[{"data":1,"prerenderedAt":403},["ShallowReactive",2],{"method-magnusson2009thesis":3},{"method":4,"reference":69,"equipment":86,"figures":156,"results":157},{"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":25,"limitations":31,"sensors":41,"platform":50,"estimator":56,"association":57,"timeModel":58,"deskew":59,"loopClosure":60,"globalOptimization":61,"mapRepresentation":62,"prior":63,"outputGeometry":64,"compute":65,"codeUrl":66,"codeLicense":67,"relatedVersions":68},"magnusson2009thesis","Magnusson, 2009","3D-NDT thesis","The three-dimensional normal-distributions transform: an efficient representation for registration, surface analysis, and loop detection",2009,"classic","C02","registration_component","此博士論文以常態分布轉換（NDT）作為三維掃描的通用表面表示，並用於掃描配準、迴圈偵測與表面結構分析。配準部分將 Biber 與 Straßer 的二維 NDT 擴展到三維，以 z-y-x Euler 角參數化、解析梯度與 Hessian 搭配牛頓法與 Moré-Thuente 線搜尋求解，並提出由粗到細的迭代離散化（2 m、1 m、0.5 m 格子）、連結格子與三線性內插等延伸。作者在 Kvarntorp 礦坑隧道、模擬場景與飛行時間相機資料上，以 100 組預設初始偏移進行受控測試，並以里程計為初值處理兩段礦坑掃描序列，與 ICP 比較後結論為 NDT 對初始旋轉誤差較穩健且較快；Hessian 的反矩陣可估計位姿變異，作為配準成功與否的信心指標。論文另提出以色彩核函數擴充的 Colour-NDT、不需位姿資訊而以 NDT 表面形狀直方圖進行的外觀式迴圈偵測，以及用於礦堆巨石偵測的局部表面粗糙度分類。","PhD thesis presenting 3D-NDT as a general surface representation: Newton registration with iterative discretisation, linked cells and trilinear interpolation, evaluated against ICP on mine-tunnel, simulated and time-of-flight data; a Hessian-based confidence measure; Colour-NDT; NDT surface-shape histograms for appearance-based loop detection; and roughness-based point classification for boulder detection.","full_text_reviewed","thesis_or_report","supplementary","論文主要應用為地下採礦：Kvarntorp 砂岩礦坑隧道的掃描配準與迴圈偵測，以及 Kemi 鉻礦礦堆的巨石偵測。第 1 章指出三維隧道模型可用於核對新開挖隧道形狀是否符合原設計並計算開挖量；第 10 章指出表面結構分析可延伸至採礦與營建中的料堆擷取。論文未使用營建工地資料；隧道與料堆情境對隧道施工與土方量測的參考價值屬推論。",[20,21,22,23,24],"simulation","underground_or_tunnel","completed_building","controlled_experiment","cross_site",[26,27,28,29,30],"Kvarntorp-Loop (48 scans): NDT and ICP both 98% successful with NDT much faster; trilinear NDT registered all scans (Sec. 6.4.3, Fig. 6.27)","Mission-4 (55 scans): NDT 96% (53 of 55) vs ICP 87%; trilinear NDT failed only on Scan 42 (Sec. 6.4.3, Fig. 6.28)","collaborative comparison with the Osnabrück ICP: within 0.20 m for 13.4% (ICP), 24.9% (NDT) and 99.8% (trilinear NDT) of 441 start poses (Sec. 6.4.2, Fig. 6.20)","the largest eigenvalue of the inverse Hessian (threshold 0.5) separates failed from successful registrations better than the NDT score or the mean squared point distance (Sec. 6.6)","loop detection: 80.6% recall at 1% false positives on Hannover-2 and 47.0% recall at 100% precision in the SLAM-style test; about 25 000 scan comparisons per second (Sec. 8.2.3, 8.2.5)",[32,33,34,35,36,37,38,39,40],"real-data reference poses were chosen manually from visually best registrations, so success is judged by thresholds of 0.20 m and 0.05 rad (Sec. 6.4.1, 6.4.3)","the collaborative ICP comparison uses one scan pair and is not claimed to be statistically significant (Sec. 6.4.2)","Newton optimisation is local and needs an initial pose estimate (Sec. 10.2)","trilinear interpolation takes about four times as long as non-interpolated NDT (Sec. 6.4.1, 6.4.2)","Colour-NDT was assessed only visually on two indoor time-of-flight data sets (Sec. 7.3.2)","loop-detection recall falls to 27.5% to 28.6% in the self-similar mine data set, and the EM threshold needs at least about 100 scans (Sec. 8.2.3, 10.2)","boulder detection was evaluated qualitatively on four muck piles and cannot find buried boulders or faces aligned with the pile slope (Sec. 9.3.3, 9.4)","the author notes that 3D sensors of the time were too slow, expensive or fragile for production mines (Sec. 10.2)","discretisation