[{"data":1,"prerenderedAt":583},["ShallowReactive",2],{"method-rloam2021":3},{"method":4,"reference":51,"equipment":71,"figures":100,"results":101},{"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":27,"sensors":34,"platform":36,"estimator":38,"association":39,"timeModel":40,"deskew":41,"loopClosure":42,"globalOptimization":43,"mapRepresentation":44,"prior":45,"outputGeometry":46,"compute":47,"codeUrl":48,"codeLicense":49,"relatedVersions":50},"rloam2021","Oelsch et al., 2021","R-LOAM","R-LOAM: Improving LiDAR Odometry and Mapping With Point-to-Mesh Features of a Known 3D Reference Object",2021,"recent","C04","localization_in_prior_map_or_bim","R-LOAM 延伸 LOAM（A-LOAM 實作）的建圖模組：假設環境中有一個幾何與全域位姿皆已知的參考物件，先以物件包圍盒裁切掃描點，再透過 AABB 樹找出每個掃描點在三角網格上的最近虛擬點，形成點到網格（point-to-mesh）殘差，與 LOAM 的角點、面點殘差經正規化後共同最佳化，網格權重隨迭代次數以對數方式增加。驗證在 Gazebo 模擬的機庫 B737、廂型車與艾菲爾鐵塔三種情境，分別模擬 Velodyne VLP-16 與 Ouster OS1-128；相對 LOAM，三種情境的中位數 APE 平均降幅分別超過 92%、69% 與 94%，但需要 15 至 35 次迭代，且完全依賴網格與物件位姿的正確性。","R-LOAM adds normalized point-to-mesh residuals, found via bounding-box scan isolation and AABB-tree closest-point search on a known, globally placed CAD mesh, to A-LOAM's mapping optimization; in Gazebo simulations with VLP-16 and OS1-128 it reduced median APE versus LOAM by over 92%, 69% and 94% on average in the three scenarios, at the cost of 15-35 optimization iterations and a perfect-model assumption.","full_text_reviewed","peer_reviewed_published","supplementary","作者在相關研究回顧營建工地的 In situ Fabricator 與以 CAD 建築模型定位末端執行器的機器人（Sec. II-B），並在限制一節指出 BIM 相關設備可產生次毫米精度的三維模型（Sec. VI）。本文驗證僅在 Gazebo 模擬的機庫飛機與艾菲爾鐵塔目視檢測情境，未在營建工地或以真實 BIM 模型驗證；（推論）以已知網格作為配準約束的做法可延伸到 BIM 先驗，但竣工幾何與 BIM 的偏差會直接影響結果。",[20],"simulation",[22,23,24,25,26],"Average reduction in median APE over LOAM of over 92% (scenario 1), 69% (scenario 2) and 94% (scenario 3) (Sec. V)","scenario 1 VLP-16: median APE 2.0 cm at 35 iterations vs best LOAM 30.9 cm, median RE 0.09 vs 1.59 deg","OS1-128: 1.2 cm vs 13.2 cm (Table II)","with the VLP-16 at the Eiffel Tower LOAM fails while R-LOAM reaches 0.3 cm median APE (Table IV)","gains remain with a small reference object (van), where only 22% (VLP-16) or 45% (OS1-128) of scans have more than 100 mesh features (Sec. V, Table III)",[28,29,30,31,32,33],"Assumes a perfect mesh and exact object pose","mesh imperfections or object pose errors directly degrade R-LOAM and a large error could make it worse than LOAM (Sec. VI)","validated only in Gazebo simulation with added Gaussian noise (Sec. IV-A)","scan isolation assumes no other objects inside the object's bounding box (Sec. III-B1)","best results need 15-35 map optimization iterations, beyond LOAM's default 2, which the authors say requires remote computing or post-processing (Sec. V)","in scenario 2 (van) the VLP-16 error rose again after 15 iterations (Table III)",[35],"Simulated Velodyne VLP-16 (16 lines, up to 30,000 points per scan, 10 Hz, Gaussian noise sigma 0.03) and simulated Ouster OS1-128 (128 lines, up to 262,144 points per scan, 10 Hz, sigma 0.05), one at a time, on a 1-DoF gimbal tilting between -0.6 and +0.6 rad on a quadcopter UAV in Gazebo (Sec. IV-A, Fig. 4)",[37],"quadcopter UAV with 1-DoF LiDAR gimbal in Gazebo simulation: manual and autonomous