[{"data":1,"prerenderedAt":189},["ShallowReactive",2],{"method-surmann2003_kurt3d":3},{"method":4,"reference":54,"equipment":75,"figures":102,"results":103},{"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":35,"estimator":37,"association":38,"timeModel":39,"deskew":40,"loopClosure":41,"globalOptimization":42,"mapRepresentation":43,"prior":44,"outputGeometry":45,"compute":46,"codeUrl":47,"codeLicense":48,"relatedVersions":49},"surmann2003_kurt3d","Surmann et al., 2003","AIS 3D laser robot for indoor digitalization","An autonomous mobile robot with a 3D laser range finder for 3D exploration and digitalization of indoor environments",2003,"classic","C01","full_slam_with_global_correction","本文提出一套不需人工介入的室內 3D 數位化系統。Ariadne 輪式機器人上裝有以伺服馬達俯仰轉動 2D 雷射而成的 AIS 3D 雷射測距儀，停車後掃描水平 180 度、垂直 120 度的範圍。各次 3D 掃描以里程計為初值，用加上 k-d 樹與縮減點的 ICP 做六自由度配準，再以「同步匹配」把每筆掃描對所有重疊的鄰近掃描重新配準，直到不再移動，以分散累積誤差。下一個掃描位置由近似藝廊問題的最佳視點規劃決定，機器人再以全域穩定的馬達控制器與 3D 物體外框避開桌面等突出障礙物，最後輸出 DXF、VRML 與八元樹網格。","Autonomous stop-scan-go indoor 3D digitalization: a servo-pitched 2D laser gives 180 x 120 deg scans that are registered by fast ICP and globally by simultaneous matching, with next-best-view planning and 3D obstacle-aware navigation.","full_text_reviewed","peer_reviewed_published","background","未在營建工地驗證；實驗在 GMD Robobench 辦公走廊與設有樓梯、電梯的入口大廳。作者把設施管理、建築、隧道與礦坑的興建和維護列為需求來源，並指出潛在應用包括現場調查、結構工程、建物修復、露天與地下礦業、變形監測（Sec. 1、7）。停車掃描後以 ICP 與同步匹配配準、再規劃下一站的流程，與今日以定點掃描儀加自動配準及站位規劃的工地掃描流程相近（推論）。",[20],"completed_building",[22,23,24,25],"Reduced points plus k-d trees cut ICP time for two scans from 3 h 47 min (all points, brute force) to under 1.4 s on a Pentium-III-800 (Table 1).","Simultaneous matching of 20 scans reconstructed the corridor consistently, whereas pairwise and incremental matching accumulated errors (Sec. 3.2; Fig. 4).","Bounding boxes from 3D scans let the robot plan around obstacles with jutting-out edges such as tables, which standard sensors often miss (Sec. 5.2).","The whole system runs without human intervention and addresses exploration, registration and navigation together (Sec. 7).",[27,28,29,30],"Points of dynamic objects are not identified or removed; the robot simply repeats the scan when other sensors detect motion (Sec. 3.4).","Edge-point feature matching was insufficient in simple office corridors dominated by floor, ceiling and walls (Sec. 3.3).","The kidnapped-robot problem is not addressed (Sec. 7).","(inference) Results are qualitative; no accuracy against an independent survey is reported.",[32,33,34],"AIS 3D laser range finder: a 2D laser range finder pitched by a servo, 180 deg (h) x 120 deg (v), with reflectance (2D scanner model not reported)","wheel encoders (odometry)","two 2D safety laser scanners used as bumpers and for dynamic collision avoidance",[36],"wheeled UGV (Ariadne, industrial DTV)","ICP with Horn's quaternion closed form, k-d trees and reduced points, initialized by odometry; 'simultaneous matching' re-registers every scan against the union of its overlapping neighbours through a queue until no scan moves, distributing the global error (after Pulli) (Sec. 3.1-3.2)","closest-point correspondences on reduced points; two scans overlap if more than 250 corresponding point pairs exist (Sec. 3.2)","discrete poses (stop, scan, plan and go; one 6-DoF pose per 3D scan)","not_applicable (the robot stands still during each 3D scan; a 181 x 256 scan takes 3.4 s)","no explicit detection; revisits are handled through neighbour overlap in simultaneous matching","simultaneous matching: iterative queue-based re-registration of all overlapping scans (Sec. 3.2)","registered 3D point clouds; octree for visualization and meshing; horizontal-slice polygons with seen and unseen edges for planning; object bounding boxes (Sec. 4, 5.2, 6.1)","none","2D point and line map, 3D volumetric model in DXF and VRML, 3D grid for an OpenGL viewer, octree-based mesh (Sec. 6)","Pentium-III-800 MHz with 384 MB RAM and real-time Linux on the robot; ICP of two scans under 1.4 s with reduced points and k-d tree (Table 1); next-best-view planning up to 2 s on 20 m x 30 m scenes (Sec. 4.3)",null,"not_applicable (no code linked in the paper)",[50],{"relation":51,"title":52,"doi_or_url":53},"repository_record","Fraunhofer publica record listed by OpenAlex (not read)","https:\u002F\u002Fpublica.fraunhofer.de\u002Fhandle\u002Fpublica\u002F203887",{"id":5,"kind":55,"shortName":7,"title":8,"authors":56,"year":9,"venue":60,"venueType":61,"publisher":62,"volumeIssuePages":63,"doi":64,"arxivId":47,"url":65,"firstPublicDate":66,"publicationStatus":16,"metadataStatus":67,"fulltextStatus":15,"era":10,"classicReason":68,"codeUrl":47,"cluster":11,"topics":69,"mdpi":70,"verification":71,"label":6,"fulltextRoute":72,"versionRead":73,"addedByCensus":74},"method",[57,58,59],"Hartmut