[{"data":1,"prerenderedAt":567},["ShallowReactive",2],{"method-malio2023":3},{"method":4,"reference":65,"equipment":87,"figures":152,"results":153},{"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":37,"platform":40,"estimator":43,"association":44,"timeModel":45,"deskew":46,"loopClosure":47,"globalOptimization":47,"mapRepresentation":48,"prior":49,"outputGeometry":50,"compute":51,"codeUrl":52,"codeLicense":53,"relatedVersions":54},"malio2023","Jung et al., 2023","MA-LIO","Asynchronous Multiple LiDAR-Inertial Odometry Using Point-Wise Inter-LiDAR Uncertainty Propagation",2023,"recent","C05","odometry_with_local_mapping","MA-LIO 處理多顆非同步、視野與掃描樣式不同的 LiDAR：先以 IMU 離散模型傳播位姿與共變異數，再以 B 樣條內插求得任一點取樣時刻的位姿，把各 LiDAR 的點去畸變並轉換到最後一顆 LiDAR 最新點的座標系，因此不需嚴格硬體同步也不依賴 LiDAR 間重疊。每個點依取樣時刻（狀態共變異數）與距離傳播出點級不確定度，用於加權點到平面殘差與決定是否存入 ikd-Tree 地圖；另以量測法向量奇異值比計算定位權重，在隧道或窄走廊等退化場景中提高 IMU 先驗的比重。狀態估計採迭代誤差狀態卡爾曼濾波。","Asynchronous multi-LiDAR inertial odometry that uses IMU propagation with B-spline interpolation to deskew and time-align points from LiDARs with different scan patterns, propagates point-wise uncertainty from acquisition-time state covariance and range to weight point-to-plane residuals and filter map insertion, and applies a degeneracy-dependent localization weight within an iterated error-state Kalman filter with ikd-Tree mapping.","full_text_reviewed","peer_reviewed_published","main_body","Hilti SLAM Dataset 2021 的 Construct 序列（依名稱對應資料集中的 Construction Site 序列，推論）為營建工地，MA-LIO 在此的 ATEt 為 0.063 m，優於只用 Ouster 的 FAST-LIO2（0.088 m）與其他多 LiDAR 方法（Table II）；該評估使用資料集提供的評分器與參考點 [helmberger2022hilti]。作者自建資料含約 400 m 隧道，定位權重可在退化段降低 LiDAR 殘差比重。多顆不同廠牌 LiDAR 非同步融合、以不確定度過濾地圖點，對需要擴大視野以涵蓋天花板、樓板與牆面的工地掃描平台有直接參考價值（推論）。",[20,21,22,23,24],"public_benchmark","real_construction_site","underground_or_tunnel","independent_reference","cross_site",[26,27,28,29,30],"Lowest ATEt on four of the five evaluated Hilti 2021 sequences and tied with LOCUS 2.0 on UZH (0.177 m), including Construct (0.063 m versus 0.088 m for FAST-LIO2 with the Ouster alone) (Table II)","On UrbanNav, lowest ATEt on Mongok, Whampoa and TST (2.579, 4.236, 2.342 m) versus FAST-LIO2 5.917, 7.066, 8.783 m (Table III)","On the authors' high-speed city data, City02 with a 400 m tunnel had ATEt 6.707 m versus 35.308 m for FAST-LIO2 and 72.382 m for M-LOAM (Table IV)","Point-wise uncertainty was the most influential component in the ablation and enabled consistent mapping with the narrow-FOV Livox in the LAB sequence where the baseline failed (Table V, Fig. 7)","Works with any combination of LiDAR makes and scanning patterns without strict hardware synchronization (Sec. I, III)",[32,33,34,35,36],"Adding a third LiDAR brought little gain when it overlapped strongly with the second; LiDAR placement matters (Sec. III-F, Fig. 10)","On TST the uncertainty module slightly degraded accuracy relative to RAW because an inclined LiDAR became the primary one (Table V, Sec. III-E)","B-spline undistortion is done before downsampling and dominates part of the runtime (Sec. III-F)","Continuous-time terms are not included in the optimization for real-time reasons (Sec. I)","No loop closure; own-dataset ground truth only uses INS positions with a particular solution status (Sec. III-A)",[38,39],"multiple asynchronous 3D LiDARs of different makes and scan patterns (Ouster OS0-64 plus Livox; Velodyne HDL-32E, VLP-16 and LS-16C; Ouster OS2-128 plus Livox Avia and Livox Tele)","IMU (100 to 400 Hz; models not stated)",[41,42],"handheld (Hilti SLAM Dataset 2021)","vehicle (UrbanNav Hong Kong; authors' city dataset up to about 50 km\u002Fh)","iterated error-state Kalman filter on manifold (FAST-LIO2 style) whose state includes each LiDAR-IMU extrinsic; measurement residuals weighted by point-wise uncertainty rescaled with fixed interval conversion, and a localization weight from the singular values of measurement normals that shifts weight to the IMU prior in degenerate scenes (Sec. II-A, II-E, II-F)","direct point-to-plane: five nearest neighbours in the ikd-Tree define a local plane whose covariance is a weighted sum of neighbour point covariances (Sec. II-E)","discrete IMU propagation with covariance, plus cumulative B-spline interpolation over four propagated poses to obtain pose and covariance at any point time (Sec. II-B)","B-spline interpolated IMU poses undistort every LiDAR's points to its latest point time and then compensate inter-LiDAR temporal offsets by transforming them to the latest point of the latest LiDAR (Sec. II-C)","none","ikd-Tree point map storing only points whose propagated uncertainty trace is below a threshold, with downsampling that keeps low-uncertainty points near voxel centres (Sec. II-G)","LiDAR-IMU extrinsics in the state; a set of LiDARs chosen to minimize arrival-time differences (Sec. II-A, II-C)","odometry and a merged multi-LiDAR point map (Figs. 1, 6)","real time: 28.1 to 47.7 ms per scan on Whampoa with 1 to 3 LiDARs and 46.4 to 79 ms on City01 with 1 to 3 LiDARs on an Intel i7 CPU at 2.50 GHz with 48 GB RAM; point-wise uncertainty at most 5 ms (Table VI, Sec. III-F)","https:\u002F\u002Fgithub.com\u002Fminwoo0611\u002FMA-LIO","GPL-2.0 (LICENSE file checked)",[55,59,62],{"relation":56,"title":57,"doi_or_url":58},"preprint","arXiv 2305.16792 (v1 2023-05-26; v2 2023-11-07 with typo corrections recommended by the authors over the IEEE version)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.16792",{"relation":60,"title":61,"doi_or_url":52},"correction_statement_in_article","Repository README (update 2023-11-07) states the IEEE version contains typo errors and points to the revised arXiv version",{"relation":63,"title":64,"doi_or_url":52},"code_release","minwoo0611\u002FMA-LIO",{"id":5,"kind":66,"shortName":7,"title":8,"authors":67,"year":9,"venue":71,"venueType":72,"publisher":73,"volumeIssuePages":74,"doi":75,"arxivId":76,"url":77,"firstPublicDate":78,"publicationStatus":16,"metadataStatus":79,"fulltextStatus":15,"era":10,"classicReason":80,"codeUrl":52,"cluster":11,"topics":81,"mdpi":82,"verification":83,"label":6,"fulltextRoute":84,"versionRead":85,"addedByCensus":86},"method",[68,69,70],"Minwoo Jung","Sangwoo Jung","Ayoung Kim","IEEE Robotics and Automation Letters","journal","IEEE","8(7):4211-4218","10.1109\u002Flra.2023.3281264","2305.16792","https:\u002F\u002Fdoi.org\u002F10.1109\u002FLRA.2023.3281264","2023-05-26","metadata_verified","not_applicable",[11],false,"corrected","arXiv","arXiv v2 (2023-11-07), recommended by the authors because the IEEE version has typos; RA-L version of record not read",true,[88,95,100,105,110,114,116,119,123,125,127,130,136,141,148],{"category":89,"model":90,"canonical":90,"role":91,"dataset":92,"specs":93,"locator":94},"lidar","Ouster OS0-64","dataset sensor","Hilti SLAM Dataset 2021","not_reported","Table I; Sec. III-A",{"category":89,"model":96,"canonical":97,"role":91,"dataset":92,"specs":98,"locator":99},"Livox Horizon (as written in Table I)","Livox Horizon","limited