flights around a B737 in a hangar and around the lower Eiffel Tower (Sec. IV-A, Table I)","LOAM (A-LOAM) mapping optimization extended to a joint cost of normalized corner, surface and point-to-mesh residuals with Huber loss; mesh weight lambda raised logarithmically from 0.1 to 40 over up to 35 correspondence and optimization iterations; Ceres Levenberg-Marquardt trust region; A-LOAM's real-time abort was disabled so every scan is map-optimized (Sec. III-B3, IV-B)","LOAM corner and surface point correspondences plus point-to-mesh correspondences: scans are cropped to the object's bounding box plus a buffer (2 m, 200 m for the Eiffel Tower, ground removed below 0.5 or 1 m), then for each remaining point the closest virtual point on the triangular mesh is found via an AABB tree and the IGL library (Sec. III-B1, III-B2, IV-B)","discrete per-scan poses as in A-LOAM; no additional time model described","not_reported (the paper does not describe motion or gimbal-actuation compensation beyond LOAM defaults)","none; the paper notes LOAM performs no loop closure and R-LOAM does not add one (Sec. II-A, III)","none; drift is reduced by aligning each scan to the mesh within the per-scan map optimization (Sec. III-B3)","LOAM feature map of corner and surface points stored in a cube structure; map building unchanged (Sec. III-A, III-B3)","exact 3D triangular mesh (CAD) of the reference object, up to hundreds of thousands of faces (B737, van or Eiffel Tower), with its exact pose in the map frame assumed known before takeoff (Sec. I, II-B, VI)","per-scan 6-DoF poses and the LOAM feature map; improved 3D reconstruction is claimed but no map accuracy metric is reported (Sec. V, VII)","not_reported; high iteration counts (15-35) are said to need a remote multi-core SLAM setup or post-processing to be real-time (Sec. V)",null,"not_verified",[],{"id":5,"kind":52,"shortName":7,"title":8,"authors":53,"year":9,"venue":57,"venueType":58,"publisher":59,"volumeIssuePages":60,"doi":61,"arxivId":48,"url":62,"firstPublicDate":63,"publicationStatus":16,"metadataStatus":64,"fulltextStatus":15,"era":10,"classicReason":65,"codeUrl":48,"cluster":11,"topics":66,"mdpi":67,"verification":68,"label":6,"fulltextRoute":69,"versionRead":70,"addedByCensus":67},"method",[54,55,56],"Martin Oelsch","Mojtaba Karimi","Eckehard Steinbach","IEEE Robotics and Automation Letters","journal","IEEE","6(2): 2068-2075","10.1109\u002Flra.2021.3060413","https:\u002F\u002Fapi.semanticscholar.org\u002Fgraph\u002Fv1\u002Fpaper\u002FDOI:10.1109\u002FLRA.2021.3060413","2021-02-20","metadata_verified","not_applicable",[11],false,"corrected","NTU institutional (Chrome)","version of record: IEEE Xplore HTML full text, IEEE RA-L 6(2):2068-2075 (published 18 February 2021); Tables I-IV read from the publisher's table images",[72,80,85,91,95],{"category":73,"model":74,"canonical":75,"role":76,"dataset":77,"specs":78,"locator":79},"lidar","Velodyne VLP-16 (simulated in Gazebo)","Velodyne VLP-16","method input","R-LOAM simulated datasets 1, 3, 5","16 scan lines, up to 30,000 points per scan, 10 Hz, zero-mean Gaussian noise sigma 0.03","Sec. IV-A, Table I",{"category":73,"model":81,"canonical":82,"role":76,"dataset":83,"specs":84,"locator":79},"Ouster