issues of basic NDT motivate the multi-resolution and interpolation extensions (abstract)",[42,43,44,45,46,47,48,49],"SICK 2D lidar on a pan\u002Ftilt unit producing pitching 3D scans on Tjorven (180 deg horizontal, about 100 deg vertical field of view; SICK model not named for Tjorven)","SICK lidar on a continuously rotating slip-ring mount (yawing omnidirectional scans) and a Hokuyo 2D lidar for 2D localisation on Alfred","tiltable SICK laser scanner on Kurt3D (pitching scans)","PMD[vision] 19k time-of-flight camera combined with a Matrix-Vision Blue Fox colour camera (Colour-NDT data)","SwissRanger time-of-flight camera (3D-Cam scan pair, collected by Jacobs University Bremen)","SICK lidar on a Schunk PowerCube via slip-ring contacts with a digital camera (Kemi mine muck-pile scans)","simulated yawing lidar (Sci-Fi and Sim-Mine scan pairs)","wheel-encoder odometry for initial pose estimates (Kvarntorp-Loop, Mission-4)",[51,52,53,54,55],"ActivMedia Pioneer P3-AT robot 'Tjorven' (Kvarntorp mine scans, Colour-NDT data)","Permobil electric-wheelchair platform 'Alfred' (part of the loop-detection data)","Kurt3D robot of Osnabrück University (Mission-4, Mission-4-1 and collaborative ICP comparison data)","service van carrying the slip-ring lidar to muck piles in the Kemi mine (scanner on the floor or in the van)","simulation (ray-traced scans)","Newton's method with Moré-Thuente line search on the NDT score (Gaussian approximation of a normal-plus-uniform mixture) with analytic gradient and Hessian and z-y-x Euler parametrisation; baseline uses iterative discretisation (2, 1, 0.5 m cells) with linked cells, 20% spatially distributed subsampling of the current scan and a step-size convergence limit of 1e-6; a BFGS quasi-Newton variant was less robust","each current-scan point is scored against the Gaussian of the cell it falls in; linked cells use the nearest occupied cell (kD tree of occupied cells); trilinear interpolation weights the eight nearest cells; Colour-NDT weights per-cell colour-kernel Gaussians; the ICP baseline uses point-to-point closest points with a fixed 0.5 m outlier threshold (0.1 m for 3D-Cam)","not_applicable (pairwise registration)","none applied: the mobile-robot registration data sets were acquired stop-and-scan (robot stopped every few metres); Sec. 3.2 only reviews motion-compensation methods for scanning while moving","appearance-based loop detection with NDT surface-shape histograms (1 spherical, 9 planar and 1 linear class in 5 range intervals; orientation normalised by dominant plane directions); threshold chosen manually or from an EM-fitted Gamma mixture; detects loop candidates only","not addressed: pose-graph relaxation is deferred to existing methods (Grisetti et al., Borrmann et al.); Mission-4 and Mission-4-1 reference poses were produced with the Borrmann et al. relaxation using manually created loop closures","NDT cell grids: iterative multi-resolution cells (2, 1, 0.5 m for lidar scans; 0.5, 0.25, 0.125 m for time-of-flight data), with octree and k-means variants evaluated; Colour-NDT stores three colour-weighted Gaussians per cell; loop detection summarises overlapping 0.5 m cells as 55-bin surface-shape histograms","initial pose estimate for registration (predefined offsets in the pairwise tests; wheel odometry in the Kvarntorp-Loop and Mission-4 sequences); loop detection uses no pose information, only scan order for a 30-scan minimum loop size","6-DoF relative pose; NDT surface representation; loop detection output","C++; NDT vs ICP runs on an Intel Core2 Duo 2.80 GHz (one core, 2 GiB RAM); collaborative comparison and loop detection on a 1.6 GHz Intel Celeron laptop (2 GiB); boulder labelling on a laptop