FOV","Table I; Fig. 4",{"category":101,"model":102,"canonical":102,"role":91,"dataset":92,"specs":103,"locator":104},"imu","IMU at 200 Hz (model not stated)","200 Hz","Table I",{"category":89,"model":106,"canonical":106,"role":91,"dataset":107,"specs":108,"locator":109},"Velodyne HDL-32E","UrbanNav","central LiDAR","Table I; Table VI",{"category":89,"model":111,"canonical":111,"role":91,"dataset":107,"specs":112,"locator":113},"Velodyne VLP-16","inclined side LiDAR","Table I; Sec. III-C",{"category":89,"model":115,"canonical":115,"role":91,"dataset":107,"specs":112,"locator":109},"LS-16C (written LS-C16 in Table VI)",{"category":101,"model":117,"canonical":117,"role":91,"dataset":107,"specs":118,"locator":104},"IMU at 400 Hz (model not stated)","400 Hz",{"category":89,"model":120,"canonical":120,"role":121,"dataset":122,"specs":108,"locator":109},"Ouster OS2-128","method input","MA-LIO city dataset (City01-03)",{"category":89,"model":124,"canonical":124,"role":121,"dataset":122,"specs":93,"locator":104},"Livox Avia",{"category":89,"model":126,"canonical":126,"role":121,"dataset":122,"specs":93,"locator":104},"Livox Tele",{"category":101,"model":128,"canonical":128,"role":121,"dataset":122,"specs":129,"locator":94},"IMU at 100 Hz (model not stated)","100 Hz; sensors time-referenced with PTP but not fired simultaneously",{"category":131,"model":132,"canonical":132,"role":133,"dataset":122,"specs":134,"locator":135},"gnss","Inertial Navigation System (model not stated)","reference or ground truth","ground truth using only positions with status INS SOLUTION FREE","Sec. III-A",{"category":137,"model":138,"canonical":138,"role":121,"dataset":122,"specs":139,"locator":140},"platform","vehicle","up to about 50 km\u002Fh, U-turns and a 400 m tunnel","Sec. III-A; Sec. III-D",{"category":142,"model":143,"canonical":143,"role":144,"dataset":145,"specs":146,"locator":147},"compute","Intel i7 CPU @ 2.50 GHz (model not stated)","compute for runtime",null,"48 GB RAM","Table VI",{"category":137,"model":149,"canonical":149,"role":91,"dataset":92,"specs":150,"locator":151},"hand-held system (Hilti SLAM Dataset 2021)","small-scale indoor and outdoor environments","Sec. III-A-1",[],{"totalRows":154,"groupCount":155,"groups":156,"others":561},65,5,[157,265,398,516],{"slug":158,"group":159,"sourceId":5,"sourceLabel":6,"table":160,"selfRows":161,"metrics":162,"seqs":167,"entrants":181,"cells":192,"outcomes":259,"locators":260,"hardware":261,"wordings":262,"notes":263},"malio2023-table-v","malio2023:Table V","Table V",30,[163],{"label":164,"unit":165,"statistic":166,"alignment":93},"ATEt","m","RMSE",[168,171,173,175,177,179],{"dataset":122,"sequence":169,"environment":170},"City01","vehicle, urban",{"dataset":122,"sequence":172,"environment":170},"City02",{"dataset":122,"sequence":174,"environment":170},"City03",{"dataset":107,"sequence":176,"environment":170},"Mongok",{"dataset":107,"sequence":178,"environment":170},"Whampoa",{"dataset":107,"sequence":180,"environment":170},"TST",[182,184,186,188,190],{"name":183,"methodId":5,"linkable":86,"proposed":82,"self":86},"MA-LIO RAW",{"name":185,"methodId":5,"linkable":86,"proposed":82,"self":86},"MA-LIO CNT",{"name":187,"methodId":5,"linkable":86,"proposed":82,"self":86},"MA-LIO F-UNC",{"name":189,"methodId":5,"linkable":86,"proposed":82,"self":86},"MA-LIO UNC",{"name":191,"methodId":5,"linkable":86,"proposed":86,"self":86},"MA-LIO