[{"data":1,"prerenderedAt":260},["ShallowReactive",2],{"method-nuchter2007_6dslam":3},{"method":4,"reference":59,"equipment":82,"figures":118,"results":119},{"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":24,"limitations":31,"sensors":38,"platform":41,"estimator":43,"association":44,"timeModel":45,"deskew":46,"loopClosure":47,"globalOptimization":48,"mapRepresentation":49,"prior":50,"outputGeometry":51,"compute":52,"codeUrl":53,"codeLicense":54,"relatedVersions":55},"nuchter2007_6dslam","Nüchter et al., 2007","6D SLAM (Kurt3D, stop-scan-go ICP SLAM)","6D SLAM, 3D mapping outdoor environments",2007,"classic","C04","full_slam_with_global_correction","本文提出以三維雷射掃描為基礎的 6D SLAM（六自由度同時定位與建圖）：機器人以停下、掃描、再前進（stop-scan-go）的方式取得每一幅三維點雲，先把輪式里程計外推為六自由度初值，再以八元樹（octree）由粗到細搜尋初始對齊，之後用 ICP（Iterative Closest Point）逐幅配準（registration）。偵測到迴圈時，把閉合誤差依行經路徑長度比例分攤給迴圈內各幅掃描；資料收集完成後，再以同步配準（simultaneous matching）式的全域鬆弛（global relaxation）反覆將每幅掃描對其重疊鄰居重新配準。為壓低計算量，作者使用點數縮減、近似 k-d tree 與快取 k-d tree（cached k-d tree）搜尋。整套系統只維持單一位姿假設，未使用機率式不確定性表示。","A stop-scan-go 6-DoF SLAM system that registers 3D laser scans with ICP (octree-based initial guess, fast k-d tree variants), distributes loop-closing error along the path, and refines the whole map by offline neighbour-wise global relaxation without probabilistic uncertainty.","full_text_reviewed","peer_reviewed_published","background","作者在引言列舉建築（architecture）、隧道施工與維護、工廠設計、設施管理、都市與區域規劃等可能應用，並提到同組先前以 6D SLAM 測繪廢棄地下礦坑的工作（Sec. 1，Nüchter et al. 2004）。本文實驗則在 Schloss Dagstuhl 會議中心（84 幅掃描、240 m 閉合迴圈，Sec. 5.2）、Birlinghoven 研究室建築室內外（32 幅掃描，含 1.05 m 高差坡道，Sec. 5.3）、Birlinghoven 園區戶外（77 幅掃描，Sec. 5.4）與 RoboCup Rescue 競賽場地（Sec. 5.5）進行，另提及 ELROB 試驗；均不是營建工地、隧道或基礎設施的驗證。精度只以未校正航照的距離比例（Table II）與一段捲尺量測（Fig. 13）比對，作者自稱為粗略（sketchy）比較（Sec. 5.6），參考資料偏弱。",[20,21,22,23],"completed_building","controlled_experiment","cross_site","independent_reference",[25,26,27,28,29,30],"Octree-based initial estimation lets ICP match scans from rudimentary odometry guesses; without it ICP would likely converge to a wrong minimum in the campus experiment (Sec. 3.2, Sec. 5.4)","ICP tolerated initial errors of about 1 m in position and about 15 degrees in orientation in the Birlinghoven data set (Sec. 5.3)","3D scans taken at sparse locations were aligned where a 2D slice-based alignment of the same data showed noticeable errors (Sec. 5.2, Fig. 10)","Length ratios measured in the final 77-scan campus map deviated 0.5-3.8% from ratios measured in an uncalibrated aerial image (Table II)","A closed-loop distance of 2096 cm in the point cloud versus 2080 cm by meter rule (Fig. 13 caption)","Cached k-d tree search gives exact correspondences faster than conventional k-d tree search after the first ICP iteration (Sec. 4.4, Fig. 7)",[32,33,34,35,36,37],"The approach concentrates on single loops and keeps one pose hypothesis; multi-hypothesis tracking was considered computationally infeasible (Sec. 3, Sec. 6)","The octree initial-guess heuristic works best outdoors; symmetric indoor scenes such as corridors risk many plausible matches (Sec. 3.2)","No outdoor ground truth; the aerial-image comparison is described by the authors as sketchy and a proper SLAM performance metric as missing (Sec. 5.6)","Poses are corrected only at scan poses, leaving gaps in the trajectory that must be patched afterwards (Sec. 5.4, Fig. 18)","No covariance from scan matching; explicit uncertainty representation left to future work (Sec. 6)","(inference) Stop-scan-go acquisition (3.4 s for a 181x256 scan, Sec. 5.1) is incompatible with continuous handheld or walking capture typical of current construction