OS1-128 (simulated in Gazebo)","Ouster OS1-128","R-LOAM simulated datasets 2, 4, 6","128 scan lines, up to 262,144 points per scan, 10 Hz, zero-mean Gaussian noise sigma 0.05",{"category":86,"model":87,"canonical":87,"role":76,"dataset":88,"specs":89,"locator":90},"other","1-DoF gimbal (simulated)","R-LOAM simulated datasets","tilts the LiDAR back and forth between -0.6 and +0.6 rad throughout the flight","Sec. IV-A, Fig. 4",{"category":92,"model":93,"canonical":93,"role":76,"dataset":88,"specs":94,"locator":79},"platform","Quadcopter UAV (simulated in Gazebo)","manual flight in dataset 1; datasets 2-4 follow this trajectory with an autonomous flight controller, max 0.5 m\u002Fs (Sec. IV-A); control mode for the Eiffel Tower datasets 5-6 not stated",{"category":86,"model":96,"canonical":96,"role":97,"dataset":88,"specs":98,"locator":99},"Gazebo simulator ground-truth pose","reference or ground truth","6-DoF ground-truth pose of the LiDAR in the global frame","Sec. IV-A",[],{"totalRows":102,"groupCount":103,"groups":104,"others":582},75,4,[105,235,329,430],{"slug":106,"group":107,"sourceId":5,"sourceLabel":6,"table":108,"selfRows":109,"metrics":110,"seqs":119,"entrants":126,"cells":149,"outcomes":229,"locators":230,"hardware":231,"wordings":232,"notes":233},"rloam2021-table-ii","rloam2021:Table II","Table II",20,[111,116],{"label":112,"unit":113,"statistic":114,"alignment":115},"APE in cm, median","cm","median","not_reported",{"label":117,"unit":118,"statistic":114,"alignment":115},"RE (rotational error) in deg, median","deg",[120,124],{"dataset":121,"sequence":122,"environment":123},"R-LOAM Gazebo simulated datasets","Dataset 1: VLP-16, 15111 scans, 0.35 m\u002Fs, 514 m, manual flight","Scenario 1 (B737 in hangar, airplane as reference)",{"dataset":121,"sequence":125,"environment":123},"Dataset 2: OS1-128, 9905 scans, 0.48 m\u002Fs, 474 m",[127,131,133,135,137,139,141,143,145,147],{"name":128,"methodId":129,"linkable":130,"proposed":67,"self":67},"LOAM [1], #Iter 2 (def)","aloam_software",true,{"name":132,"methodId":129,"linkable":130,"proposed":67,"self":67},"LOAM [1], #Iter 5",{"name":134,"methodId":129,"linkable":130,"proposed":67,"self":67},"LOAM [1], #Iter 15",{"name":136,"methodId":129,"linkable":130,"proposed":67,"self":67},"LOAM [1], #Iter 25",{"name":138,"methodId":129,"linkable":130,"proposed":67,"self":67},"LOAM [1], #Iter 35",{"name":140,"methodId":5,"linkable":130,"proposed":130,"self":130},"R-LOAM, #Iter 2 (def)",{"name":142,"methodId":5,"linkable":130,"proposed":130,"self":130},"R-LOAM, #Iter 5",{"name":144,"methodId":5,"linkable":130,"proposed":130,"self":130},"R-LOAM, #Iter 15",{"name":146,"methodId":5,"linkable":130,"proposed":130,"self":130},"R-LOAM, #Iter 25",{"name":148,"methodId":5,"linkable":130,"proposed":130,"self":130},"R-LOAM, #Iter 