with a 1600 MHz CPU (2 GiB); interpolated NDT about four times slower than non-interpolated NDT; histogram creation 0.18 to 0.50 s per histogram and about 7 microseconds per histogram comparison; boulder labelling 7.3 to 25.9 s per scan",null,"not_verified",[],{"id":5,"kind":70,"shortName":7,"title":8,"authors":71,"year":9,"venue":73,"venueType":74,"publisher":75,"volumeIssuePages":76,"doi":66,"arxivId":66,"url":77,"firstPublicDate":78,"publicationStatus":16,"metadataStatus":79,"fulltextStatus":15,"era":10,"classicReason":80,"codeUrl":66,"cluster":11,"topics":81,"mdpi":82,"verification":83,"label":6,"fulltextRoute":84,"versionRead":85,"addedByCensus":82},"method",[72],"Martin Magnusson","Örebro Studies in Technology 36 (doctoral dissertation, Örebro University)","thesis","Örebro University","Örebro Studies in Technology 36; ISBN 978-91-7668-696-6","https:\u002F\u002Fwww.diva-portal.org\u002Fsmash\u002Fget\u002Fdiva2:276162\u002FFULLTEXT02.pdf","2009","metadata_verified","necessary technical node: consolidated 3D-NDT description (multi-resolution, trilinear interpolation, Newton optimization, loop detection) used as reference for NDT-based LiDAR localization.",[11],false,"confirmed","publisher OA","printed doctoral dissertation (Örebro Studies in Technology 36, ISBN 978-91-7668-696-6, printed 10\u002F2009), DiVA full-text PDF diva2:276162 FULLTEXT02, 220 PDF pages",[87,94,100,106,109,114,119,125,130,135,140,146,152],{"category":88,"model":89,"canonical":89,"role":90,"dataset":91,"specs":92,"locator":93},"platform","ActivMedia Pioneer P3-AT ('Tjorven')","method input","Straight, Crossing, Kvarntorp-Loop (Kvarntorp mine); Sofa-1, Sofa-2","onboard computer, wheel encoders for 2D odometry, pan\u002Ftilt SICK lidar, omnidirectional camera, differential GPS antenna","Sec. 4.1, 6.4.1, 6.4.3, 7.3.1",{"category":95,"model":96,"canonical":96,"role":90,"dataset":97,"specs":98,"locator":99},"lidar","SICK lidar on pan\u002Ftilt unit (model not named for Tjorven)","Straight, Crossing, Kvarntorp-Loop","pitching 3D scans, 180 deg horizontal and about 100 deg vertical field of view; about 90 000 to 95 000 points per scan in the mine data","Sec. 4.1, 6.4.1, 6.4.3",{"category":88,"model":101,"canonical":101,"role":102,"dataset":103,"specs":104,"locator":105},"Permobil electric wheelchair ('Alfred')","dataset sensor","part of the loop-detection data (data set not specified)","custom platform with hydraulic lift","Sec. 4.2",{"category":95,"model":107,"canonical":107,"role":102,"dataset":103,"specs":108,"locator":105},"SICK lidar on continuously rotating motor with slip-ring contacts (Alfred)","omnidirectional yawing 3D scans",{"category":88,"model":110,"canonical":110,"role":90,"dataset":111,"specs":112,"locator":113},"Kurt3D (Osnabrück University)","Mission-4, Mission-4-1, collaborative ICP comparison scan pair","controlled speed up to 4 m\u002Fs; two digital colour cameras","Sec. 4.3, 6.4.2, 6.4.3, 8.2.1",{"category":95,"model":115,"canonical":115,"role":90,"dataset":116,"specs":117,"locator":118},"tiltable SICK laser scanner (Kurt3D)","Mission-4, Mission-4-1","pitching scans with field of view similar to Tjorven; about 70 000 to 75 000 points per scan","Sec. 4.3, 6.4.3, 8.2.1",{"category":120,"model":121,"canonical":121,"role":90,"dataset":122,"specs":123,"locator":124},"other","PMD[vision] 19k time-of-flight camera","Sofa-1, Sofa-2","maximum range 7.5 m, 40 deg viewing angle, about 288 000 points per second","Sec. 3.1.6, 7.3.1",{"category":126,"model":127,"canonical":127,"role":90,"dataset":122,"specs":128,"locator":129},"camera","Matrix-Vision Blue Fox colour camera","combined with the time-of-flight camera to colour the point clouds","Sec. 