mobile mapping. Bosse and Zlot 2009 (Sec. I, full text) cite stop-and-scan systems as refs. [3]-[5], but the reference list is not displayed on IEEE Xplore, so whether this paper is among them is unverified; Zlot and Bosse 2014 (Sec. 1) list the group's earlier mine-mapping system (Nüchter et al. 2004) among stop-and-scan solutions.",[39,40],"3D laser range finder built from a SICK 2D scanner on a servo-driven pitch mount (Sec. 5.1)","wheel odometry used only for initial pose extrapolation (Sec. 3.1)",[42],"wheeled UGV (Kurt3D outdoor version, six-wheel skid steer; Sec. 5.1)","single-hypothesis deterministic pose estimation: odometry extrapolated to 6 DoF, octree-based coarse-to-fine initial alignment, then ICP with closed-form SVD solution per scan; explicitly no probabilistic filter or covariance (Sec. 3, Sec. 6)","point-to-point closest-point correspondences (ICP) using k-d tree, approximate k-d tree and cached k-d tree search; octree cube-overlap counting for the initial guess (Sec. 3.2-3.3, Sec. 4)","discrete poses (one 6-DoF pose per stop-scan-go 3D scan)","avoided by design: the robot stands still while each 3D scan is taken (stop-scan-go, Sec. 3 and Sec. 5.1); no in-motion distortion correction","loop hypothesis from maximum laser range and current pose, revised by octree matching; accepted when the number of closest-point pairs exceeds a threshold; closing error distributed over the loop's scans in proportion to travelled path length (translation linearly, rotation by quaternion interpolation) (Sec. 3.4)","'simultaneous matching' global relaxation inspired by Pulli: queue-based re-registration of each scan against the union of overlapping neighbours (overlap = more than 250 point pairs) until no scan moves more than 5 cm; the first scan is fixed; run offline after acquisition (Sec. 3.5, Sec. 4)","set of registered 3D point clouds (scans); octree used only for initial alignment; voxel view shown for a RoboCup arena map (Fig. 19)","none (odometry only as initial guess)","globally registered 3D point cloud and 6-DoF scan poses; a continuous trajectory is reconstructed afterwards by distributing the gaps between trajectory patches (Sec. 5.4, Fig. 18); no covariance output","for 77 scans: scan registration plus loop detection about 10 min, global relaxation about 2 h on a Pentium-IV 2.8 GHz (Sec. 5.4); octree heuristic up to about 2 s per scan pair if applied naively (Sec. 4); approximate k-d tree search cuts ICP time to roughly 75% (Sec. 4.3); on-board CPU Pentium Centrino 1.4 GHz (Sec. 5.1)","https:\u002F\u002Fslam6d.sourceforge.io\u002F","GPL-3.0 (3DTK LICENSING file and package.xml in github.com\u002FJMUWRobotics\u002F3DTK, main branch; bundled third-party libraries keep their own licences; the file also places 3DTK-generated images and videos under CC BY-SA 3.0 and asks that the software not be used in military contexts). Applies to the current toolkit, not to a frozen release matching the paper.",[56],{"relation":57,"title":58,"doi_or_url":53},"code_release","3DTK - The 3D Toolkit (slam6d); the 3DTK publications page lists this paper among those the toolkit implements; the paper itself does not cite a code URL",{"id":5,"kind":60,"shortName":7,"title":61,"authors":62,"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":53,"cluster":11,"topics":77,"mdpi":78,"verification":79,"label":6,"fulltextRoute":80,"versionRead":81,"addedByCensus":78},"method","6D SLAM—3D mapping outdoor environments",[63,64,65,66],"Andreas Nüchter","Kai Lingemann","Joachim Hertzberg","Hartmut Surmann","Journal of Field Robotics","journal","Wiley","24(8-9): 