FULL",[193,197,200,203,206,209,211,213,215,217,219,221,223,225,227,229,231,233,235,237,239,241,243,245,247,249,251,253,255,257],[194,194,194,195,196,194,196,196,194],0,7.345,-1,[198,194,194,199,196,194,196,196,194],1,7.28,[201,194,194,202,196,194,196,196,194],2,7.001,[204,194,194,205,196,194,196,196,194],3,6.831,[207,194,194,208,196,194,196,196,194],4,6.538,[194,194,198,210,196,194,196,196,194],7.346,[198,194,198,212,196,194,196,196,194],6.835,[201,194,198,214,196,194,196,196,194],7.058,[204,194,198,216,196,194,196,196,194],6.844,[207,194,198,218,196,194,196,196,194],6.707,[194,194,201,220,196,194,196,196,194],6.043,[198,194,201,222,196,194,196,196,194],5.969,[201,194,201,224,196,194,196,196,194],6.547,[204,194,201,226,196,194,196,196,194],5.837,[207,194,201,228,196,194,196,196,194],5.47,[194,194,204,230,196,194,196,196,194],2.652,[198,194,204,232,196,194,196,196,194],2.597,[201,194,204,234,196,194,196,196,194],2.645,[204,194,204,236,196,194,196,196,194],2.611,[207,194,204,238,196,194,196,196,194],2.579,[194,194,207,240,196,194,196,196,194],4.728,[198,194,207,242,196,194,196,196,194],4.463,[201,194,207,244,196,194,196,196,194],4.657,[204,194,207,246,196,194,196,196,194],4.078,[207,194,207,248,196,194,196,196,194],4.236,[194,194,155,250,196,194,196,196,194],2.721,[198,194,155,252,196,194,196,196,194],2.143,[201,194,155,254,196,194,196,196,194],2.773,[204,194,155,256,196,194,196,196,194],2.752,[207,194,155,258,196,194,196,196,194],2.324,[],[160],[],[],[264],"Component ablation with ATEt: RAW (IMU discrete model, equal point weights), CNT (B-spline interpolation), F-UNC (single state covariance as in M-LOAM), UNC (point-wise uncertainty with localization weight), FULL (CNT + UNC)",{"slug":266,"group":267,"sourceId":5,"sourceLabel":6,"table":268,"selfRows":269,"metrics":270,"seqs":282,"entrants":289,"cells":299,"outcomes":392,"locators":393,"hardware":394,"wordings":395,"notes":396},"malio2023-table-iii","malio2023:Table III","Table III",12,[271,273,276,279],{"label":272,"unit":165,"statistic":166,"alignment":93},"ATEt (RMSE via evo)",{"label":274,"unit":275,"statistic":166,"alignment":93},"ATEr (RMSE via evo)","deg",{"label":277,"unit":278,"statistic":166,"alignment":93},"RTEt (RMSE via evo)","%",{"label":280,"unit":281,"statistic":166,"alignment":93},"RTEr (RMSE via evo)","deg\u002Fm",[283,285,287],{"dataset":107,"sequence":176,"environment":284},"urban; repeated traversal of a single loop, dynamic objects (Sec. III-C)",{"dataset":107,"sequence":178,"environment":286},"urban with an underpass",{"dataset":107,"sequence":180,"environment":288},"dense urban at about twice Mongok speed",[290,293,295,297],{"name":291,"methodId":292,"linkable":86,"proposed":82,"self":82},"Fast-LIO2 (central LiDAR only)","fastlio2_2022",{"name":294,"methodId":145,"linkable":82,"proposed":82,"self":82},"M-LOAM",{"name":296,"methodId":145,"linkable":82,"proposed":82,"self":82},"LOCUS 2.0",{"name":298,"methodId":5,"linkable":86,"proposed":86,"self":86},"Ours",[300,302,304,306,307,309,311,313,315,317,319,321,323,325,327,329,331,333,335,337,338,339,341,343,345,347,348,350,352,354,356,358,360,362,364,366,368,370,372,374,376,378,380,382,384,386,388,390],[194,194,194,301,196,194,196,196,194],5.917,[198,194,194,303,196,194,196,196,194],25.899,[201,194,194,305,196,194,196,196,194],6.846,[204,194,194,238,196,194,196,196,194],[194,198,194,308,196,194,196,196,194],4.039,[198,198,194,310,196,194,196,196,194],9.14,[201,198,194,312,196,194,196,196,194],5.616,[204,198,194,314,196,194,196,196,194],2.383,[194,201,194,316,196,194,196,196,194],