35",[150,154,157,159,161,164,166,169,171,173,175,178,180,183,185,188,190,193,195,197,199,201,202,204,206,207,208,209,210,211,212,214,216,218,219,221,222,224,226,228],[151,151,151,152,153,151,153,153,151],0,50.5,-1,[151,155,151,156,153,151,153,153,151],1,2.2,[155,151,151,158,153,151,153,153,151],57.7,[155,155,151,160,153,151,153,153,151],2.56,[162,151,151,163,153,151,153,153,151],2,51.1,[162,155,151,165,153,151,153,153,151],2.12,[167,151,151,168,153,151,153,153,151],3,30.9,[167,155,151,170,153,151,153,153,151],1.59,[103,151,151,172,153,151,153,153,151],31.9,[103,155,151,174,153,151,153,153,151],1.61,[176,151,151,177,153,151,153,153,151],5,10.5,[176,155,151,179,153,151,153,153,151],0.4,[181,151,151,182,153,151,153,153,151],6,6.6,[181,155,151,184,153,151,153,153,151],0.31,[186,151,151,187,153,151,153,153,151],7,3.1,[186,155,151,189,153,151,153,153,151],0.25,[191,151,151,192,153,151,153,153,151],8,2.8,[191,155,151,194,153,151,153,153,151],0.11,[196,151,151,162,153,151,153,153,151],9,[196,155,151,198,153,151,153,153,151],0.09,[151,151,155,200,153,151,153,153,151],13.2,[151,155,155,198,153,151,153,153,151],[155,151,155,203,153,151,153,153,151],13.5,[155,155,155,205,153,151,153,153,151],0.08,[162,151,155,203,153,151,153,153,151],[162,155,155,205,153,151,153,153,151],[167,151,155,203,153,151,153,153,151],[167,155,155,205,153,151,153,153,151],[103,151,155,203,153,151,153,153,151],[103,155,155,205,153,151,153,153,151],[176,151,155,213,153,151,153,153,151],2.6,[176,155,155,215,153,151,153,153,151],0.13,[181,151,155,217,153,151,153,153,151],1.9,[181,155,155,198,153,151,153,153,151],[186,151,155,220,153,151,153,153,151],1.4,[186,155,155,205,153,151,153,153,151],[191,151,155,223,153,151,153,153,151],1.3,[191,155,155,225,153,151,153,153,151],0.07,[196,151,155,227,153,151,153,153,151],1.2,[196,155,155,225,153,151,153,153,151],[],[108],[],[],[234],"Scenario 1, airplane as reference object; APE (cm) and RE (deg) against Gazebo ground truth for 2 to 35 correspondence and optimization iterations; LOAM = A-LOAM",{"slug":236,"group":237,"sourceId":5,"sourceLabel":6,"table":238,"selfRows":109,"metrics":239,"seqs":242,"entrants":248,"cells":259,"outcomes":323,"locators":324,"hardware":325,"wordings":326,"notes":327},"rloam2021-table-iii","rloam2021:Table III","Table III",[240,241],{"label":112,"unit":113,"statistic":114,"alignment":115},{"label":117,"unit":118,"statistic":114,"alignment":115},[243,246],{"dataset":121,"sequence":244,"environment":245},"Dataset 3: VLP-16, 9718 scans, 0.49 m\u002Fs, 474 m","Scenario 2 (B737 in hangar, van as reference)",{"dataset":121,"sequence":247,"environment":245},"Dataset 4: OS1-128, 9726 scans, 0.49 m\u002Fs, 474 m",[249,250,251,252,253,254,255,256,257,258],{"name":128,"methodId":129,"linkable":130,"proposed":67,"self":67},{"name":132,"methodId":129,"linkable":130,"proposed":67,"self":67},{"name":134,"methodId":129,"linkable":130,"proposed":67,"self":67},{"name":136,"methodId":129,"linkable":130,"proposed":67,"self":67},{"name":138,"methodId":129,"linkable":130,"proposed":67,"self":67},{"name":140,"methodId":5,"linkable":130,"proposed":130,"self":130},{"name":142,"methodId":5,"linkable":130,"proposed":130,"self":130},{"name":144,"methodId":5,"linkable":130,"proposed":130,"self":130},{"name":146,"methodId":5,"linkable":130,"proposed":130,"self":130},{"name":148,"methodId":5,"linkable":130,"proposed":130,"self":130},[260,262,264,266,268,270,272,274,276,278,279,281,283,285,287,289,291,292,293,294,295,297,298,300,302,304,305,306,307,308,309,310,311,313,314,316,318,320,321,322],[151,151,151,261,153,151,153,153,151],19,[151