[{"data":1,"prerenderedAt":972},["ShallowReactive",2],{"method-coinlio2024":3},{"method":4,"reference":54,"equipment":77,"figures":109,"results":149},{"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":23,"limitations":28,"sensors":31,"platform":34,"estimator":36,"association":37,"timeModel":38,"deskew":39,"loopClosure":40,"globalOptimization":40,"mapRepresentation":41,"prior":40,"outputGeometry":42,"compute":43,"codeUrl":44,"codeLicense":45,"relatedVersions":46},"coinlio2024","Pfreundschuh et al., 2024","COIN-LIO","COIN-LIO: Complementary Intensity-Augmented LiDAR Inertial Odometry",2024,"recent","C05","odometry_with_local_mapping","COIN-LIO 以 FAST-LIO2 的點到平面配準為基礎，將 LiDAR 強度回波投影為強度影像並做亮度一致化濾波，再把影像區塊的光度誤差（photometric error）一併放入迭代擴展卡爾曼濾波。作者偵測點雲配準中資訊不足的方向，刻意挑選能在該方向提供互補資訊的影像區塊，以改善隧道、平坦場地等幾何退化場景的穩健性。作者並公開以全測站量測地面真值的 ENWIDE 退化場景資料集。","Augments FAST-LIO2 with photometric residuals on filtered LiDAR intensity images, selecting patches that constrain geometrically uninformative directions, to improve robustness in tunnels and flat fields.","full_text_reviewed","peer_reviewed_published","background","ENWIDE 以手持 Ouster OS0-128 錄製五種場景：Tunnel（城市、室內）、Intersection（城市、戶外）、Runway（戶外、城市）、Field 與 Katzensee（戶外、自然環境）；地面真值由 Leica MS60 全測站量測，約 3 cm 精度。隧道情境與地下工程相關（推論），但非施工中工地。",[20,21,22],"public_benchmark","underground_or_tunnel","independent_reference",[24,25,26,27],"Drastically improved robustness on ENWIDE degenerate sequences where compared baselines fail or drift (Sec. IV-C)","Slightly better than baselines on geometry-rich Newer College (Sec. V)","Only compared method without a failure mark on all ten ENWIDE sequences; on Newer College Stairs only COIN-LIO and MD-SLAM avoided failure, with COIN-LIO at 0.102 m versus 0.340 m ATE (Tables I and II)","Ablation: filtered images with geometrically complementary patch selection gave the lowest ATE on all three ablation sequences (Table III)",[29,30],"Requires high-resolution LiDARs to form dense intensity images (Sec. V)","LIO approaches can still fail over long degenerate segments due to IMU noise and bias drift (Sec. IV-C, discussing baselines)",[32,33],"high-resolution 3D LiDAR with intensity (Ouster OS0-128)","IMU",[35],"handheld","iterated extended Kalman filter built on FAST-LIO2, fusing IMU, point-to-plane registration and photometric error on intensity-image patches","point-to-plane geometry plus photometric patch residuals on brightness-filtered intensity images; patches selected to be informative in directions where geometry is uninformative","discrete poses (as FAST-LIO2)","IMU-propagated undistortion of each point to the scan-end time as in FAST-LIO2 (Sec. III-B); for photometric residuals, tracked points are projected into the distorted LiDAR frame using a projection-based undistortion map that returns each pixel's point index and timestamp (Sec. III-G)","none","ikd-Tree point map inherited from FAST-LIO2 plus a feature map of tracked 5 x 5 intensity patches whose pixels are each initialised at their own global 3D position","odometry and point map; export format not_reported","CPU real time; on average 29.7 ms per frame (33 Hz) on an Intel i7-11800H mobile CPU on the Newer College Park sequence, of which 6.2 ms is spent on the photometric components","https:\u002F\u002Fgithub.com\u002Fethz-asl\u002FCOIN-LIO","BSD-3-Clause (LICENSE file checked)",[47,51],{"relation":48,"title":49,"doi_or_url":50},"preprint","COIN-LIO (arXiv v4, journal ref ICRA 