Surmann","Andreas Nüchter","Joachim Hertzberg","Robotics and Autonomous Systems","journal","Elsevier","45(3-4):181-198","10.1016\u002Fj.robot.2003.09.004","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.robot.2003.09.004","2003-12","metadata_verified","necessary technical node: autonomous stop-scan-go indoor 3D digitalization with a servo-pitched 2D laser, fast ICP and a global 'simultaneous matching' of all overlapping scans, with next-best-view planning; the authors name facility management, architecture, site survey and structural engineering as target uses, and Cole and Newman [cole_newman2006_3dslam] contrast it as the stop-acquire-move approach.",[11],false,"confirmed","NTU institutional (Chrome)","ScienceDirect HTML full text of the version of record (Robotics and Autonomous Systems 45(3-4):181-198)",true,[76,82,87,92,98],{"category":77,"model":78,"canonical":78,"role":79,"dataset":47,"specs":80,"locator":81},"platform","Ariadne robot","method input","industrial DTV, about 80 cm x 60 cm, 90 cm high, payload 200 kg, up to 0.8 m\u002Fs, 250 kg, about 8 h per battery charge","Sec. 2.1",{"category":83,"model":84,"canonical":84,"role":79,"dataset":47,"specs":85,"locator":86},"mobile_scanner_device","AIS 3D laser range finder","2D laser range finder on a servo-driven pitch mount; 180 deg (h) x 120 deg (v); horizontal 181, 361 or 721 and vertical 128 or 256 points; 181 x 256 scan in 3.4 s; reflectance measured; scanner 17 W, servo 0.85 W","Sec. 2.2",{"category":88,"model":89,"canonical":89,"role":79,"dataset":47,"specs":90,"locator":91},"lidar","2D safety laser scanners","two units, front and rear, 180 deg horizontal plane each; used as bumper substitutes and for dynamic collision avoidance","Sec. 2.1; Sec. 5.3",{"category":93,"model":94,"canonical":94,"role":95,"dataset":47,"specs":96,"locator":97},"compute","Pentium-III-800 MHz","compute for runtime","384 MB RAM, real-time Linux","Sec. 2.1; Table 1",{"category":93,"model":99,"canonical":99,"role":95,"dataset":47,"specs":100,"locator":101},"Pentium-III-600","offline polygon creation and object segmentation need around 1 s per typical indoor scene","Sec. 2.2.1",[],{"totalRows":104,"groupCount":105,"groups":106,"others":188},9,2,[107,164],{"slug":108,"group":109,"sourceId":5,"sourceLabel":6,"table":110,"selfRows":111,"metrics":112,"seqs":120,"entrants":125,"cells":134,"outcomes":153,"locators":155,"hardware":156,"wordings":157,"notes":162},"surmann2003-kurt3d-table-1","surmann2003_kurt3d:Table 1","Table 1",8,[113,117],{"label":114,"unit":115,"statistic":116,"alignment":44},"computing time for scan matching (","s","not_reported",{"label":118,"unit":119,"statistic":116,"alignment":44},"number of ICP iterations","iterations",[121],{"dataset":122,"sequence":123,"environment":124},"GMD Robobench (two scans)","scan pair of Fig. 3","indoor office corridor",[126,128,130,132],{"name":127,"methodId":5,"linkable":74,"proposed":70,"self":74},"All points and brute force search",{"name":129,"methodId":5,"linkable":74,"proposed":70,"self":74},"Reduced points and brute force search",{"name":131,"methodId":5,"linkable":74,"proposed":70,"self":74},"All points and kD-tree",{"name":133,"methodId":5,"linkable":74,"proposed":74,"self":74},"Reduced points and kD-tree",[135,139,142,144,146,148,149,152],[136,136,136,137,138,136,136,136,136],0,13620,-1,[136,140,136,141,138,136,136,138,136],1,27,[140,136,136,143,138,136,136,140,136],186,[140,140,136,145,138,136,136,138,136],25,[105,136,136,147,138,136,136,105,136],6,[105,140,136,141,138,136,136,138,136],[150,136,136,151,136,136,136,150,136],3,1.4,[150,140,136,145,138,136,136,138,136],[154],"upper bound (\u003C1.4 s)",[110],[94],[158,159,160,161],"computing time for scan matching (as written: 3 h, 47 min)","computing time for scan matching (as written: 3 min, 6 s)","computing time for scan matching (as written: 6 s)","computing time for scan matching (as written: \u003C1.4 s)",[163],"Computing time for matching two 3D scans of the GMD Robobench (46,336 points; 4,910 reduced points) on a Pentium-III-800, odometry initialization",{"slug":165,"group":166,"sourceId":5,"sourceLabel":6,"table":167,"selfRows":140,"metrics":168,"seqs":172,"entrants":175,"cells":178,"outcomes":180,"locators":182,"hardware":184,"wordings":185,"notes":186},"surmann2003-kurt3d-text-sec-4-3","surmann2003_kurt3d:Text Sec. 4.3","Text Sec. 4.3",[169],{"label":170,"unit":115,"statistic":171,"alignment":44},"planning time (up to)","max",[173],{"dataset":116,"sequence":116,"environment":174},"indoor",[176],{"name":177,"methodId":5,"linkable":74,"proposed":74,"self":74},"next best view planner",[179],[136,136,136,105,136,136,136,138,136],[181],"upper bound",[183],"Sec. 4.3",[94],[],[187],"Whole next-best-view planning algorithm on scenes of 20 m x 30 m",[],1790510661048]