7.3.1",{"category":120,"model":131,"canonical":131,"role":102,"dataset":132,"specs":133,"locator":134},"SwissRanger time-of-flight camera","3D-Cam (Jacobs University Bremen)","3D-Cam pair: about 65% overlap, about 25 000 points per scan","Sec. 6.4.1",{"category":95,"model":136,"canonical":136,"role":90,"dataset":137,"specs":138,"locator":139},"SICK lidar on a Schunk PowerCube via slip-ring contacts (with a digital camera)","Kemi mine muck piles (locations A to D)","72 000 to 418 000 points per scan, subsampled to one point per dm3 (10 000 to 22 000 points)","Sec. 9.3.2",{"category":141,"model":142,"canonical":142,"role":90,"dataset":143,"specs":144,"locator":145},"wheel_or_leg_odometry","wheel encoders (robot odometry)","Kvarntorp-Loop, Mission-4","initial pose errors up to about 1.5 m and 0.2 rad per step (Kvarntorp-Loop), up to 1.4 rad (Mission-4 Scan 33)","Sec. 6.4.3",{"category":147,"model":148,"canonical":148,"role":149,"dataset":66,"specs":150,"locator":151},"compute","Intel Core2 Duo 2.80 GHz, 2 GiB RAM (one core used)","compute for runtime","NDT and ICP pairwise experiments","Sec. 6.4.2",{"category":147,"model":153,"canonical":153,"role":149,"dataset":66,"specs":154,"locator":155},"laptop with 1.6 GHz Intel Celeron, 2 GiB RAM","collaborative ICP comparison (Sec. 6.4.2) and loop-detection timings (Sec. 8.2.5, Table 8.3: '1.6 GHz CPU'); Sec. 9.3.2 reports boulder labelling only on 'a laptop computer with a 1600 MHz CPU and 2 GiB of RAM' without naming the CPU, so it is not stated that this is the same Celeron laptop","Sec. 6.4.2, 8.2.5, 9.3.2",[],{"totalRows":158,"groupCount":159,"groups":160,"others":359},38,11,[161,231,282,324],{"slug":162,"group":163,"sourceId":5,"sourceLabel":6,"table":164,"selfRows":165,"metrics":166,"seqs":174,"entrants":193,"cells":197,"outcomes":225,"locators":226,"hardware":227,"wordings":228,"notes":229},"magnusson2009thesis-table-8-2","magnusson2009thesis:Table 8.2","Table 8.2",12,[167,172],{"label":168,"unit":169,"statistic":170,"alignment":171},"recall","%","not_reported","not_applicable",{"label":173,"unit":169,"statistic":170,"alignment":171},"precision",[175,179,181,185,187,191],{"dataset":176,"sequence":177,"environment":178},"Hannover-2 (428 revisited, 494 non-revisited scans)","manual threshold, td 0.0737","outdoor university campus",{"dataset":176,"sequence":180,"environment":178},"automatic threshold (Gamma mixture, EM), td 0.0843",{"dataset":182,"sequence":183,"environment":184},"AASS-Loop (23 revisited, 37 non-revisited scans)","manual threshold, td 0.099","indoor lab and office building",{"dataset":182,"sequence":186,"environment":184},"automatic threshold (Gamma mixture, EM), td 0.0906",{"dataset":188,"sequence":189,"environment":190},"Mission-4-1 (35 revisited, 95 non-revisited scans)","manual threshold, td 0.087","underground mine (Kvarntorp)",{"dataset":188,"sequence":192,"environment":190},"automatic threshold (Gamma mixture, EM), td 0.0851",[194],{"name":195,"methodId":5,"linkable":196,"proposed":196,"self":196},"NDT surface-shape histograms (loop detection)",true,[198,202,205,207,209,212,213,216,217,220,221,224],[199,199,199,200,201,199,201,201,199],0,47,-1,[199,203,199,204,201,199,201,201,199],1,100,[199,199,203,206,201,199,201,201,199],55.6,[199,203,203,208,201,199,201,201,199],94.8,[199,199,210,211,201,199,201,201,199],2,69.6,[199,203,210,204,201,199,201,201,199],[199,199,214,215,201,199,201,201,199],3,60.9,[199,203,214,204,201,199,201,201,199],[199,199,218,219,201,199,201,201,199],4,28.6,[199,203,218,204,201,199,201,201,199],[199,199,222,223,201,199,201,201,199],5,22.9,[199,203,222,204,201,199,201,201,199],[],[164],[],[],[230],"SLAM-scenario loop detection: each scan matched to its most similar scan