[{"data":1,"prerenderedAt":643},["ShallowReactive",2],{"method-voxelslam2026":3},{"method":4,"reference":59,"equipment":87,"figures":142,"results":143},{"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":22,"limitations":27,"sensors":32,"platform":35,"estimator":39,"association":40,"timeModel":41,"deskew":42,"loopClosure":43,"globalOptimization":44,"mapRepresentation":45,"prior":46,"outputGeometry":47,"compute":48,"codeUrl":49,"codeLicense":50,"relatedVersions":51},"voxelslam2026","Liu et al., 2026","Voxel-SLAM","Voxel‐SLAM: A Complete, Accurate, and Versatile Light Detection and Ranging‐Inertial Simultaneous Localization and Mapping System",2026,"recent","C05","full_slam_with_global_correction","Voxel-SLAM 以同一種自適應體素地圖貫穿初始化、里程計、局部建圖、迴圈與全域建圖五個模組，並依作者所稱的短期、中期、長期與多地圖四類資料關聯設計。局部建圖以滑動視窗 LiDAR-慣性光束法平差（bundle adjustment, BA）同時修正狀態與地圖；迴圈偵測以 BTC 描述子並加上平面約束與漂移比例檢查，觸發位姿圖最佳化與地圖重建。全域建圖以階層式 BA 從關鍵影格視窗到子地圖逐層最佳化，以提升多次作業地圖的一致性；其地圖表示明言沿用 VoxelMap。","A complete LiDAR-inertial SLAM that uses one adaptive voxel map for initialization, EKF odometry, sliding-window LiDAR-inertial BA, BTC-based multi-session loop closure, and hierarchical global BA.","full_text_reviewed","peer_reviewed_published","main_body","作者使用 Hilti 資料集手持序列（Hesai XT-32 與 Bosch BMI085 IMU），並描述為室內外結構化施工環境。依期刊版附錄表 C1，hilti01 至 hilti08 對應 exp01、exp02、exp03、exp07、exp09、exp11、exp15 與 exp21 序列，其中只有 exp01 至 exp03 以 construction 命名，其餘為 long-corridor、cupola、gallery 與 outside；hilti09 至 hilti13 對應 site1 手持序列，作者明言為同一施工現場，用於多次作業地圖合併：多次作業 ATE 由僅位姿圖最佳化的 7.6 cm 降至全域建圖後的 4.9 cm（表 5）。單次作業時完整系統在 hilti01 至 hilti13 的 ATE 為 0.62 至 13.8 cm（表 2），誤差最大者為含狹窄樓梯的序列。評估僅為軌跡 ATE，未見點雲幾何對參考量測的評估。",[20,21],"public_benchmark","real_construction_site",[23,24,25,26],"Initializes within about 1 s of data even from a highly dynamic initial state where FAST-LIO2 diverged (Sec. 1; Sec. 10.1)","Multi-session merging demonstrated on Hilti sequences recorded at the same construction site: multisession ATE 7.6 cm with PGO only and 4.9 cm with global mapping (Table 5)","Full system has the lowest ATE on all 13 Hilti sequences among compared odometry and SLAM methods; global mapping improves ATE even without detected loops (Table 2)","Global mapping after a session costs 0.6% to 4.7% of the data collection time (Sec. 10.5)",[28,29,30,31],"Missed loop between overlapping Hilti sequences (narrow corridor) led to insufficient constraints in one multi-session case, later mitigated by global mapping (Sec. 10.3.1; Fig. 12b)","Relocalization does not remove the root cause of divergence; in long degenerate conditions such as dynamic scenes, rain or fog, or long feature-poor tunnels, frequent reinitialization wastes data, so the system suits non-extreme scenarios (Sec. 10.4, Remark 1)","Relies only on planar features from the adaptive voxel map (Sec. 1; Sec. 3.3)","Future work: fuse images for degenerate scenes and colour, and GPU acceleration for global mapping (Sec. 11)",[33,34],"3D LiDAR","IMU",[36,37,38],"handheld","UAV","vehicle","Odometry: IMU propagation with motion compensation, then scan-to-map registration against the adaptive voxel map following VoxelMap (point-to-plane distances with per-point noise plus IMU residuals update the current state); the rough LIO inside initialization follows FAST-LIO; local mapping: sliding-window LiDAR-inertial BA (window 10) combining BALM2 point-cluster BA factors with IMU preintegration, solved by Levenberg-Marquardt with analytic Jacobian and Hessian; PGO via GTSAM after loops; hierarchical global BA","point-to-plane to adaptive voxel-map planes; BA over point clusters per voxel","discrete poses","IMU propagation compensates in-scan motion distortion within the