as written 100:4.1)","Reduction ratio (as written 100:8.9)",[191],"Statistics of the planar simplification for three data sets; times given as min:s in the paper and converted to seconds here; reduction ratio given as 100:x and stored as x",[],1790510661103]