[{"data":1,"prerenderedAt":533},["ShallowReactive",2],{"method-voxelmappp2024":3},{"method":4,"reference":64,"equipment":90,"figures":128,"results":129},{"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":35,"platform":38,"estimator":41,"association":42,"timeModel":43,"deskew":44,"loopClosure":45,"globalOptimization":45,"mapRepresentation":46,"prior":47,"outputGeometry":48,"compute":49,"codeUrl":50,"codeLicense":51,"relatedVersions":52},"voxelmappp2024","Wu et al., 2024b","VoxelMap++","VoxelMap++: Mergeable Voxel Mapping Method for Online LiDAR(-Inertial) Odometry",2024,"recent","C05","odometry_with_local_mapping","VoxelMap++ 延伸 VoxelMap：每個 0.5 m 體素只以三自由度參數（a、b、d）與其共變異數表示平面，並以可累加的和式遞增最小平方擬合，降低計算與記憶體。體素內平面在累積 50 點收斂後即丟棄原始點，並以 union-find 與鄰近體素平面做共面檢定（馬氏距離配合卡方 95% 門檻），將多個「子平面」視為同一「父平面」的量測，以共變異數跡加權融合，使整面牆或地板共享一個更準確、共變異數更小的平面。狀態估計沿用 FAST-LIO 與 VoxelMap 的迭代誤差狀態卡爾曼濾波。","VoxelMap extension that stores a 3DOF plane with covariance per 0.5 m hashed voxel, fitted incrementally by least squares, and merges converged coplanar voxel planes through union-find with a Mahalanobis test and trace-weighted fusion, so large walls and floors share one lower-uncertainty plane used in a FAST-LIO style iterated error-state Kalman filter.","full_text_reviewed","peer_reviewed_published","supplementary","論文未在營建工地驗證；自建資料為 UESTC 校園森林草地與具長走廊的既有建物，只以回到原點的端點誤差評估。把整面地板、天花板與牆合併為大平面來抑制走廊漂移，對施工中建物的長走廊與樓板場景有直接參考價值；但作者指出場景變動（如電梯門關閉）會使已收斂的體素平面失效而發散，而施工現場的臨時構件、模板與材料堆置持續變動，這項限制可能更明顯（推論）。",[20,21,22],"public_benchmark","completed_building","cross_site",[24,25,26,27],"Lowest ATE on 5 of the 7 M2DGR sequences (all except Street07 and the failed Lift04), e.g. Street02 1.16 m versus 1.74 m for VoxelMap and 2.32 m for FAST-LIO2 (Table I)","End-to-end error 0.03 to 0.07 m on five unstructured forest and grassland loops of 284 to 520 m (Table II)","End-to-end error 0.25 to 1.45 m on five indoor corridor loops where other methods reached up to 19.7 m, attributed to merging floor and ceiling into large planes that constrain pitch drift (Table III, Fig. 8)","Lowest computation time and memory among FAST-LIO2, Faster-LIO, VoxelMap and VoxelMap++ on the tested loops (Table IV)",[29,30,31,32,33,34],"Fails in dynamic scenes: on M2DGR lift04 the converged voxels of the elevator doors do not update when the doors close and the estimate diverges (Sec. IV-A, V)","Street07 ATE (12.85 m) was worse than FAST-LIO2, Faster-LIO and LINS (Table I)","On unstructured loopH VoxelMap had a lower end-to-end error than VoxelMap++ (0.0492 vs 0.0734 m) (Table II)","Own datasets have no RTK ground truth; only start-to-end errors of loops that return to the origin are reported (Sec. IV-B)","Baseline values for A-LOAM, LeGO-LOAM, LIO-SAM and LINS are copied from the M2DGR paper (Sec. IV-A)","Voxels stop updating after 50 points, so later changes are not absorbed (Sec. III-C-2) (inference from the design and the lift04 failure)",[36,37],"3D LiDAR, spinning or non-repetitive solid-state (Velodyne VLP-32C in M2DGR; Livox HAP in the authors' data)","IMU (Realsense D435i IMU in M2DGR; ZED 2i built-in IMU in the authors' data)",[39,40],"ground robot (M2DGR)","trolley (pushed cart with Livox HAP and ZED 2i)","iterated error-state Kalman filter as in FAST-LIO and VoxelMap, with point-to-plane observations whose noise combines point and merged-plane covariances (Sec. III-A, III-D)","each point is hashed to its 0.5 m voxel and matched point-to-plane to the root (father) plane of that voxel's union-find node (Sec. III-C, III-D)","discrete scan poses with FAST-LIO style IMU propagation (Sec. III-A)","FAST-LIO style preprocessing of raw points (backward propagation implied by 'similar to FAST-LIO'; not detailed) (Sec. III-A)","none","hash table of 0.5 m voxels, each