[{"data":1,"prerenderedAt":957},["ShallowReactive",2],{"method-fasterlio2022":3},{"method":4,"reference":50,"equipment":75,"figures":106,"results":107},{"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":26,"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},"fasterlio2022","Bai et al., 2022","Faster-LIO","Faster-LIO: Lightweight Tightly Coupled Lidar-Inertial Odometry Using Parallel Sparse Incremental Voxels",2022,"recent","C05","odometry_with_local_mapping","Faster-LIO 以 FAST-LIO2 為基礎，將 ikd-Tree 換成增量式稀疏體素（iVox），以雜湊表與 LRU 快取管理體素，並以近似 k 近鄰查詢取代嚴格 k 近鄰，以換取大幅加速。作者提出線性與偽希爾伯特曲線（PHC）兩種體素內結構，並指出近似鄰點雖會帶來不精確，但在 LIO 配準中影響不大。其貢獻集中在資料結構效率，而非估計架構或全域一致性。","Replaces FAST-LIO2's ikd-Tree with incremental sparse voxels (iVox) supporting incremental insertion and parallel approximate k-NN, greatly increasing LIO throughput at similar accuracy.","full_text_reviewed","peer_reviewed_published","supplementary","not_reported",[20],"public_benchmark",[22,23,24,25],"Per-scan rate of 1000-2000 Hz (solid-state) and over 200 Hz (32-line) at the same level of accuracy (abstract; Sec. VI)","iVox can be integrated into other SLAM systems such as LeGO-LOAM mapping to reduce mapping time (Sec. V-D)","Speed increase against FastLIO2 of 1.52 to 3.49 times across nine sequences, and lower pose-computation (opt) time than FastLIO2 on all nine sequences, e.g. utbm_2 5.47 ms versus 19.35 ms (Table I)","In LeGO-LOAM mapping, iVox reduced total per-scan runtime from 69.41 ms to 51.706 ms, mainly by removing keyframe extraction (Table III)",[27,28,29,30],"Nearest neighbours are approximate; authors note iVox is not a good choice when exact neighbours are required (Sec. V-A)","No loop closure detection (author-stated, Sec. V-C; loop closure of LIO-SAM and LiLi-OM was disabled for comparison)","Linear Faster-LIO has higher APE than FastLIO2 on all nine sequences in Table II (e.g. liosam_1 1.78 m versus 0.83 m) while RPE is similar; authors describe accuracy as comparable (Table II; Sec. V-C; table-derived)","k-NN time of iVox grows faster than tree structures as points per voxel increase, so small to medium local maps are preferred (Sec. V-A)",[32,33],"3D LiDAR (solid-state LiDAR in the AVIA dataset from FastLIO2 and 32-line spinning LiDARs; sensor models not named in the paper)","IMU",[35],"not described in the paper; evaluation on public datasets AVIA (from FastLIO2), NCLT, ULHK, UTBM and liosam_1; the LeGO-LOAM comparison uses data gathered from a ground vehicle (Sec. V; Sec. V-D)","iterated EKF pipeline inherited from FAST-LIO2 (paper states Faster-LIO is developed under FastLIO2 with code refactoring)","point-to-plane residuals using approximate k-NN from incremental sparse voxels","discrete poses (as FAST-LIO2)","preprocessing plus undistortion inherited from FAST-LIO2 (Table I footnote)","none","incremental sparse voxels (iVox) in a hash map with LRU cache; linear or pseudo-Hilbert-curve (PHC) in-voxel layout","odometry and point-cloud map; export format not_reported","CPU with parallel k-NN, threads set to the CPU maximum: AMD R7-5800X (8 cores, 3.8 GHz) and Intel Xeon Gold 5128 (16 cores, 2.3 GHz; Fig. 1 caption says 5218) desktops, plus an Intel i7-10750H laptop for accuracy runs; 1000-2000 Hz per scan for solid-state and over 200 Hz for 32-line spinning LiDAR (abstract)","https:\u002F\u002Fgithub.com\u002Fgaoxiang12\u002Ffaster-lio","GPL-2.0 (LICENSE file checked)",[47],{"relation":48,"title":49,"doi_or_url":44},"code_release","gaoxiang12\u002Ffaster-lio (includes author-posted PDF doc\u002Ffaster-lio.pdf)",{"id":5,"kind":51,"shortName":7,"title":8,"authors":52,"year":9,"venue":59,"venueType":60,"publisher":61,"volumeIssuePages":62,"doi":63,"arxivId":64,"url":65,"firstPublicDate":66,"publicationStatus":16,"metadataStatus":67,"fulltextStatus":15,"era":10,"classicReason":68,"codeUrl":44,"cluster":11,"topics":69,"mdpi":71,"verification":72,"label":6,"fulltextRoute":73,"versionRead":74,"addedByCensus":71},"method",[53,54,55,56,57,58],"Chunge Bai","Tao Xiao","Yajie Chen","Haoqian Wang","Fang Zhang","Xiang Gao","IEEE Robotics and Automation