odometry module (Sec. VI-A)","BTC descriptor place recognition with geometric verification, plus checks for plane constraints in three independent directions and a drift-to-travel-distance ratio (e.g., at most 1%); works within and across sessions","Pose graph optimization (GTSAM) with map rebuilding when the drift distance exceeds a threshold (e.g., 0.1 m); keyframe-window BA (size 10, stride 5) in real time; after a session ends, global BA over submaps with coarse-to-fine voxelization and a top-down PGO (hierarchical BA), also across sessions","adaptive voxel map (planes with point clusters) shared by initialization, odometry, local mapping, loop closure and global mapping","none (previous sessions used for multi-map association)","globally optimized multi-session point-cloud map and scan poses","Laptop Intel i7-10750H (3.5 GHz, 32 GB) and onboard Intel i3-N305 (3.0 GHz, 16 GB); mean total per-scan thread time 0.027 s (Hilti), 0.095 s (MARS-LVIG), 0.051 s (UrbanNav), 0.044 s (MulRan), 0.037 s onboard; global mapping after session end 3.2 s (Hilti mean) to 62.1 s (MulRan mean), 14 s for the 11-min onboard sequence; memory 1.1 to 5.3 GB","https:\u002F\u002Fgithub.com\u002Fhku-mars\u002FVoxel-SLAM","GPL-2.0 (LICENSE file checked)",[52,56],{"relation":53,"title":54,"doi_or_url":55},"preprint","Voxel-SLAM: A Complete, Accurate, and Versatile LiDAR-Inertial SLAM System (arXiv v1)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2410.08935",{"relation":57,"title":58,"doi_or_url":49},"code_release","hku-mars\u002FVoxel-SLAM",{"id":5,"kind":60,"shortName":7,"title":8,"authors":61,"year":9,"venue":72,"venueType":73,"publisher":74,"volumeIssuePages":75,"doi":76,"arxivId":77,"url":78,"firstPublicDate":79,"publicationStatus":16,"metadataStatus":80,"fulltextStatus":15,"era":10,"classicReason":81,"codeUrl":49,"cluster":11,"topics":82,"mdpi":83,"verification":84,"label":6,"fulltextRoute":85,"versionRead":86,"addedByCensus":83},"method",[62,63,64,65,66,67,68,69,70,71],"Zheng Liu","Haotian Li","Chongjian Yuan","Xiyuan Liu","Jiarong Lin","Rundong Li","Chunran Zheng","Bingyang Zhou","Wenyi Liu","Fu Zhang","Advanced Intelligent Systems","journal","Wiley","8(4):e202501081","10.1002\u002Faisy.202501081","2410.08935","https:\u002F\u002Fapi.crossref.org\u002Fworks\u002F10.1002\u002Faisy.202501081","2024-10-11","metadata_verified","not_applicable",[11],false,"corrected","NTU institutional (Chrome)","Version of record, Advanced Intelligent Systems 8(4):e202501081 (Wiley HTML, open access, published 2026-01-27); arXiv v1 PDF also downloaded as a cross-check",[88,95,99,104,107,112,115,120,123,130,132,138],{"category":89,"model":90,"canonical":90,"role":91,"dataset":92,"specs":93,"locator":94},"lidar","Hesai XT-32","dataset sensor","Hilti (handheld sequences, Table C1)","handheld sequences; LiDAR at 10 Hz","Sec. 10",{"category":96,"model":97,"canonical":97,"role":91,"dataset":92,"specs":98,"locator":94},"imu","Bosch BMI085","400 Hz",{"category":89,"model":100,"canonical":100,"role":91,"dataset":101,"specs":102,"locator":103},"Livox Avia","MARS-LVIG","downward-looking on a UAV at about 100 m height; speed up to 12 m\u002Fs","Sec. 10; Sec. 10.2.2",{"category":96,"model":105,"canonical":105,"role":91,"dataset":101,"specs":106,"locator":94},"BMI088 (built-in IMU of Livox Avia)","200 Hz",{"category":89,"model":108,"canonical":109,"role":91,"dataset":110,"specs":111,"locator":94},"HDL-32 E (as written)","Velodyne HDL-32E","UrbanNav","robotcar urban dataset",{"category":96,"model":113,"canonical":114,"role":91,"dataset":110,"specs":98,"locator":94},"Xsens-MTI-30 (as written)","Xsens