holding a 3DOF plane (a, b, d) with 3x3 covariance fitted incrementally by least squares; converged planes (after 50 points, raw points discarded) are merged with coplanar neighbours by union-find using a Mahalanobis chi-square test and trace-weighted fusion, so many voxels share one father plane (Sec. III-B, III-C)","LiDAR-IMU extrinsic calibrated with LI-Init; sensors synchronized by IEEE 1588-2008 in the authors' data (Sec. IV)","odometry and a compact plane map (merged planes plus unmerged voxel planes); raw points are not kept after voxel convergence","CPU: average 4.88 ms and 126 MB on a 203 m corridor loop and 13.83 ms and 196 MB on a 329 m unstructured loop, lower than VoxelMap, Faster-LIO and FAST-LIO2, on a laptop with a 2.9 GHz 8-core CPU and 16 GiB memory (Table IV, Sec. IV)","https:\u002F\u002Fgithub.com\u002Fuestc-icsp\u002FVoxelMapPlus_Public","not_stated (no LICENSE file found on main or master branch)",[53,57,60],{"relation":54,"title":55,"doi_or_url":56},"preprint","arXiv 2308.02799 v1 (2023-08-05), only version; six authors in a different order and without Kaiyong Zhao","https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.02799",{"relation":58,"title":59,"doi_or_url":50},"code_release","uestc-icsp\u002FVoxelMapPlus_Public",{"relation":61,"title":62,"doi_or_url":63},"predecessor_method","VoxelMap (Yuan et al., RA-L 2022)","yuan2022voxelmap",{"id":5,"kind":65,"shortName":7,"title":8,"authors":66,"year":9,"venue":74,"venueType":75,"publisher":76,"volumeIssuePages":77,"doi":78,"arxivId":79,"url":80,"firstPublicDate":81,"publicationStatus":16,"metadataStatus":82,"fulltextStatus":15,"era":10,"classicReason":83,"codeUrl":50,"cluster":11,"topics":84,"mdpi":85,"verification":86,"label":6,"fulltextRoute":87,"versionRead":88,"addedByCensus":89},"method",[67,68,69,70,71,72,73],"Chang Wu","Yuan You","Yifei Yuan","Xiaotong Kong","Ying Zhang","Qiyan Li","Kaiyong Zhao","IEEE Robotics and Automation Letters","journal","IEEE","9(1):427-434","10.1109\u002Flra.2023.3333736","2308.02799","https:\u002F\u002Fdoi.org\u002F10.1109\u002FLRA.2023.3333736","2023-08-05","metadata_verified","not_applicable",[11],false,"corrected","arXiv","arXiv v1 (2023-08-05; only arXiv version); RA-L version of record not read, and its author list differs",true,[91,99,103,107,113,116,122],{"category":92,"model":93,"canonical":94,"role":95,"dataset":96,"specs":97,"locator":98},"lidar","Livox HAP","Livox Hap","method input","VoxelMap++ own datasets (UESTC forest, grassland and corridors)","solid-state non-repetitive LiDAR, 120 x 25 deg FOV, 10 Hz; described as the first automotive-grade LiDAR for serial production","Sec. IV; Fig. 4",{"category":100,"model":101,"canonical":101,"role":95,"dataset":96,"specs":102,"locator":98},"imu","ZED 2i camera built-in IMU (written 'built-in Next-Gen IMU')","IMU with gyroscope, accelerometer, barometer and magnetometer; 500 Hz; synchronized with the LiDAR by IEEE 1588-2008",{"category":104,"model":105,"canonical":105,"role":95,"dataset":96,"specs":106,"locator":98},"platform","trolley (pushed cart)","sensors strapped down on the trolley",{"category":92,"model":108,"canonical":108,"role":109,"dataset":110,"specs":111,"locator":112},"Velodyne VLP-32C","dataset sensor","M2DGR","360 x 40 deg FOV, 10 Hz","Sec. IV",{"category":100,"model":114,"canonical":114,"role":109,"dataset":110,"specs":115,"locator":112},"Realsense D435i IMU (written 'VI-sensor Realsense d435i')","200 Hz",{"category":117,"model":118,"canonical":118,"role":119,"dataset":110,"specs":120,"locator":121},"gnss","GNSS-IMU system with real-time kinematic signals (model not stated)","reference or ground truth","ground truth","Sec. IV-A",{"category":123,"model":124,"canonical":124,"role":125,"dataset":126,"specs":127,"locator":112},"compute","laptop, 