2024)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.01235",{"relation":52,"title":53,"doi_or_url":44},"code_release","ethz-asl\u002FCOIN-LIO",{"id":5,"kind":55,"shortName":7,"title":8,"authors":56,"year":9,"venue":62,"venueType":63,"publisher":64,"volumeIssuePages":65,"doi":66,"arxivId":67,"url":68,"firstPublicDate":69,"publicationStatus":16,"metadataStatus":70,"fulltextStatus":15,"era":10,"classicReason":71,"codeUrl":44,"cluster":11,"topics":72,"mdpi":73,"verification":74,"label":6,"fulltextRoute":75,"versionRead":76,"addedByCensus":73},"method",[57,58,59,60,61],"Patrick Pfreundschuh","Helen Oleynikova","Cesar Cadena","Roland Siegwart","Olov Andersson","2024 IEEE International Conference on Robotics and Automation (ICRA)","conference","IEEE","pp. 1730-1737","10.1109\u002Ficra57147.2024.10610938","2310.01235","https:\u002F\u002Fapi.crossref.org\u002Fworks?query.bibliographic=COIN-LIO...","2023-10-02","metadata_verified","not_applicable",[11],false,"corrected","arXiv","arXiv v4 (2024-05-30), journal-ref ICRA 2024 pp. 1730-1737; IEEE version of record not read",[78,85,89,94,98,103],{"category":79,"model":80,"canonical":80,"role":81,"dataset":82,"specs":83,"locator":84},"lidar","Ouster OS0","method input","ENWIDE","128 beam, hand-held, with integrated IMU; intensity returns projected into images","Sec. IV-B",{"category":86,"model":87,"canonical":87,"role":81,"dataset":82,"specs":88,"locator":84},"imu","Ouster OS0 integrated IMU (model not reported)","not_reported",{"category":90,"model":91,"canonical":91,"role":92,"dataset":82,"specs":93,"locator":84},"total_station","Leica MS60","reference or ground truth","ground-truth positions with approximately 3 cm accuracy",{"category":95,"model":96,"canonical":96,"role":81,"dataset":82,"specs":97,"locator":84},"platform","hand-held rig (not further described)","walking (smooth) and running (dynamic) sequences",{"category":79,"model":80,"canonical":80,"role":99,"dataset":100,"specs":101,"locator":102},"dataset sensor","Newer College Dataset (multi-camera extension)","128-beam, hand-held","Sec. IV-A",{"category":104,"model":105,"canonical":105,"role":106,"dataset":107,"specs":108,"locator":102},"compute","Intel i7-11800H","compute for runtime",null,"mobile CPU",[110,123,131,141],{"refId":5,"refLabel":6,"fig":111,"whatZh":112,"license":113,"licenseUrl":114,"sourceUrl":115,"src":116,"width":117,"height":118,"thumb":119,"thumbWidth":120,"thumbHeight":121,"modified":122},"Fig. 1","隧道場景的累積點雲依強度著色並疊加 COIN-LIO 軌跡；連結僅為 Fig. 1 上方子圖，中段的追蹤特徵與下方俯視點雲為另外的子圖","CC BY 4.0","https:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby\u002F4.0\u002F","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2310.01235v4\u002Fimages\u002Ftunnel_orange.png","\u002Ffigure-files\u002Fcoinlio2024\u002Ffig-1.webp",1400,670,"\u002Ffigure-files\u002Fcoinlio2024\u002Ffig-1.thumb.webp",480,230,"resized to at most 1400 px wide and converted to WebP",{"refId":5,"refLabel":6,"fig":124,"whatZh":125,"license":113,"licenseUrl":114,"sourceUrl":126,"src":127,"width":117,"height":128,"thumb":129,"thumbWidth":120,"thumbHeight":130,"modified":122},"Fig. 2","系統架構圖：點雲同時用於幾何配準與投影強度影像的光度誤差，兩種殘差在迭代擴展卡爾曼濾波中合併更新","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2310.01235v4\u002Fpipeline.png","\u002Ffigure-files\u002Fcoinlio2024\u002Ffig-2.webp",391,"\u002Ffigure-files\u002Fcoinlio2024\u002Ffig-2.thumb.webp",134,{"refId":5,"refLabel":6,"fig":132,"whatZh":133,"license":113,"licenseUrl":114,"sourceUrl":134,"src":135,"width":136,"height":137,"thumb":138,"thumbWidth":120,"thumbHeight":139,"modified":140},"Fig. 