0.188,[198,201,194,318,196,194,196,196,194],0.632,[201,201,194,320,196,194,196,196,194],0.174,[204,201,194,322,196,194,196,196,194],0.167,[194,204,194,324,196,194,196,196,194],0.749,[198,204,194,326,196,194,196,196,194],1.006,[201,204,194,328,196,194,196,196,194],0.71,[204,204,194,330,196,194,196,196,194],0.736,[194,194,198,332,196,194,196,196,194],7.066,[198,194,198,334,196,194,196,196,194],31.482,[201,194,198,336,196,194,196,196,194],18.124,[204,194,198,248,196,194,196,196,194],[194,198,198,332,196,194,196,196,194],[198,198,198,340,196,194,196,196,194],8.286,[201,198,198,342,196,194,196,196,194],9.404,[204,198,198,344,196,194,196,196,194],4.6,[194,201,198,346,196,194,196,196,194],0.39,[198,201,198,328,196,194,196,196,194],[201,201,198,349,196,194,196,196,194],0.339,[204,201,198,351,196,194,196,196,194],0.207,[194,204,198,353,196,194,196,196,194],1.034,[198,204,198,355,196,194,196,196,194],1.213,[201,204,198,357,196,194,196,196,194],1.238,[204,204,198,359,196,194,196,196,194],1.033,[194,194,201,361,196,194,196,196,194],8.783,[198,194,201,363,196,194,196,196,194],53.682,[201,194,201,365,196,194,196,196,194],33.292,[204,194,201,367,196,194,196,196,194],2.342,[194,198,201,369,196,194,196,196,194],6.64,[198,198,201,371,196,194,196,196,194],21.584,[201,198,201,373,196,194,196,196,194],13.367,[204,198,201,375,196,194,196,196,194],5.085,[194,201,201,377,196,194,196,196,194],0.494,[198,201,201,379,196,194,196,196,194],2.177,[201,201,201,381,196,194,196,196,194],0.841,[204,201,201,383,196,194,196,196,194],0.351,[194,204,201,385,196,194,196,196,194],1.264,[198,204,201,387,196,194,196,196,194],1.355,[201,204,201,389,196,194,196,196,194],1.748,[204,204,201,391,196,194,196,196,194],1.261,[],[268],[],[],[397],"UrbanNav (HDL-32E central, VLP-16 and LS-16C inclined, 400 Hz IMU); RMSE of ATE and RTE via evo",{"slug":399,"group":400,"sourceId":5,"sourceLabel":6,"table":401,"selfRows":269,"metrics":402,"seqs":407,"entrants":414,"cells":419,"outcomes":510,"locators":511,"hardware":512,"wordings":513,"notes":514},"malio2023-table-iv","malio2023:Table IV","Table IV",[403,404,405,406],{"label":272,"unit":165,"statistic":166,"alignment":93},{"label":274,"unit":275,"statistic":166,"alignment":93},{"label":277,"unit":278,"statistic":166,"alignment":93},{"label":280,"unit":281,"statistic":166,"alignment":93},[408,410,412],{"dataset":122,"sequence":169,"environment":409},"urban with many rotations and U-turns, vehicle",{"dataset":122,"sequence":172,"environment":411},"urban with a 400 m tunnel, vehicle",{"dataset":122,"sequence":174,"environment":413},"4.3 km urban route with dynamic objects and no intermediate loop, vehicle",[415,416,417,418],{"name":291,"methodId":292,"linkable":86,"proposed":82,"self":82},{"name":294,"methodId":145,"linkable":82,"proposed":82,"self":82},{"name":296,"methodId":145,"linkable":82,"proposed":82,"self":82},{"name":298,"methodId":5,"linkable":86,"proposed":86,"self":86},[420,422,424,426,427,429,431,433,435,437,439,441,443,445,447,449,451,453,455,457,458,460,462,464,466,468,470,472,474,476,478,480,482,484,486,488,489,491,493,495,496,498,500,502,503,505,507,509],[194,194,194,421,196,194,196,196,194],9.97,[198,194,194,423,196,194,196,196,194],33.907,[201,194,194,425,196,194,196,196,194],23.998,[204,194,194,208,196,194,196,196,194],[194,198,194,428,196,194,196,196,194],4.575,[198,198,194,430,196,194,196,196,194],8.792,[201,198,194,432,196,194,196,196,194],5.521,[204,198,194,434,196,194,196,196,194],3.491,[194,201,194,436,196