Letters","journal","IEEE","7(2):4861-4868","10.1109\u002Flra.2022.3152830",null,"https:\u002F\u002Fapi.crossref.org\u002Fworks\u002F10.1109\u002FLRA.2022.3152830","2022-02-22","metadata_verified","not_applicable",[11,70],"C12",false,"corrected","publisher OA","Version of record, IEEE RA-L 7(2):4861-4868 (open access, CC BY 4.0): IEEE Xplore full-text HTML plus the identical published-layout PDF posted in the authors' GitHub repository (used for the table images)",[76,82,85,89,96,102],{"category":77,"model":78,"canonical":78,"role":79,"dataset":64,"specs":80,"locator":81},"compute","AMD R7-5800X","compute for runtime","8 cores, 3.8 GHz desktop ('iVox AMD')","Sec. V; Fig. 1",{"category":77,"model":83,"canonical":83,"role":79,"dataset":64,"specs":84,"locator":81},"Intel Xeon Gold 5128","16 cores, 2.3 GHz desktop ('iVox Intel'); Fig. 1 caption gives Xeon Gold 5218",{"category":77,"model":86,"canonical":86,"role":79,"dataset":64,"specs":87,"locator":88},"Intel i7-10750H","laptop CPU, 6 cores, boost 5 GHz; used for accuracy experiments","Sec. V",{"category":90,"model":91,"canonical":91,"role":92,"dataset":93,"specs":94,"locator":95},"gnss","RTK (model not reported)","reference or ground truth","NCLT; UTBM robocar dataset","reference ground-truth trajectories of the selected sequences","Sec. V-C; Fig. 11",{"category":97,"model":98,"canonical":98,"role":99,"dataset":93,"specs":100,"locator":101},"lidar","spinning LiDAR (model not named)","dataset sensor","abstract reports over 200 Hz for 32-line spinning lidars; line count and model per dataset not stated","Abstract; Sec. V-B",{"category":97,"model":103,"canonical":103,"role":99,"dataset":104,"specs":105,"locator":88},"solid-state LiDAR (model not named)","AVIA","AVIA dataset from FastLIO2",[],{"totalRows":108,"groupCount":109,"groups":110,"others":877},190,15,[111,304,508,626],{"slug":112,"group":113,"sourceId":5,"sourceLabel":6,"table":114,"selfRows":115,"metrics":116,"seqs":126,"entrants":153,"cells":163,"outcomes":297,"locators":298,"hardware":299,"wordings":301,"notes":302},"fasterlio2022-table-i","fasterlio2022:Table I","Table I",45,[117,121,123],{"label":118,"unit":119,"statistic":120,"alignment":68},"pre (ms): preprocessing+undistortion+downsampling per scan","ms","mean",{"label":122,"unit":119,"statistic":120,"alignment":68},"opt (ms): pose computation per scan",{"label":124,"unit":125,"statistic":68,"alignment":68},"Spd inc: speed increase against FastLIO2","ratio (x)",[127,131,133,137,139,141,143,147,149],{"dataset":128,"sequence":129,"environment":130},"NCLT","nclt_2","NCLT public dataset (scene and platform not described in the paper)",{"dataset":128,"sequence":132,"environment":130},"nclt_4",{"dataset":134,"sequence":135,"environment":136},"UTBM robocar dataset","utbm_2","UTBM robocar public dataset (scene not described in the paper)",{"dataset":134,"sequence":138,"environment":136},"utbm_3",{"dataset":134,"sequence":140,"environment":136},"utbm_4",{"dataset":134,"sequence":142,"environment":136},"utbm_5",{"dataset":144,"sequence":145,"environment":146},"ULHK (UrbanLoco)","ulhk_1","ULHK public dataset (scene not described in the paper)",{"dataset":144,"sequence":148,"environment":146},"ulhk_2",{"dataset":150,"sequence":151,"environment":152},"LIO-SAM dataset","liosam_1","liosam_1 sequence (scene not described in the paper)",[154,156,158,161],{"name":7,"methodId":5,"linkable":155,"proposed":155,"self":155},true,{"name":157,"methodId":5,"linkable":155,"proposed":155,"self":155},"Faster-LIO 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stated for Table I (paper platforms: AMD R7-5800X, Intel Xeon Gold 5128, Intel i7-10750H laptop)",[],[303],"Time evaluation: 'pre' = preprocessing + undistortion + downsampling per scan, 'opt' = pose computation; Spd inc = speed increase against FastLIO2; LIO-SAM and LiLi-OM columns omitted because they are measured per keyframe on distributed ROS nodes (Table I footnote)",{"slug":305,"group":306,"sourceId":5,"sourceLabel":6,"table":307,"selfRows":308,"metrics":309,"seqs":316,"entrants":335,"cells":345,"outcomes":500,"locators":503,"hardware":504,"wordings":505,"notes":506},"fasterlio2022-table-ii","fasterlio2022:Table II","Table