699-722","10.1002\u002Frob.20209",null,"http:\u002F\u002Fkos.informatik.uni-osnabrueck.de\u002Fdownload\u002Fjfr2007.pdf","2007-09-07","metadata_verified","necessary technical node: a complete 6-DoF SLAM pipeline on 3D laser scans (ICP registration, loop closing with error distribution, global relaxation) published before LOAM, needed so the RQ1 lineage does not jump from 2D Lu-Milios\u002FGMapping to LOAM; note that LOAM cites it only as a general lidar-navigation reference (loam2014 Sec. II), so it is a historical node rather than a documented direct ancestor of LOAM.",[11],false,"corrected","author copy","version of record: Wiley typeset PDF, Journal of Field Robotics 24(8\u002F9):699-722 (2007), hosted on the University of Osnabrück author server",[83,89,93,98,104,108,114],{"category":84,"model":85,"canonical":85,"role":86,"dataset":72,"specs":87,"locator":88},"lidar","3D laser range finder built from a SICK 2D laser range finder on a servo-driven pitch mount (SICK model not stated)","method input","scans up to 180 deg (h) x 120 deg (v); horizontal resolutions 181, 361, 721 and vertical 128, 225, 420, 500; a 181-point plane takes 13 ms; a 181 x 256 scan takes 3.4 s; stop-scan-go acquisition","Sec. 5.1, Fig. 8",{"category":90,"model":91,"canonical":91,"role":86,"dataset":72,"specs":92,"locator":88},"platform","Kurt3D (outdoor version)","45 cm x 33 cm x 29 cm, 22.6 kg; two 90 W motors driving six skid-steered wheels; 16-bit CMOS microcontroller for motor control",{"category":94,"model":95,"canonical":95,"role":86,"dataset":72,"specs":96,"locator":97},"wheel_or_leg_odometry","Kurt3D wheel odometer","planar odometry extrapolated to 6 DoF as initial guess only","Sec. 3.1",{"category":99,"model":100,"canonical":100,"role":101,"dataset":72,"specs":102,"locator":103},"compute","Pentium-Centrino-1400 with 768 MB RAM, Linux (robot core computer)","compute for runtime","on-board computer of Kurt3D; no processing times are reported on it","Sec. 5.1",{"category":99,"model":105,"canonical":105,"role":101,"dataset":72,"specs":106,"locator":107},"Pentium-IV-2800 MHz","scan registration and loop detection for 77 scans about 10 min; global relaxation run for 2 h","Sec. 5.4",{"category":109,"model":110,"canonical":110,"role":111,"dataset":72,"specs":112,"locator":113},"other","Meter rule","reference or ground truth","reference distance of a closed-loop span (2080 cm) and pose shifts for matchability tests","Sec. 4.3, Fig. 13",{"category":109,"model":115,"canonical":115,"role":111,"dataset":72,"specs":116,"locator":117},"Uncalibrated mid-resolution aerial image","distance ratios between reference points A-D compared with the point model","Sec. 5.4, Fig. 15, Table II",[],{"totalRows":120,"groupCount":121,"groups":122,"others":253},10,5,[123,169,199,227],{"slug":124,"group":125,"sourceId":5,"sourceLabel":6,"table":126,"selfRows":127,"metrics":128,"seqs":134,"entrants":145,"cells":149,"outcomes":163,"locators":164,"hardware":165,"wordings":166,"notes":167},"nuchter2007-6dslam-table-ii","nuchter2007_6dslam:Table II","Table II",4,[129],{"label":130,"unit":131,"statistic":132,"alignment":133},"deviation between length ratio in aerial view and in point model","%","not_reported","none",[135,139,141,143],{"dataset":136,"sequence":137,"environment":138},"author-collected Kurt3D data (Schloss Birlinghoven campus)","AB\u002FBC (aerial 0.683, model 0.662)","outdoor campus",{"dataset":136,"sequence":140,"environment":138},"AB\u002FBD (aerial 0.645, model 0.67)",{"dataset":136,"sequence":142,"environment":138},"AC\u002FCD (aerial 1.131, model 1.141)",{"dataset":136,"sequence":144,"environment":138},"CD\u002FBD (aerial 1.088, model 1.082)",[146],{"name":147,"methodId":5,"linkable":148,"proposed":148,"self":148},"6D SLAM point model (ICP, loop closing, global relaxation)",true,[150,154,157,160],[151,151,151,152,153,151,153,153,151],0,3.1,-1,[151,151,155,156,153,151,153,153,151],1,3.8,[151,151,158,159,153,151,153,153,151],2,0.9,[151,151,161,162,153,151,153,153,151],3,0.5,[],[126],[],[],[168],"Length ratios measured in an uncalibrated aerial photo compared with ratios in the final 77-scan point model of the Schloss Birlinghoven campus",{"slug":170,"group":171,"sourceId":5,"sourceLabel":6,"table":172,"selfRows":158,"metrics":173,"seqs":180,"entrants":185,"cells":188,"outcomes":192,"locators":193,"hardware":195,"wordings":196,"notes":197},"nuchter2007-6dslam-text-sec-5-3","nuchter2007_6dslam:Text Sec. 5.3","Text Sec. 5.3",[174,177],{"label":175,"unit":176,"statistic":132,"alignment":133},"tolerated initial position error in x, y, z (about)","m",{"label":178,"unit":179,"statistic":132,"alignment":133},"tolerated initial orientation error (about)","deg",[181],{"dataset":182,"sequence":183,"environment":184},"author-collected Kurt3D data (Birlinghoven robotic lab)","32 scans","indoor and outdoor",[186],{"name":187,"methodId":5,"linkable":148,"proposed":148,"self":148},"ICP scan matching",[189,190],[151,151,151,155,153,151,153,153,151],[151,155,151,191,153,151,153,153,151],15,[],[194],"Sec. 5.3",[],[],[198],"Error tolerance of the initial estimate for successful ICP registration in the Birlinghoven data set",{"slug":200,"group":201,"sourceId":5,"sourceLabel":6,"table":202,"selfRows":158,"metrics":203,"seqs":210,"entrants":213,"cells":218,"outcomes":221,"locators":222,"hardware":223,"wordings":224,"notes":225},"nuchter2007-6dslam-text-sec-5-4","nuchter2007_6dslam:Text Sec. 5.4","Text Sec. 5.4",[204,207],{"label":205,"unit":206,"statistic":132,"alignment":133},"time for scan registration and closed loop detection (total)","min",{"label":208,"unit":209,"statistic":132,"alignment":133},"time the global relaxation was run (total)","h",[211],{"dataset":136,"sequence":212,"environment":138},"77 scans",[214,216],{"name":215,"methodId":5,"linkable":148,"proposed":148,"self":148},"6D SLAM",{"name":217,"methodId":5,"linkable":148,"proposed":148,"self":148},"6D SLAM global relaxation",[219,220],[151,151,151,120,153,151,151,153,151],[155,155,151,158,153,151,151,153,151],[],[107],[105],[],[226],"Processing times for the 77-scan campus data set",{"slug":228,"group":229,"sourceId":5,"sourceLabel":6,"table":230,"selfRows":155,"metrics":231,"seqs":235,"entrants":240,"cells":243,"outcomes":246,"locators":247,"hardware":249,"wordings":250,"notes":251},"nuchter2007-6dslam-text-fig-13-caption","nuchter2007_6dslam:Text Fig. 13 caption","Text Fig. 13 caption",[232],{"label":233,"unit":234,"statistic":132,"alignment":133},"distance d measured in the point cloud model (meter rule: 2080 cm)","cm",[236],{"dataset":237,"sequence":238,"environment":239},"author-collected Kurt3D data (Birlinghoven robotic lab, 32 scans)","closed loop distance d","indoor and outdoor lab buildings",[241],{"name":242,"methodId":5,"linkable":148,"proposed":148,"self":148},"6D SLAM point model",[244],[151,151,151,245,153,151,153,153,151],2096,[],[248],"Fig. 13 caption",[],[],[252],"Closed-loop span measured in the registered point cloud versus meter rule (2080 cm)",[254],{"group":255,"slug":256,"sourceLabel":6,"table":257,"selfRows":155,"datasets":258},"nuchter2007_6dslam:Text Sec. 4.3","nuchter2007-6dslam-text-sec-4-3","Text Sec. 4.3",[259],"not_reported (two 3D scans)",1790510661035]