4","原始強度影像、反射率影像與本文濾波影像的比較，含草地與隧道的局部放大","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2310.01235v4\u002Fimages\u002Fimage_details_annotated.png","\u002Ffigure-files\u002Fcoinlio2024\u002Ffig-4.webp",1024,610,"\u002Ffigure-files\u002Fcoinlio2024\u002Ffig-4.thumb.webp",286,"converted to WebP",{"refId":5,"refLabel":6,"fig":142,"whatZh":143,"license":113,"licenseUrl":114,"sourceUrl":144,"src":145,"width":117,"height":146,"thumb":147,"thumbWidth":120,"thumbHeight":148,"modified":122},"Fig. 6","COIN-LIO 在 ENWIDE 的 FieldS、IntersectionS、KatzenseeS 與 RunwayS 所建立的點雲地圖","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2310.01235v4\u002Fimages\u002Fmaps_result_short.png","\u002Ffigure-files\u002Fcoinlio2024\u002Ffig-6.webp",1190,"\u002Ffigure-files\u002Fcoinlio2024\u002Ffig-6.thumb.webp",408,{"totalRows":150,"groupCount":151,"groups":152,"others":944},75,9,[153,449,699,758],{"slug":154,"group":155,"sourceId":5,"sourceLabel":6,"table":156,"selfRows":157,"metrics":158,"seqs":166,"entrants":197,"cells":216,"outcomes":393,"locators":444,"hardware":445,"wordings":446,"notes":447},"coinlio2024-table-ii","coinlio2024:Table II","Table II",20,[159,163],{"label":160,"unit":161,"statistic":162,"alignment":88},"Absolute Trajectory Error (RMSE)","m","RMSE",{"label":164,"unit":165,"statistic":88,"alignment":88},"Relative Error (RTE over 10 m segments)","%",[167,170,173,176,179,182,185,188,191,194],{"dataset":82,"sequence":168,"environment":169},"TunnelS (251.58 m)","urban, indoor tunnel, smooth walking",{"dataset":82,"sequence":171,"environment":172},"TunnelD (179.71 m)","urban, indoor tunnel, dynamic running",{"dataset":82,"sequence":174,"environment":175},"IntersectionS (279.28 m)","urban, outdoor, smooth",{"dataset":82,"sequence":177,"environment":178},"IntersectionD (388.47 m)","urban, outdoor, dynamic",{"dataset":82,"sequence":180,"environment":181},"RunwayS (333.57 m)","outdoor, urban runway, smooth",{"dataset":82,"sequence":183,"environment":184},"RunwayD (357.14 m)","outdoor, urban runway, dynamic",{"dataset":82,"sequence":186,"environment":187},"FieldS (232.70 m)","outdoor, nature field, smooth",{"dataset":82,"sequence":189,"environment":190},"FieldD (287.91 m)","outdoor, nature field, dynamic",{"dataset":82,"sequence":192,"environment":193},"KatzenseeS (242.88 m)","outdoor, nature, smooth",{"dataset":82,"sequence":195,"environment":196},"KatzenseeD (177.20 m)","outdoor, nature, dynamic",[198,202,204,206,209,212,214],{"name":199,"methodId":200,"linkable":201,"proposed":73,"self":73},"KISS-ICP [10]","kissicp2023",true,{"name":203,"methodId":107,"linkable":73,"proposed":73,"self":73},"MD-SLAM [6]",{"name":205,"methodId":107,"linkable":73,"proposed":73,"self":73},"Du and Beltrame [8]",{"name":207,"methodId":208,"linkable":201,"proposed":73,"self":73},"LIO-SAM [12]","liosam2020",{"name":210,"methodId":211,"linkable":201,"proposed":73,"self":73},"FAST-LIO2 [1]","fastlio2_2022",{"name":213,"methodId":107,"linkable":73,"proposed":73,"self":73},"RI-LIO [7]",{"name":215,"methodId":5,"linkable":201,"proposed":201,"self":201},"Ours 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2662.983%)","failed (marked x; RTE 2022.878%)","failed (marked x; RTE 2362.314%)","failed (marked x; RTE 2334.174%)","failed (marked x; RTE 3984.588%)","failed (marked x; RTE 2196.344%)","failed (marked x; RTE 1999.968%)","failed (marked x; RTE 1485.377%)","failed (marked x; RTE 316.12%)","failed (marked x; RTE 81.31%)","failed (marked x; RTE 53.64%)","failed (marked x; RTE 59.84%)","failed (marked x; RTE 