more than 30 steps away; true positive if the scan is manually labelled revisited, the match is within 10 m and below td; manual td vs td from an EM-fitted Gamma mixture at p(fp) = 0.5%",{"slug":232,"group":233,"sourceId":5,"sourceLabel":6,"table":234,"selfRows":235,"metrics":236,"seqs":244,"entrants":249,"cells":257,"outcomes":274,"locators":275,"hardware":278,"wordings":279,"notes":280},"magnusson2009thesis-text-sec-6-4-2-figs-6-20-6-21-captions","magnusson2009thesis:Text Sec. 6.4.2 (Figs. 6.20-6.21 captions)","Text Sec. 6.4.2 (Figs. 6.20-6.21 captions)",6,[237,240,242],{"label":238,"unit":169,"statistic":170,"alignment":239},"success rate, strict translation threshold 0.20 m (second value in the Fig. 6.20 caption)","none",{"label":241,"unit":169,"statistic":170,"alignment":239},"success rate, loose translation threshold 1.0 m (first value in the Fig. 6.20 caption)",{"label":243,"unit":169,"statistic":170,"alignment":239},"success rate judging rotation error only (5 deg)",[245],{"dataset":246,"sequence":247,"environment":248},"Kvarntorp tunnel scan pair (collaborative comparison)","441 start poses","underground mine tunnel (Kvarntorp)",[250,253,255],{"name":251,"methodId":252,"linkable":196,"proposed":82,"self":82},"ICP (University of Osnabrück implementation, parameters selected by that group)","besl1992icp",{"name":254,"methodId":5,"linkable":196,"proposed":196,"self":196},"NDT (baseline: iterative discretisation with linked cells)",{"name":256,"methodId":5,"linkable":196,"proposed":196,"self":196},"NDT with trilinear interpolation",[258,260,262,264,266,268,270,272,273],[199,199,199,259,201,199,201,201,199],13.4,[199,203,199,261,201,199,201,201,199],29.9,[199,210,199,263,201,203,201,201,199],95.2,[203,199,199,265,201,199,201,201,199],24.9,[203,203,199,267,201,199,201,201,199],72.1,[203,210,199,269,201,203,201,201,199],97.7,[210,199,199,271,201,199,201,201,199],99.8,[210,203,199,271,201,199,201,201,199],[210,210,199,204,201,203,201,201,199],[],[276,277],"Fig. 6.20 caption; Sec. 6.4.2","Fig. 6.21 caption",[],[],[281],"collaborative comparison: one slightly curved Kvarntorp tunnel scan pair (8 000 subsampled points each), 441 start poses with horizontal-plane translation offsets and rotation offsets from -80 to +80 deg; Osnabrück ICP vs thesis NDT; success = translation within 0.20 m (strict) or 1.0 m (loose), rotation within 5 deg; reference pose agreed manually",{"slug":283,"group":284,"sourceId":5,"sourceLabel":6,"table":285,"selfRows":218,"metrics":286,"seqs":296,"entrants":302,"cells":308,"outcomes":316,"locators":317,"hardware":320,"wordings":321,"notes":322},"magnusson2009thesis-text-sec-6-4-3-figs-6-27-6-28-captions","magnusson2009thesis:Text Sec. 6.4.3 (Figs. 6.27-6.28 captions)","Text Sec. 6.4.3 (Figs. 6.27-6.28 captions)",[287,289,291,293],{"label":288,"unit":169,"statistic":170,"alignment":239},"success rate",{"label":290,"unit":169,"statistic":170,"alignment":239},"all scans correctly registered",{"label":292,"unit":169,"statistic":170,"alignment":239},"success rate (53 of 55 scans)",{"label":294,"unit":295,"statistic":170,"alignment":239},"number of failed registrations (Scan 42)","scans",[297,300],{"dataset":298,"sequence":299,"environment":190},"Kvarntorp-Loop (Tjorven, 48 scans)","all consecutive scan pairs",{"dataset":301,"sequence":299,"environment":190},"Mission-4 (Kurt3D, 55 scans, closed loop)",[303,305,307],{"name":304,"methodId":5,"linkable":196,"proposed":196,"self":196},"NDT (baseline)",{"name":306,"methodId":252,"linkable":196,"proposed":82,"self":82},"ICP (baseline)",{"name":256,"methodId":5,"linkable":196,"proposed":196,"self":196},[309,311,312,313,315],[199,199,199,310,201,199,201,201,199],98,[203,199,199,310,201,199,201,201,199],[210,203,199,204,201,203,201,201,199],[199,210,203,314,201,210,201,201,199],96,[210,214,203,203,201,203,201,201,199],[],[318,145,319],"Fig. 6.27 caption","Fig. 6.28 caption; Sec. 6.4.3",[],[],[323],"stop-and-scan sequences in the Kvarntorp mine registered pairwise from odometry initial poses; success = within 0.20 m and 0.05 rad of manually determined reference poses",{"slug":325,"group":326,"sourceId":5,"sourceLabel":6,"table":327,"selfRows":214,"metrics":328,"seqs":331,"entrants":343,"cells":345,"outcomes":352,"locators":353,"hardware":355,"wordings":356,"notes":357},"magnusson2009thesis-table-8-1","magnusson2009thesis:Table 8.1","Table 8.1",[329],{"label":330,"unit":169,"statistic":170,"alignment":171},"recall with less than 1% false positives",[332,336,340],{"dataset":333,"sequence":334,"environment":335},"Hannover-2 (922 omnidirectional scans, about 1.24 km)","all pairs; tr 3 m; 9 984 overlapping and 839 178 non-overlapping pairs; td 0.1494","outdoor university campus (Leibniz Universität Hannover)",{"dataset":337,"sequence":338,"environment":339},"AASS-Loop (60 omnidirectional scans, 111 m)","all pairs; tr 1 m; 32 overlapping and 3 508 non-overlapping pairs; td 0.0990","indoor lab and office building (AASS, Örebro University)",{"dataset":341,"sequence":342,"environment":190},"Mission-4-1 (131 scans, 180 deg field of view, about 370 m)","all pairs; tr 3 m; 138 overlapping and 16 632 non-overlapping pairs; td 0.1125 (Sec. 8.2.3 gives 0.1134)",[344],{"name":195,"methodId":5,"linkable":196,"proposed":196,"self":196},[346,348,350],[199,199,199,347,201,199,201,201,199],80.6,[199,199,203,349,201,199,201,201,199],62.5,[199,199,210,351,201,199,201,201,199],27.5,[],[354],"Table 8.1; Sec. 8.2.3",[],[],[358],"loop detection over all scan pairs; maximum recall with less than 1% false positives; ground truth = scan pairs closer than tr (Mission-4-1 also within 20 deg heading)",[360,368,374,380,386,391,397],{"group":361,"slug":362,"sourceLabel":6,"table":363,"selfRows":214,"datasets":364},"magnusson2009thesis:Table 8.3","magnusson2009thesis-table-8-3","Table 8.3",[365,366,367],"AASS-Loop (112 000 points per scan, 2.4 histograms per scan)","Hannover-2 (15 000 points per scan, 3.2 histograms per scan)","Mission-4-1 (70 000 points per scan, 2.8 histograms per scan)",{"group":369,"slug":370,"sourceLabel":6,"table":371,"selfRows":210,"datasets":372},"magnusson2009thesis:Text Sec. 6.4.1 (discretisation methods)","magnusson2009thesis-text-sec-6-4-1-discretisation-methods","Text Sec. 6.4.1 (discretisation methods)",[373],"Straight (Kvarntorp-Loop scans 51 and 52)",{"group":375,"slug":376,"sourceLabel":6,"table":377,"selfRows":210,"datasets":378},"magnusson2009thesis:Text Sec. 6.4.2 (Crossing)","magnusson2009thesis-text-sec-6-4-2-crossing","Text Sec. 6.4.2 (Crossing)",[379],"Crossing (Kvarntorp-Loop scans 36 and 38)",{"group":381,"slug":382,"sourceLabel":6,"table":383,"selfRows":210,"datasets":384},"magnusson2009thesis:Text Sec. 8.2.1","magnusson2009thesis-text-sec-8-2-1","Text Sec. 8.2.1",[385],"AASS-Loop (60 scans, 111 m)",{"group":387,"slug":388,"sourceLabel":6,"table":389,"selfRows":210,"datasets":390},"magnusson2009thesis:Text Sec. 9.3.2","magnusson2009thesis-text-sec-9-3-2","Text Sec. 9.3.2",[137],{"group":392,"slug":393,"sourceLabel":6,"table":394,"selfRows":203,"datasets":395},"magnusson2009thesis:Text Sec. 6.4.1 (sample ratio)","magnusson2009thesis-text-sec-6-4-1-sample-ratio","Text Sec. 6.4.1 (sample ratio)",[396],"Crossing",{"group":398,"slug":399,"sourceLabel":6,"table":400,"selfRows":203,"datasets":401},"magnusson2009thesis:Text Sec. 8.2.5","magnusson2009thesis-text-sec-8-2-5","Text Sec. 8.2.5",[402],"AASS-Loop",1790510660967]