MTi-30",{"category":89,"model":116,"canonical":117,"role":91,"dataset":118,"specs":119,"locator":94},"OS1-64","Ouster OS1-64","MulRan","robotcar; LiDAR loses about 70 deg of FoV",{"category":96,"model":121,"canonical":121,"role":91,"dataset":118,"specs":122,"locator":94},"IMU (model not reported)","100 Hz",{"category":124,"model":125,"canonical":125,"role":126,"dataset":127,"specs":128,"locator":129},"mobile_scanner_device","light handheld device with Livox Avia and its internal IMU","method input","private dataset (private1, private2)","used for private1 (forest, fast initial motion) and private2 (campus with elevator and escalator, 11 min 34 s)","Sec. 10; Fig. 16a; Table C1",{"category":89,"model":100,"canonical":100,"role":126,"dataset":127,"specs":131,"locator":94},"on the authors' handheld device",{"category":133,"model":134,"canonical":134,"role":135,"dataset":136,"specs":137,"locator":94},"compute","laptop with Intel i7-10750H (written i7-10 750 H) at 3.5 GHz, 32 GB memory","compute for runtime",null,"used for initialization, single-session and multisession experiments",{"category":133,"model":139,"canonical":139,"role":135,"dataset":136,"specs":140,"locator":141},"onboard computer with Intel i3-N305 at 3.0 GHz, 16 GB memory","used for the online relocalization experiment (private2)","Sec. 10; Sec. 10.4",[],{"totalRows":144,"groupCount":145,"groups":146,"others":642},64,3,[147,380,596],{"slug":148,"group":149,"sourceId":5,"sourceLabel":6,"table":150,"selfRows":151,"metrics":152,"seqs":158,"entrants":205,"cells":223,"outcomes":372,"locators":374,"hardware":376,"wordings":377,"notes":378},"voxelslam2026-table-2-full-slam-with-lc","voxelslam2026:Table 2 (full SLAM with LC)","Table 2 (full SLAM with LC)",26,[153],{"label":154,"unit":155,"statistic":156,"alignment":157},"absolute trajectory error (RMSE, centimeters)","cm","RMSE","not_reported",[159,163,166,169,173,177,181,185,189,193,196,199,202],{"dataset":160,"sequence":161,"environment":162},"Hilti handheld sequence exp01-construction (name per Table C1)","hilti01","construction environment (sequence named construction)",{"dataset":164,"sequence":165,"environment":162},"Hilti handheld sequence exp02-construction (name per Table C1)","hilti02",{"dataset":167,"sequence":168,"environment":162},"Hilti handheld sequence exp03-construction (name per Table C1)","hilti03",{"dataset":170,"sequence":171,"environment":172},"Hilti handheld sequence exp07-long-corridor (name per Table C1)","hilti04","long corridor",{"dataset":174,"sequence":175,"environment":176},"Hilti handheld sequence exp09-cupola (name per Table C1)","hilti05","cupola",{"dataset":178,"sequence":179,"environment":180},"Hilti handheld sequence exp11-lower-gallery (name per Table C1)","hilti06","lower gallery",{"dataset":182,"sequence":183,"environment":184},"Hilti handheld sequence exp15-upper-gallery (name per Table C1)","hilti07","upper gallery",{"dataset":186,"sequence":187,"environment":188},"Hilti handheld sequence exp21-outside (name per Table C1)","hilti08","outside",{"dataset":190,"sequence":191,"environment":192},"Hilti handheld sequence site1-handheld-1 (name per Table C1)","hilti09","construction site (same site for hilti09 to hilti13, Sec. 10.3.1)",{"dataset":194,"sequence":195,"environment":192},"Hilti handheld sequence site1-handheld-2 (name per Table C1)","hilti10",{"dataset":197,"sequence":198,"environment":192},"Hilti handheld sequence site1-handheld-3 (name per Table C1)","hilti11",{"dataset":200,"sequence":201,"environment":192},"Hilti handheld sequence site1-handheld-4 (name per Table C1)","hilti12",{"dataset":203,"sequence":204,"environment":192},"Hilti handheld sequence site1-handheld-5 (name per Table