II",36,[310,313],{"label":311,"unit":312,"statistic":18,"alignment":18},"APE (m)","m",{"label":314,"unit":315,"statistic":18,"alignment":40},"RPE (%) translational per 100 m","%",[317,319,321,323,325,327,329,331,333],{"dataset":128,"sequence":318,"environment":130},"nclt_2 (0.26 km)",{"dataset":128,"sequence":320,"environment":130},"nclt_4 (1.86 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LIO-SAM needs 9-axis IMU input, UTBM skipped (Sec. V-C)","'-' in table (Table I footnote: failed due to large drift or lack of necessary input)",[307],[],[],[507],"Accuracy in APE (m) over whole trajectories and translational RPE (%) per 100 m; loop closure of LIO-SAM and LiLi-OM disabled; parameters of LIO-SAM and LiLi-OM not adjusted; reference mostly RTK",{"slug":509,"group":510,"sourceId":511,"sourceLabel":512,"table":307,"selfRows":513,"metrics":514,"seqs":525,"entrants":542,"cells":547,"outcomes":619,"locators":621,"hardware":622,"wordings":623,"notes":624},"adalio2023-table-ii","adalio2023:Table II","adalio2023","Lim et al., 2023",28,[515,518,520,522],{"label":516,"unit":517,"statistic":18,"alignment":18},"number of markers within 1 cm","count",{"label":519,"unit":517,"statistic":18,"alignment":18},"number of markers within 10 cm",{"label":521,"unit":517,"statistic":18,"alignment":18},"number of markers within 100 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validation sequences with millimetre-level marker poses; each marker scored 10, 6 or 3 if the closest estimated pose is within 1, 10 or 100 cm; x = trajectory diverged",{"slug":627,"group":628,"sourceId":629,"sourceLabel":630,"table":631,"selfRows":632,"metrics":633,"seqs":640,"entrants":675,"cells":687,"outcomes":870,"locators":872,"hardware":873,"wordings":874,"notes":875},"iglio2024-table-iii","iglio2024:Table III","iglio2024","Chen et al., 2024","Table III",16,[634,638],{"label":635,"unit":312,"statistic":636,"alignment":637},"Absolute pose error (RMSE, meters)","RMSE","SE3",{"label":635,"unit":312,"statistic":636,"alignment":639},"first-pose",[641,644,645,647,648,650,654,656,658,660,662,664,665,669,671,673],{"dataset":128,"sequence":642,"environment":643},"nclt_1","campus (Velodyne 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(name per Table C1)","Hilti handheld sequence exp21-outside (name per Table C1)","Hilti handheld sequence site1-handheld-1 (name per Table C1)","Hilti handheld sequence site1-handheld-2 (name per Table C1)","Hilti handheld sequence site1-handheld-3 (name per Table C1)","Hilti handheld sequence site1-handheld-4 (name per Table C1)","Hilti handheld sequence site1-handheld-5 (name per Table C1)",{"group":898,"slug":899,"sourceLabel":900,"table":901,"selfRows":205,"datasets":902},"yuan2022voxelmap:Table V","yuan2022voxelmap-table-v","Yuan et al., 2022","Table V",[903],"authors' Livox Avia datasets",{"group":905,"slug":906,"sourceLabel":907,"table":114,"selfRows":200,"datasets":908},"voxelmappp2024:Table I","voxelmappp2024-table-i","Wu et al., 2024b",[909],"M2DGR",{"group":911,"slug":912,"sourceLabel":630,"table":307,"selfRows":195,"datasets":913},"iglio2024:Table II","iglio2024-table-ii",[104,914,915,128,916],"BG","NCD","ULHK",{"group":918,"slug":919,"sourceLabel":920,"table":921,"selfRows":190,"datasets":922},"feng2025_construction_lidar_eval:Table 3","feng2025-construction-lidar-eval-table-3","Feng et al., 2025","Table 3",[923],"Feng et al. simulated construction-site dataset (Gazebo)",{"group":925,"slug":926,"sourceLabel":920,"table":927,"selfRows":190,"datasets":928},"feng2025_construction_lidar_eval:Table 4","feng2025-construction-lidar-eval-table-4","Table 4",[929],"Feng et al. real construction-site dataset (Xi'an hospital)",{"group":931,"slug":932,"sourceLabel":907,"table":307,"selfRows":190,"datasets":933},"voxelmappp2024:Table II","voxelmappp2024-table-ii",[934],"VoxelMap++ own datasets",{"group":936,"slug":937,"sourceLabel":907,"table":631,"selfRows":190,"datasets":938},"voxelmappp2024:Table III","voxelmappp2024-table-iii",[934],{"group":940,"slug":941,"sourceLabel":630,"table":942,"selfRows":186,"datasets":943},"iglio2024:Table 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