70.32%)","failed (marked x; RTE 63.02%)","failed (marked x; RTE 49.94%)","failed (marked x; RTE 188.83%)","failed (marked x; RTE 52.18%)","failed (marked x; RTE 79.16%)","failed (marked x; RTE 49.34%)","failed (marked x; RTE 154.19%)",[156],[],[],[448],"ENWIDE dataset, hand-held Ouster OS0-128 with integrated IMU; ground-truth positions from Leica MS60 (about 3 cm); ATE RMSE (m) \u002F RTE (%) over 10 m segments; RTE > 20% declared failed (x); RTE rows kept only for non-failed cells, failed-cell RTE is in the ATE row outcome",{"slug":450,"group":451,"sourceId":452,"sourceLabel":453,"table":454,"selfRows":248,"metrics":455,"seqs":458,"entrants":492,"cells":514,"outcomes":691,"locators":693,"hardware":695,"wordings":696,"notes":697},"chen2025geode-table-5","chen2025geode:Table 5","chen2025geode","Chen et al., 2025b","Table 5",[456],{"label":457,"unit":161,"statistic":88,"alignment":88},"ATE (m)",[459,463,465,467,469,471,473,475,477,479,481,483,485,488,490],{"dataset":460,"sequence":461,"environment":462},"GEODE","Metro tunnels, alpha (Velodyne VLP-16), Tunneling 3","metro tunnel, mine tunnelling method (translational degeneracy along the axis)",{"dataset":460,"sequence":464,"environment":462},"Metro tunnels, alpha (Velodyne VLP-16), Tunneling 4",{"dataset":460,"sequence":466,"environment":462},"Metro tunnels, alpha (Velodyne VLP-16), Tunneling 5",{"dataset":460,"sequence":468,"environment":462},"Metro tunnels, beta (Ouster OS1-64), Tunneling 2",{"dataset":460,"sequence":470,"environment":462},"Metro tunnels, beta (Ouster OS1-64), Tunneling 3",{"dataset":460,"sequence":472,"environment":462},"Metro tunnels, beta (Ouster OS1-64), Tunneling 4",{"dataset":460,"sequence":474,"environment":462},"Metro tunnels, beta (Ouster OS1-64), Tunneling 5",{"dataset":460,"sequence":476,"environment":462},"Metro tunnels, gamma (Livox AVIA), Tunneling 1",{"dataset":460,"sequence":478,"environment":462},"Metro tunnels, gamma (Livox AVIA), Tunneling 2",{"dataset":460,"sequence":480,"environment":462},"Metro tunnels, gamma (Livox AVIA), Tunneling 3",{"dataset":460,"sequence":482,"environment":462},"Metro tunnels, gamma (Livox AVIA), Tunneling 4",{"dataset":460,"sequence":484,"environment":462},"Metro tunnels, gamma (Livox AVIA), Tunneling 5",{"dataset":460,"sequence":486,"environment":487},"Stairs, alpha (Velodyne VLP-16), Stairs","building stairs and corridors across multiple floors (loop from the seventh floor)",{"dataset":460,"sequence":489,"environment":487},"Stairs, beta (Ouster OS1-64), Stairs",{"dataset":460,"sequence":491,"environment":487},"Stairs, gamma (Livox AVIA), Stairs",[493,494,496,499,502,505,508,511],{"name":7,"methodId":5,"linkable":201,"proposed":73,"self":201},{"name":495,"methodId":211,"linkable":201,"proposed":73,"self":73},"FAST-LIO2",{"name":497,"methodId":498,"linkable":201,"proposed":73,"self":73},"DLIO","dlio2023",{"name":500,"methodId":501,"linkable":201,"proposed":73,"self":73},"FAST-LIVO","fastlivo2022",{"name":503,"methodId":504,"linkable":201,"proposed":73,"self":73},"Coco-LIC","cocolic2023",{"name":506,"methodId":507,"linkable":201,"proposed":73,"self":73},"R3LIVE","r3live2022",{"name":509,"methodId":510,"linkable":201,"proposed":73,"self":73},"LVI-SAM","lvisam2021",{"name":512,"methodId":513,"linkable":201,"proposed":73,"self":73},"VINS-Fusion","vinsfusion2019",[515,516,518,520,522,523,525,526,528,529,531,533,535,536,538,539,540,541,542,544,546,547,549,551,553,554,555,557,559,561,562,563,565,567,568,569,570,571,572,574,575,577,578,579,581,582,583,584,585,