,194,196,196,194],0.292,[198,201,194,438,196,194,196,196,194],0.955,[201,201,194,440,196,194,196,196,194],0.609,[204,201,194,442,196,194,196,196,194],0.266,[194,204,194,444,196,194,196,196,194],0.898,[198,204,194,446,196,194,196,196,194],1.02,[201,204,194,448,196,194,196,196,194],0.895,[204,204,194,450,196,194,196,196,194],0.874,[194,194,198,452,196,194,196,196,194],35.308,[198,194,198,454,196,194,196,196,194],72.382,[201,194,198,456,196,194,196,196,194],58.211,[204,194,198,218,196,194,196,196,194],[194,198,198,459,196,194,196,196,194],7.473,[198,198,198,461,196,194,196,196,194],4.683,[201,198,198,463,196,194,196,196,194],4.722,[204,198,198,465,196,194,196,196,194],3.522,[194,201,198,467,196,194,196,196,194],0.608,[198,201,198,469,196,194,196,196,194],3.665,[201,201,198,471,196,194,196,196,194],1.531,[204,201,198,473,196,194,196,196,194],0.565,[194,204,198,475,196,194,196,196,194],1.179,[198,204,198,477,196,194,196,196,194],1.104,[201,204,198,479,196,194,196,196,194],1.167,[204,204,198,481,196,194,196,196,194],1.084,[194,194,201,483,196,194,196,196,194],6.951,[198,194,201,485,196,194,196,196,194],33.801,[201,194,201,487,196,194,196,196,194],21.753,[204,194,201,228,196,194,196,196,194],[194,198,201,490,196,194,196,196,194],4.194,[198,198,201,492,196,194,196,196,194],6.657,[201,198,201,494,196,194,196,196,194],4.773,[204,198,201,465,196,194,196,196,194],[194,201,201,497,196,194,196,196,194],0.996,[198,201,201,499,196,194,196,196,194],1.31,[201,201,201,501,196,194,196,196,194],1.159,[204,201,201,473,196,194,196,196,194],[194,204,201,504,196,194,196,196,194],1.088,[198,204,201,506,196,194,196,196,194],1.07,[201,204,201,508,196,194,196,196,194],1.089,[204,204,201,481,196,194,196,196,194],[],[401],[],[],[515],"Authors' city dataset (OS2-128, Livox Avia, Livox Tele, 100 Hz IMU, PTP time reference); INS ground truth; RMSE of ATE and RTE via evo",{"slug":517,"group":518,"sourceId":5,"sourceLabel":6,"table":147,"selfRows":519,"metrics":520,"seqs":525,"entrants":539,"cells":541,"outcomes":554,"locators":555,"hardware":556,"wordings":558,"notes":559},"malio2023-table-vi","malio2023:Table VI",6,[521],{"label":522,"unit":523,"statistic":524,"alignment":80},"Total time per scan","ms","mean",[526,529,531,533,535,537],{"dataset":107,"sequence":527,"environment":528},"Whampoa, 1 LiDAR (HDL-32E, 4045 points after downsampling)","per scan",{"dataset":107,"sequence":530,"environment":528},"Whampoa, 2 LiDARs (+VLP-16, 5647 points)",{"dataset":107,"sequence":532,"environment":528},"Whampoa, 3 LiDARs (+LS-C16, 6784 points)",{"dataset":122,"sequence":534,"environment":528},"City01, 1 LiDAR (OS2-128, 8554 points)",{"dataset":122,"sequence":536,"environment":528},"City01, 2 LiDARs (+Avia, 10172 points)",{"dataset":122,"sequence":538,"environment":528},"City01, 3 LiDARs (+Tele, 12244 points)",[540],{"name":7,"methodId":5,"linkable":86,"proposed":86,"self":86},[542,544,546,548,550,552],[194,194,194,543,196,194,194,196,194],28.1,[194,194,198,545,196,194,194,196,194],38.6,[194,194,201,547,196,194,194,196,194],47.7,[194,194,204,549,196,194,194,196,194],46.4,[194,194,207,551,196,194,194,196,194],56.4,[194,194,155,553,196,194,194,196,194],79,[],[147],[557],"Intel i7 CPU @ 2.50 GHz, 48 GB RAM",[],[560],"Average processing time per scan versus number of LiDARs (0.4 m downsampling); totals only stored",[562],{"group":563,"slug":564,"sourceLabel":6,"table":565,"selfRows":155,"datasets":566},"malio2023:Table II","malio2023-table-ii","Table II",[92],1790510658814]