C1)","hilti13",[206,210,213,216,219,221],{"name":207,"methodId":208,"linkable":209,"proposed":83,"self":83},"LeGO-LOAM","legoloam2018",true,{"name":211,"methodId":212,"linkable":209,"proposed":83,"self":83},"LiLi-OM","liliom2021",{"name":214,"methodId":215,"linkable":209,"proposed":83,"self":83},"LIO-SAM","liosam2020",{"name":217,"methodId":218,"linkable":209,"proposed":83,"self":83},"LTA-OM","ltaom2024",{"name":220,"methodId":5,"linkable":209,"proposed":209,"self":209},"Our (Odom+LM+LC)",{"name":222,"methodId":5,"linkable":209,"proposed":209,"self":209},"Our (Full)",[224,228,231,233,235,237,240,242,245,248,251,254,257,259,261,263,264,266,267,268,269,271,273,274,276,278,279,281,283,284,286,287,289,290,292,294,296,298,300,302,304,306,308,310,312,313,315,317,319,321,323,325,327,329,331,333,335,336,338,340,341,343,345,346,347,349,351,352,353,355,357,359,361,363,364,366,368,370],[225,225,225,226,227,225,227,227,225],0,8.8,-1,[225,225,229,230,227,225,227,227,225],1,39,[225,225,232,136,225,225,227,227,225],2,[225,225,145,234,227,225,227,227,225],25.3,[225,225,236,136,225,225,227,227,225],4,[225,225,238,239,227,225,227,227,225],5,67,[225,225,241,136,225,225,227,227,225],6,[225,225,243,244,227,225,227,227,225],7,22.7,[225,225,246,247,227,225,227,227,225],8,12.6,[225,225,249,250,227,225,227,227,225],9,12.9,[225,225,252,253,227,225,227,227,225],10,27.1,[225,225,255,256,227,225,227,227,225],11,16.2,[225,225,258,136,225,225,227,227,225],12,[229,225,225,260,227,225,227,227,225],6.2,[229,225,229,262,227,225,227,227,225],14.2,[229,225,232,136,225,225,227,227,225],[229,225,145,265,227,225,227,227,225],31,[229,225,236,136,225,225,227,227,225],[229,225,238,253,227,225,227,227,225],[229,225,241,136,225,225,227,227,225],[229,225,243,270,227,225,227,227,225],18.6,[229,225,246,272,227,225,227,227,225],6.9,[229,225,249,246,227,225,227,227,225],[229,225,252,275,227,225,227,227,225],19.9,[229,225,255,277,227,225,227,227,225],22.8,[229,225,258,136,225,225,227,227,225],[232,225,225,280,227,225,227,227,225],6.1,[232,225,229,282,227,225,227,227,225],10.1,[232,225,232,136,225,225,227,227,225],[232,225,145,285,227,225,227,227,225],23.4,[232,225,236,136,225,225,227,227,225],[232,225,238,288,227,225,227,227,225],13.4,[232,225,241,136,225,225,227,227,225],[232,225,243,291,227,225,227,227,225],17.2,[232,225,246,293,227,225,227,227,225],6.6,[232,225,249,295,227,225,227,227,225],5.5,[232,225,252,297,227,225,227,227,225],17.6,[232,225,255,299,227,225,227,227,225],12.5,[232,225,258,301,227,225,227,227,225],74,[145,225,225,303,227,225,227,227,225],1.27,[145,225,229,305,227,225,227,227,225],2.5,[145,225,232,307,227,225,227,227,225],33,[145,225,145,309,227,225,227,227,225],6.7,[145,225,236,311,227,225,227,227,225],40,[145,225,238,232,227,225,227,227,225],[145,225,241,314,227,225,227,227,225],65,[145,225,243,316,227,225,227,227,225],1.2,[145,225,246,318,227,225,227,227,225],2.4,[145,225,249,320,227,225,227,227,225],1.78,[145,225,252,322,227,225,227,227,225],4.1,[145,225,255,324,227,225,227,227,225],2.9,[145,225,258,326,227,225,227,227,225],16,[236,225,225,328,227,225,227,227,225],0.78,[236,225,229,330,227,225,227,227,225],1.8,[236,225,232,332,227,225,227,227,225],2.8,[236,225,145,334,227,225,227,227,225],3.4,[236,225,236,262,227,225,227,227,225],[236,225,238,337,227,225,227,227,225],0.9,[236,225,241,339,227,225,227,227,225],9.2,[236,225,243,337,227,225,227,227,225],[236,225,246,342,227,225,227,227,225],1.25,[236,225,249,344,227,225,227,227,225],1.4,[236,225,252,318,227,225,227,227,225],[236,225,255,344,227,225,227,227,225],[236,225,258,348,227,225,227,227,225],1.26,[238,225,225,350,227,225,227,227,225],0.62,[238,225,229,344,227,225,227,227,225],[238,225,232,324,227,225,227,227,225],[238,225,145,354,227,225,227,227,225],3.3,[238,225,236,356,227,225,227,227,225],13.8,[238,225,238,358,227,225,227,227,225],0.7,[238,225,241,360,227,225,227,227,225],7.8,[238,225,243,362,227,225,227,227,225],0.82,[238,225,246,229,227,225,227,227,225],[238,225,249,365,227,225,227,227,225],1.14,[238,225,252,367,227,225,227,227,225],1.3,[238,225,255,369,227,225,227,227,225],1.22,[238,225,258,371,227,225,227,227,225],0.8,[373],"failed (dash; text states LeGO-LOAM, LiLi-OM, LINS and LIO-SAM failed in these sequences)",[375],"Table 2",[],[],[379],"Hilti handheld sequences; ATE exported from the Hilti evaluation website; full SLAM with loop closure (Our (Full) adds global mapping)",{"slug":381,"group":382,"sourceId":5,"sourceLabel":6,"table":383,"selfRows":151,"metrics":384,"seqs":386,"entrants":400,"cells":420,"outcomes":590,"locators":591,"hardware":592,"wordings":593,"notes":594},"voxelslam2026-table-2-odometry-without-lc","voxelslam2026:Table 2 (odometry without LC)","Table 2 (odometry without LC)",[385],{"label":154,"unit":155,"statistic":156,"alignment":157},[387,388,389,390,391,392,393,394,395,396,397,398,399],{"dataset":160,"sequence":161,"environment":162},{"dataset":164,"sequence":165,"environment":162},{"dataset":167,"sequence":168,"environment":162},{"dataset":170,"sequence":171,"environment":172},{"dataset":174,"sequence":175,"environment":176},{"dataset":178,"sequence":179,"environment":180},{"dataset":182,"sequence":183,"environment":184},{"dataset":186,"sequence":187,"environment":188},{"dataset":190,"sequence":191,"environment":192},{"dataset":194,"sequence":195,"environment":192},{"dataset":197,"sequence":198,"environment":192},{"dataset":200,"sequence":201,"environment":192},{"dataset":203,"sequence":204,"environment":192},[401,402,403,406,407,410,413,416,418],{"name":207,"methodId":208,"linkable":209,"proposed":83,"self":83},{"name":211,"methodId":212,"linkable":209,"proposed":83,"self":83},{"name":404,"methodId":405,"linkable":209,"proposed":83,"self":83},"LINS","lins2020",{"name":214,"methodId":215,"linkable":209,"proposed":83,"self":83},{"name":408,"methodId":409,"linkable":209,"proposed":83,"self":83},"FAST-LIO2","fastlio2_2022",{"name":411,"methodId":412,"linkable":209,"proposed":83,"self":83},"Faster-LIO","fasterlio2022",{"name":414,"methodId":415,"linkable":209,"proposed":83,"self":83},"Point-LIO","pointlio2023",{"name":417,"methodId":5,"linkable":209,"proposed":209,"self":209},"Our 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49,344,227,225,227,227,225],[246,225,252,318,227,225,227,227,225],[246,225,255,344,227,225,227,227,225],[246,225,258,348,227,225,227,227,225],[373],[375],[],[],[595],"Hilti handheld sequences (Hesai XT-32, BMI085 400 Hz); ATE exported from the Hilti evaluation website; odometry without loop closure; all methods with default parameters",{"slug":597,"group":598,"sourceId":5,"sourceLabel":6,"table":599,"selfRows":258,"metrics":600,"seqs":603,"entrants":612,"cells":617,"outcomes":636,"locators":637,"hardware":638,"wordings":639,"notes":640},"voxelslam2026-table-5","voxelslam2026:Table 5","Table 5",[601],{"label":602,"unit":155,"statistic":156,"alignment":157},"ATE (RMSE, centimeters)",[604,605,606,607,608,609],{"dataset":190,"sequence":191,"environment":192},{"dataset":194,"sequence":195,"environment":192},{"dataset":197,"sequence":198,"environment":192},{"dataset":200,"sequence":201,"environment":192},{"dataset":203,"sequence":204,"environment":192},{"dataset":610,"sequence":611,"environment":192},"Hilti site1-handheld-1 to 5 merged in one world frame","Multisession (hilti09 to 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