587,588,589,590,591,593,595,597,598,600,602,603,605,607,609,610,611,613,615,617,619,621,623,624,625,626,628,629,631,633,634,635,636,637,639,640,641,643,645,646,647,648,650,651,652,654,655,657,658,660,662,664,665,667,668,670,672,674,676,678,680,681,682,683,684,685,686,687,688,689,690],[218,218,218,107,218,218,219,219,218],[221,218,218,517,219,218,219,219,218],0.21,[223,218,218,519,219,218,219,219,218],0.18,[225,218,218,521,219,218,219,219,218],0.2,[227,218,218,107,218,218,219,219,218],[229,218,218,524,219,218,219,219,218],0.59,[231,218,218,107,221,218,219,219,218],[233,218,218,527,219,218,219,219,218],47.66,[218,218,221,107,218,218,219,219,218],[221,218,221,530,219,218,219,219,218],0.24,[223,218,221,532,219,218,219,219,218],0.14,[225,218,221,534,219,218,219,219,218],0.19,[227,218,221,107,218,218,219,219,218],[229,218,221,537,219,218,219,219,218],0.25,[231,218,221,107,221,218,219,219,218],[233,218,221,107,221,218,219,219,218],[218,218,223,107,218,218,219,219,218],[221,218,223,534,219,218,219,219,218],[223,218,223,543,219,218,219,219,218],0.13,[225,218,223,545,219,218,219,219,218],0.17,[227,218,223,107,218,218,219,219,218],[229,218,223,548,219,218,219,219,218],11.44,[231,218,223,550,219,218,219,219,218],0.3,[233,218,223,552,219,218,219,219,218],0.69,[218,218,225,532,219,218,219,219,218],[221,218,225,532,219,218,219,219,218],[223,218,225,556,219,218,219,219,218],0.11,[225,218,225,558,219,218,219,219,218],0.12,[227,218,225,560,219,218,219,219,218],0.15,[229,218,225,532,219,218,219,219,218],[231,218,225,532,219,218,219,219,218],[233,218,225,564,219,218,219,219,218],2.31,[218,218,227,566,219,218,219,219,218],0.23,[221,218,227,545,219,218,219,219,218],[223,218,227,532,219,218,219,219,218],[225,218,227,560,219,218,219,219,218],[227,218,227,534,219,218,219,219,218],[229,218,227,521,219,218,219,219,218],[231,218,227,573,219,218,219,219,218],0.37,[233,218,227,107,221,218,219,219,218],[218,218,229,576,219,218,219,219,218],0.16,[221,218,229,545,219,218,219,219,218],[223,218,229,560,219,218,219,219,218],[225,218,229,580,219,218,219,219,218],0.27,[227,218,229,545,219,218,219,219,218],[229,218,229,537,219,218,219,219,218],[231,218,229,519,219,218,219,219,218],[233,218,229,107,221,218,219,219,218],[218,218,231,586,219,218,219,219,218],3.84,[221,218,231,558,219,218,219,219,218],[223,218,231,556,219,218,219,219,218],[225,218,231,543,219,218,219,219,218],[227,218,231,519,219,218,219,219,218],[229,218,231,592,219,218,219,219,218],0.26,[231,218,231,594,219,218,219,219,218],0.22,[233,218,231,596,219,218,219,219,218],0.36,[218,218,233,107,218,218,219,219,218],[221,218,233,599,219,218,219,219,218],1.16,[223,218,233,601,219,218,219,219,218],8.51,[225,218,233,596,219,218,219,219,218],[227,218,233,604,219,218,219,219,218],0.42,[229,218,233,606,219,218,219,219,218],2.83,[231,218,233,608,219,218,219,219,218],35.4,[233,218,233,107,221,218,219,219,218],[218,218,235,107,218,218,219,219,218],[221,218,235,612,219,218,219,219,218],1.88,[223,218,235,614,219,218,219,219,218],15.43,[225,218,235,616,219,218,219,219,218],1.56,[227,218,235,618,219,218,219,219,218],2.06,[229,218,235,620,219,218,219,219,218],86.51,[231,218,235,622,219,218,219,219,218],2.1,[233,218,235,107,221,218,219,219,218],[218,218,151,107,218,218,219,219,218],[221,218,151,534,219,218,219,219,218],[223,218,151,627,219,218,219,219,218],2.63,[225,218,151,521,219,218,219,219,218],[227,218,151,630,219,218,219,219,218],4.06,[229,218,151,632,219,218,219,219,218],63.05,[231,218,151,530,219,218,219,219,218],[233,218,151,107,221,218,219,219