,155,151,263,153,151,153,153,151],0.83,[155,151,151,265,153,151,153,153,151],65,[155,155,151,267,153,151,153,153,151],3.08,[162,151,151,269,153,151,153,153,151],24,[162,155,151,271,153,151,153,153,151],1.63,[167,151,151,273,153,151,153,153,151],23.8,[167,155,151,275,153,151,153,153,151],1.62,[103,151,151,277,153,151,153,153,151],23.9,[103,155,151,275,153,151,153,153,151],[176,151,151,280,153,151,153,153,151],17.9,[176,155,151,282,153,151,153,153,151],0.81,[181,151,151,284,153,151,153,153,151],7.3,[181,155,151,286,153,151,153,153,151],0.44,[186,151,151,288,153,151,153,153,151],6.3,[186,155,151,290,153,151,153,153,151],0.37,[191,151,151,203,153,151,153,153,151],[191,155,151,290,153,151,153,153,151],[196,151,151,203,153,151,153,153,151],[196,155,151,290,153,151,153,153,151],[151,151,155,296,153,151,153,153,151],14.8,[151,155,155,225,153,151,153,153,151],[155,151,155,299,153,151,153,153,151],15.3,[155,155,155,301,153,151,153,153,151],0.06,[162,151,155,303,153,151,153,153,151],15.4,[162,155,155,301,153,151,153,153,151],[167,151,155,303,153,151,153,153,151],[167,155,155,301,153,151,153,153,151],[103,151,155,303,153,151,153,153,151],[103,155,155,301,153,151,153,153,151],[176,151,155,288,153,151,153,153,151],[176,155,155,198,153,151,153,153,151],[181,151,155,312,153,151,153,153,151],4.9,[181,155,155,301,153,151,153,153,151],[186,151,155,315,153,151,153,153,151],4.3,[186,155,155,317,153,151,153,153,151],0.05,[191,151,155,319,153,151,153,153,151],4.2,[191,155,155,317,153,151,153,153,151],[196,151,155,319,153,151,153,153,151],[196,155,155,317,153,151,153,153,151],[],[238],[],[],[328],"Scenario 2, van as small reference object; APE (cm) and RE (deg) against Gazebo ground truth for 2 to 35 iterations; LOAM = A-LOAM",{"slug":330,"group":331,"sourceId":5,"sourceLabel":6,"table":332,"selfRows":109,"metrics":333,"seqs":336,"entrants":342,"cells":353,"outcomes":423,"locators":425,"hardware":426,"wordings":427,"notes":428},"rloam2021-table-iv","rloam2021:Table IV","Table IV",[334,335],{"label":112,"unit":113,"statistic":114,"alignment":115},{"label":117,"unit":118,"statistic":114,"alignment":115},[337,340],{"dataset":121,"sequence":338,"environment":339},"Dataset 5: VLP-16, 5674 scans, 0.51 m\u002Fs, 291 m","Scenario 3 (Eiffel Tower, tower as reference)",{"dataset":121,"sequence":341,"environment":339},"Dataset 6: OS1-128, 5762 scans, 0.51 m\u002Fs, 291 m",[343,344,345,346,347,348,349,350,351,352],{"name":128,"methodId":129,"linkable":130,"proposed":67,"self":67},{"name":132,"methodId":129,"linkable":130,"proposed":67,"self":67},{"name":134,"methodId":129,"linkable":130,"proposed":67,"self":67},{"name":136,"methodId":129,"linkable":130,"proposed":67,"self":67},{"name":138,"methodId":129,"linkable":130,"proposed":67,"self":67},{"name":140,"methodId":5,"linkable":130,"proposed":130,"self":130},{"name":142,"methodId":5,"linkable":130,"proposed":130,"self":130},{"name":144,"methodId":5,"linkable":130,"proposed":130,"self":130},{"name":146,"methodId":5,"linkable":130,"proposed":130,"self":130},{"name":148,"methodId":5,"linkable":130,"proposed":130,"self":130},[354,356,358,360,362,364,366,368,370,372,374,376,378,380,382,384,386,388,389,390,392,394,396,398,400,402,404,405,407,409,410