,218],[218,218,238,107,218,218,219,219,218],[221,218,238,560,219,218,219,219,218],[223,218,238,638,219,218,219,219,218],5.74,[225,218,238,543,219,218,219,219,218],[227,218,238,519,219,218,219,219,218],[229,218,238,642,219,218,219,219,218],57.46,[231,218,238,644,219,218,219,219,218],0.35,[233,218,238,107,221,218,219,219,218],[218,218,240,107,218,218,219,219,218],[221,218,240,580,219,218,219,219,218],[223,218,240,649,219,218,219,219,218],2.48,[225,218,240,558,219,218,219,219,218],[227,218,240,545,219,218,219,219,218],[229,218,240,653,219,218,219,219,218],1.3,[231,218,240,545,219,218,219,219,218],[233,218,240,656,219,218,219,219,218],22.03,[218,218,242,107,218,218,219,219,218],[221,218,242,659,219,218,219,219,218],4.69,[223,218,242,661,219,218,219,219,218],4.89,[225,218,242,663,219,218,219,219,218],3.28,[227,218,242,107,218,218,219,219,218],[229,218,242,666,219,218,219,219,218],4.54,[231,218,242,107,221,218,219,219,218],[233,218,242,669,219,218,219,219,218],3.66,[218,218,244,671,219,218,219,219,218],0.45,[221,218,244,673,219,218,219,219,218],0.38,[223,218,244,675,219,218,219,219,218],0.41,[225,218,244,677,219,218,219,219,218],0.4,[227,218,244,679,219,218,219,219,218],6.84,[229,218,244,107,221,218,219,219,218],[231,218,244,107,221,218,219,219,218],[233,218,244,524,219,218,219,219,218],[218,218,246,107,218,218,219,219,218],[221,218,246,107,221,218,219,219,218],[223,218,246,107,221,218,219,219,218],[225,218,246,107,221,218,219,219,218],[227,218,246,107,221,218,219,219,218],[229,218,246,107,221,218,219,219,218],[231,218,246,107,221,218,219,219,218],[233,218,246,107,221,218,219,219,218],[71,692],"failed",[694],"Table 5 (VoR)",[],[],[698],"ATE (m) per sequence, average of five runs, parameters not tuned per sequence; X = breakdown or error > 100 m; - = algorithm not adapted to this data. Only Metro tunnels and Stairs blocks extracted; shield-tunnel sequences are absent from Table 5 because all methods failed (Sec. 5.2). Stairs GT from PALoc (inlier RMSE 0.07 m alpha, 0.08 m beta); gamma stairs GT not accurate.",{"slug":700,"group":701,"sourceId":5,"sourceLabel":6,"table":702,"selfRows":248,"metrics":703,"seqs":705,"entrants":713,"cells":724,"outcomes":751,"locators":753,"hardware":754,"wordings":755,"notes":756},"coinlio2024-table-iii","coinlio2024:Table III","Table III",[704],{"label":160,"unit":161,"statistic":162,"alignment":88},[706,709,711],{"dataset":82,"sequence":707,"environment":708},"TunnelD","urban, indoor tunnel, dynamic",{"dataset":82,"sequence":710,"environment":175},"IntersectionS",{"dataset":82,"sequence":712,"environment":196},"KatzenseeD",[714,716,718,720,722],{"name":715,"methodId":5,"linkable":201,"proposed":73,"self":201},"COIN-LIO ablation: Intensity image + Strongest-gradient features",{"name":717,"methodId":5,"linkable":201,"proposed":73,"self":201},"COIN-LIO ablation: Reflectivity image + Strongest-gradient features",{"name":719,"methodId":5,"linkable":201,"proposed":73,"self":201},"COIN-LIO ablation: Filtered image + Strongest-gradient features",{"name":721,"methodId":5,"linkable":201,"proposed":73,"self":201},"COIN-LIO ablation: Filtered image + Random features",{"name":723,"methodId":5,"linkable":201,"proposed":201,"self":201},"COIN-LIO ablation: Filtered image + Complementary features (proposed)",[725,727,729,731,732,734,736,738,740,742,744,746,748,749,750],[218,218,218,726,219,218,219,219,218],13.928,[218,218,221,728,219,218,219,219,218],0.472,[218,218,223,730,219,218,219,219,218],0.948,[221,218,218,107,218,218,219,219,218],[221,218,221,733,219,218,219,219,218],0.699,[221,218,223,735,219,218,219,219,218],0.874,[223,218,218,737,219,218,219,219,218],0.814,[223,218,221,739,219,218,219,219,218],0.489,[223,218,223,741,219,218,219,219,218],0.701,[225,218,218,743,219,218,219,219,218],1.913,[225,218,221,745,219,218,219,219,218],0.58,[225,218,223,747,219,218,219,219,218],1.073,[227,218,218,358,219,218,219,219,218],[227,218,221,362,219,218,219,219,218],[227,218,223,390,219,218,219,219,218],[752],"failed (marked x)",[702],[],[],[757],"Ablation of COIN-LIO image type and patch selection policy on ENWIDE; ATE RMSE (m); x = failed",{"slug":759,"group":760,"sourceId":761,"sourceLabel":762,"table":763,"selfRows":151,"metrics":764,"seqs":772,"entrants":791,"cells":811,"outcomes":937,"locators":938,"hardware":939,"wordings":940,"notes":941},"yan2026tunnel-table-1","yan2026tunnel:Table 1","yan2026tunnel","Yan et al., 2026a","Table 1",[765,767,769],{"label":766,"unit":161,"statistic":162,"alignment":88},"ATE (m), RMSE of global absolute trajectory error",{"label":768,"unit":161,"statistic":162,"alignment":88},"Average ATE (m)",{"label":770,"unit":771,"statistic":162,"alignment":88},"Average RPE (%) per 100 m","% per 100 m",[773,777,779,781,783,785,787,789],{"dataset":774,"sequence":775,"environment":776},"Kimera-Multi Campus-Tunnel (KMCT)","07_ac2-005","MIT campus subterranean tunnel, long and uniform (Kimera-Multi Campus-Tunnel; eight Clearpath Jackal robots, 6753 m in about 30 min)",{"dataset":774,"sequence":778,"environment":776},"07_acl-001",{"dataset":774,"sequence":780,"environment":776},"07_api-003",{"dataset":774,"sequence":782,"environment":776},"07_hat-002",{"dataset":774,"sequence":784,"environment":776},"07_sob-002",{"dataset":774,"sequence":786,"environment":776},"07_spl-007",{"dataset":774,"sequence":788,"environment":776},"07_tho-001",{"dataset":774,"sequence":790,"environment":776},"Average (7 sequences)",[792,795,798,801,803,805,806,807,809],{"name":793,"methodId":794,"linkable":201,"proposed":73,"self":73},"ORB-SLAM3","orbslam3_2021",{"name":796,"methodId":797,"linkable":201,"proposed":73,"self":73},"VINS-Mono","vinsmono2018",{"name":799,"methodId":800,"linkable":201,"proposed":73,"self":73},"LeGO-LOAM","legoloam2018",{"name":802,"methodId":208,"linkable":201,"proposed":73,"self":73},"LIO-SAM",{"name":804,"methodId":211,"linkable":201,"proposed":73,"self":73},"Fast-LIO2",{"name":509,"methodId":510,"linkable":201,"proposed":73,"self":73},{"name":7,"methodId":5,"linkable":201,"proposed":73,"self":201},{"name":808,"methodId":107,"linkable":73,"proposed":73,"self":73},"VINS-FEN",{"name":810,"methodId":761,"linkable":201,"proposed":201,"self":73},"Proposed",[812,813,815,817,819,821,823,825,827,829,831,833,835,837,839,841,843,845,847,849,851,853,855,857,859,861,863,865,866,867,868,870,872,874,876,878,880,881,883,885,887,888,889,891,893,894,895,897,899,901,903,905,907,909,911,912,913,914,915,917,919,921,922,924,926,927,929,931,932,933,935],[218,218,218,107,218,218,219,219,218],[221,218,218,814,219,218,219,219,218],3.96,[223,218,218,816,219,218,219,219,218],3.63,[225,218,218,818,219,218,219,219,218],3.41,[227,218,218,820,219,218,219,219,218],2.7,[229,218,218,822,219,218,219,219,218],2.77,[231,218,218,824,219,218,219,219,218],2.71,[233,218,218,826,219,218,219,219,218],3.05,[235,218,218,828,219,218,219,219,218],2.89,[218,218,221,830,219,218,219,219,218],3.99,[221,218,221,832,219,218,219,219,218],3.74,[223,218,221,834,219,218,219,219,218],3.38,[225,218,221,836,219,218,219,219,218],3.24,[227,218,221,838,219,218,219,219,218],2.62,[229,218,221,840,219,218,219,219,218],2.9,[231,218,221,842,219,218,219,219,218],2.66,[233,218,221,844,219,218,219,219,218],2.86,[235,218,221,846,219,218,219,219,218],2.41,[218,218,223,848,219,218,219,219,218],1.82,[221,218,223,850,219,218,219,219,218],1.49,[223,218,223,852,219,218,219,219,218],1.41,[225,218,223,854,219,218,219,219,218],1.33,[227,218,223,856,219,218,219,219,218],0.94,[229,218,223,858,219,218,219,219,218],1.18,[231,218,223,860,219,218,219,219,218],0.97,[233,218,223,862,219,218,219,219,218],1.2,[235,218,223,864,219,218,219,219,218],0.92,[218,218,225,107,218,218,219,219,218],[221,218,225,107,218,218,219,219,218],[223,218,225,107,218,218,219,219,218],[225,218,225,869,219,218,219,219,218],7.01,[227,218,225,871,219,218,219,219,218],5.3,[229,218,225,873,219,218,219,219,218],4.97,[231,218,225,875,219,218,219,219,218],5.06,[233,218,225,877,219,218,219,219,218],5.03,[235,218,225,879,219,218,219,219,218],4.95,[218,218,227,328,219,218,219,219,218],[221,218,227,882,219,218,219,219,218],1.93,[223,218,227,884,219,218,219,219,218],2.64,[225,218,227,886,219,218,219,219,218],1.46,[227,218,227,599,219,218,219,219,218],[229,218,227,882,219,218,219,219,218],[231,218,227,890,219,218,219,219,218],1.13,[233,218,227,892,219,218,219,219,218],1.28,[235,218,227,858,219,218,219,219,218],[218,218,229,107,218,218,219,219,218],[221,218,229,896,219,218,219,219,218],2.18,[223,218,229,898,219,218,219,219,218],2.58,[225,218,229,900,219,218,219,219,218],2.37,[227,218,229,902,219,218,219,219,218],1.72,[229,218,229,904,219,218,219,219,218],2.24,[231,218,229,906,219,218,219,219,218],1.91,[233,218,229,908,219,218,219,219,218],1.94,[235,218,229,910,219,218,219,219,218],1.63,[218,218,231,107,218,218,219,219,218],[221,218,231,107,218,218,219,219,218],[223,218,231,107,218,218,219,219,218],[225,218,231,107,218,218,219,219,218],[227,218,231,916,219,218,219,219,218],8.37,[229,218,231,918,219,218,219,219,218],9.09,[231,218,231,920,219,218,219,219,218],8.52,[233,218,231,107,218,218,219,219,218],[235,218,231,923,219,218,219,219,218],8.48,[227,221,233,925,219,218,219,219,221],3.26,[227,223,233,906,219,218,219,219,221],[229,221,233,928,219,218,219,219,221],3.58,[229,223,233,930,219,218,219,219,221],2.3,[231,221,233,663,219,218,219,219,221],[231,223,233,906,219,218,219,219,221],[235,221,233,934,219,218,219,219,221],3.21,[235,223,233,936,219,218,219,219,221],1.77,[692],[763],[],[],[942,943],"ATE (RMSE, m) on KMCT; each ROS bag run five times and the best trial reported; 'Failed' = failure to run","ATE (RMSE, m) on KMCT; each ROS bag run five times and the best trial reported; 'Failed' = failure to run; averages only for methods that ran on every sequence",[945,950,956,962,967],{"group":946,"slug":947,"sourceLabel":6,"table":948,"selfRows":235,"datasets":949},"coinlio2024:Table I","coinlio2024-table-i","Table I",[100],{"group":951,"slug":952,"sourceLabel":762,"table":953,"selfRows":227,"datasets":954},"yan2026tunnel:Table 2","yan2026tunnel-table-2","Table 2",[955],"WHU-Helmet (WHUH)",{"group":957,"slug":958,"sourceLabel":6,"table":959,"selfRows":223,"datasets":960},"coinlio2024:Text Sec.IV-A","coinlio2024-text-sec-iv-a","Text Sec.IV-A",[961],"Newer College Dataset",{"group":963,"slug":964,"sourceLabel":762,"table":965,"selfRows":221,"datasets":966},"yan2026tunnel:Table 3","yan2026tunnel-table-3","Table 3",[774],{"group":968,"slug":969,"sourceLabel":762,"table":970,"selfRows":221,"datasets":971},"yan2026tunnel:Table 4","yan2026tunnel-table-4","Table 4",[955],1790510657734]