,412,414,415,416,417,418,420,421,422],[151,151,151,355,151,151,153,153,151],499.3,[151,155,151,357,151,151,153,153,151],2.47,[155,151,151,359,151,151,153,153,151],380.2,[155,155,151,361,151,151,153,153,151],4.08,[162,151,151,363,151,151,153,153,151],1420.9,[162,155,151,365,151,151,153,153,151],17.28,[167,151,151,367,151,151,153,153,151],1326.4,[167,155,151,369,151,151,153,153,151],16.4,[103,151,151,371,151,151,153,153,151],1338.5,[103,155,151,373,151,151,153,153,151],18.21,[176,151,151,375,153,151,153,153,151],31.5,[176,155,151,377,153,151,153,153,151],0.29,[181,151,151,379,153,151,153,153,151],2.4,[181,155,151,381,153,151,153,153,151],0.04,[186,151,151,383,153,151,153,153,151],0.5,[186,155,151,385,153,151,153,153,151],0.02,[191,151,151,387,153,151,153,153,151],0.3,[191,155,151,385,153,151,153,153,151],[196,151,151,387,153,151,153,153,151],[196,155,151,391,153,151,153,153,151],0.01,[151,151,155,393,153,151,153,153,151],1058.2,[151,155,155,395,153,151,153,153,151],4.97,[155,151,155,397,153,151,153,153,151],105,[155,155,155,399,153,151,153,153,151],1.49,[162,151,155,401,153,151,153,153,151],9.3,[162,155,155,403,153,151,153,153,151],0.14,[167,151,155,177,153,151,153,153,151],[167,155,155,406,153,151,153,153,151],0.1,[103,151,155,408,153,151,153,153,151],11.3,[103,155,155,194,153,151,153,153,151],[176,151,155,411,153,151,153,153,151],6.4,[176,155,155,413,153,151,153,153,151],0.12,[181,151,155,379,153,151,153,153,151],[181,155,155,205,153,151,153,153,151],[186,151,155,227,153,151,153,153,151],[186,155,155,225,153,151,153,153,151],[191,151,155,419,153,151,153,153,151],1.1,[191,155,155,225,153,151,153,153,151],[196,151,155,155,153,151,153,153,151],[196,155,155,225,153,151,153,153,151],[424],"failed (authors state LOAM fails in this scenario with a VLP-16)",[332],[],[],[429],"Scenario 3, Eiffel Tower as reference object; APE (cm) and RE (deg) against Gazebo ground truth for 2 to 35 iterations; LOAM = A-LOAM; authors state LOAM fails with the VLP-16",{"slug":431,"group":432,"sourceId":433,"sourceLabel":434,"table":332,"selfRows":435,"metrics":436,"seqs":452,"entrants":461,"cells":470,"outcomes":575,"locators":576,"hardware":577,"wordings":579,"notes":580},"roloam2022-table-iv","roloam2022:Table IV","roloam2022","Oelsch et al., 2022",15,[437,440,443,445,448],{"label":438,"unit":113,"statistic":439,"alignment":115},"APE mapping (cm), max","max",{"label":441,"unit":113,"statistic":442,"alignment":115},"APE mapping (cm), mean","mean",{"label":444,"unit":113,"statistic":114,"alignment":115},"APE mapping (cm), median",{"label":446,"unit":113,"statistic":447,"alignment":115},"APE mapping (cm), RMSE","RMSE",{"label":449,"unit":450,"statistic":442,"alignment":451},"Freq. (Hz) mapping","Hz","none",[453,457,459],{"dataset":454,"sequence":455,"environment":456},"RO-LOAM hangar datasets (octocopter, VLP-16, Leica MS60 ground truth)","Dataset 1","aircraft hangar, one side of a B737 (visual inspection)",{"dataset":454,"sequence":458,"environment":456},"Dataset 2",{"dataset":454,"sequence":460,"environment":456},"Dataset 3",[462,464,466,468],{"name":463,"methodId":129,"linkable":130,"proposed":67,"self":67},"LOAM [1]",{"name":465,"methodId":433,"linkable":130,"proposed":130,"self":67},"LOAM + RO",{"name":467,"methodId":5,"linkable":130,"proposed":67,"self":130},"R-LOAM [2]",{"name":469,"methodId":433,"linkable":130,"proposed":130,"self":67},"R-LOAM + RO",[471,473,475,477,479,481,483,485,487,489,490,492,494,496,498,499,501,503,505,506,507,509,511,513,515,517,519,521,522,524,525,527,529,530,532,533,534,536,538,540,541,543,545,547,549,551,553,554,556,558,559,561,563,564,566,567,569,570,572,574],[151,151,151,472,153,151,153,153,151],466.3,[151,155,151,474,153,151,153,153,151],134.6,[151,162,151,476,153,151,153,153,151],71.1,[151,167,151,478,153,151,153,153,151],182.7,[151,103,151,480,153,151,151,153,151],3.3,[155,151,151,482,153,151,153,153,151],84.2,[155,155,151,484,153,151,153,153,151],8.9,[155,162,151,486,153,151,153,153,151],6.5,[155,167,151,488,153,151,153,153,151],13.6,[155,103,151,167,153,151,151,153,151],[162,151,151,491,153,151,153,153,151],50.7,[162,155,151,493,153,151,153,153,151],12.6,[162,162,151,495,153,151,153,153,151],11.5,[162,167,151,497,153,151,153,153,151],14.4,[162,103,151,187,153,151,151,153,151],[167,151,151,500,153,151,153,153,151],57.8,[167,155,151,502,153,151,153,153,151],8.1,[167,162,151,504,153,151,153,153,151],5.9,[167,167,151,177,153,151,153,153,151],[167,103,151,187,153,151,151,153,151],[151,151,155,508,153,151,153,153,151],423.2,[151,155,155,510,153,151,153,153,151],117.1,[151,162,155,512,153,151,153,153,151],67.2,[151,167,155,514,153,151,153,153,151],164.7,[151,103,155,516,153,151,151,153,151],2.9,[155,151,155,518,153,151,153,153,151],89.7,[155,155,155,520,153,151,153,153,151],9.6,[155,162,155,411,153,151,153,153,151],[155,167,155,523,153,151,153,153,151],15.7,[155,103,155,213,153,151,151,153,151],[162,151,155,526,153,151,153,153,151],58.4,[162,155,155,528,153,151,153,153,151],10.3,[162,162,155,520,153,151,153,153,151],[162,167,155,531,153,151,153,153,151],12.4,[162,103,155,192,153,151,151,153,151],[167,151,155,265,153,151,153,153,151],[167,155,155,535,153,151,153,153,151],7.2,[167,162,155,537,153,151,153,153,151],5.2,[167,167,155,539,153,151,153,153,151],10.4,[167,103,155,213,153,151,151,153,151],[151,151,162,542,153,151,153,153,151],354.8,[151,155,162,544,153,151,153,153,151],123.8,[151,162,162,546,153,151,153,153,151],80.6,[151,167,162,548,153,151,153,153,151],154.8,[151,103,162,550,153,151,151,153,151],3.2,[155,151,162,552,153,151,153,153,151],88.4,[155,155,162,200,153,151,153,153,151],[155,162,162,555,153,151,153,153,151],6.8,[155,167,162,557,153,151,153,153,151],21.8,[155,103,162,516,153,151,151,153,151],[162,151,162,560,153,151,153,153,151],69.7,[162,155,162,562,153,151,153,153,151],16.9,[162,162,162,299,153,151,153,153,151],[162,167,162,565,153,151,153,153,151],19.4,[162,103,162,167,153,151,151,153,151],[167,151,162,568,153,151,153,153,151],54.8,[167,155,162,401,153,151,153,153,151],[167,162,162,571,153,151,153,153,151],6.7,[167,167,162,573,153,151,153,153,151],12.2,[167,103,162,167,153,151,151,153,151],[],[332],[578],"server with 32 Intel Xeon CPUs E5-2690 @2.90 GHz and 132 GB memory (Sec. IV-B)",[],[581],"Three hangar datasets with M = 9 and L = 15; means of 5 online runs; APE mapping = map-optimized poses, APE TMO = poses used for TMO; 3-DoF position ground truth from Leica MS60",[],1790510662411]