2.9 GHz 8-core CPU (model not stated)","compute for runtime",null,"16 GiB memory",[],{"totalRows":130,"groupCount":131,"groups":132,"others":532},21,4,[133,303,387,468],{"slug":134,"group":135,"sourceId":5,"sourceLabel":6,"table":136,"selfRows":137,"metrics":138,"seqs":143,"entrants":159,"cells":181,"outcomes":296,"locators":298,"hardware":299,"wordings":300,"notes":301},"voxelmappp2024-table-i","voxelmappp2024:Table I","Table I",7,[139],{"label":140,"unit":141,"statistic":142,"alignment":142},"ATE","m","not_reported",[144,147,149,151,153,155,157],{"dataset":110,"sequence":145,"environment":146},"Street02 (1484.62 m, day, long-term)","ground robot; M2DGR structured urban sequences (streets by day and night, dark room, hall, door from outdoors to indoors, lift between floors) (Sec. IV-A, Table I)",{"dataset":110,"sequence":148,"environment":146},"Street06 (479.63 m, night, straight line)",{"dataset":110,"sequence":150,"environment":146},"Street07 (1104.07 m, night, zigzag route)",{"dataset":110,"sequence":152,"environment":146},"Roomdark06 (72.53 m, room, complete darkness)",{"dataset":110,"sequence":154,"environment":146},"Hall05 (79.28 m, long-term, large overlap)",{"dataset":110,"sequence":156,"environment":146},"Door01 (285.51 m, outdoors to indoors)",{"dataset":110,"sequence":158,"environment":146},"Lift04 (142.78 m, first floor to second floor by lift)",[160,163,166,169,172,175,178,180],{"name":161,"methodId":162,"linkable":89,"proposed":85,"self":85},"A-LOAM","aloam_software",{"name":164,"methodId":165,"linkable":89,"proposed":85,"self":85},"LeGO-LOAM","legoloam2018",{"name":167,"methodId":168,"linkable":89,"proposed":85,"self":85},"LIO-SAM","liosam2020",{"name":170,"methodId":171,"linkable":89,"proposed":85,"self":85},"LINS","lins2020",{"name":173,"methodId":174,"linkable":89,"proposed":85,"self":85},"FAST-LIO2","fastlio2_2022",{"name":176,"methodId":177,"linkable":89,"proposed":85,"self":85},"Faster-LIO","fasterlio2022",{"name":179,"methodId":63,"linkable":89,"proposed":85,"self":85},"VoxelMap",{"name":7,"methodId":5,"linkable":89,"proposed":89,"self":89},[182,186,189,192,195,197,200,203,205,207,209,211,213,215,217,219,221,223,225,227,229,230,232,234,236,238,240,242,244,246,248,250,252,254,256,257,259,261,263,265,267,269,270,272,274,276,278,280,282,283,285,287,289,291,293,295],[183,183,183,184,185,183,185,185,183],0,5.299,-1,[183,183,187,188,185,183,185,185,183],1,0.628,[183,183,190,191,185,183,185,185,183],2,28.94,[183,183,193,194,185,183,185,185,183],3,0.314,[183,183,131,196,185,183,185,185,183],1.065,[183,183,198,199,185,183,185,185,183],5,0.274,[183,183,201,202,185,183,185,185,183],6,1.323,[187,183,183,204,185,183,185,185,183],20.021,[187,183,187,206,185,183,185,185,183],1.246,[187,183,190,208,185,183,185,185,183],35.437,[187,183,193,210,185,183,185,185,183],0.373,[187,183,131,212,185,183,185,185,183],1.03,[187,183,198,214,185,183,185,185,183],0.253,[187,183,201,216,185,183,185,185,183],1.37,[190,183,183,218,185,183,185,185,183],4.063,[190,183,187,220,185,183,185,185,183],0.417,[190,183,190,222,185,183,185,185,183],28.642,[190,183,193,224,185,183,185,185,183],0.324,[190,183,131,226,185,183,185,185,183],1.047,[190,183,198,228,185,183,185,185,183],0.268,[190,183,201,126,183,183,185,185,183],[193,183,183,231,185,183,185,185,183],5.636,[193,183,187,233,185,183,185,185,183],1.742,[193,183,190,235,185,183,185,185,183],12.009,[193,183,193,237,185,183,185,185,183],2.205,[193,183,131,239,185,183,185,185,183],1.01,[193,183,198,241,185,183,185,185,183],0.258,[193,183,201,243,185,183,185,185,183],1.318,[131,183,183,245,185,183,185,185,183],2.3236,[131,183,187,247,185,183,185,185,183],0.457,[131,183,190,249,185,183,185,185,183],11.7518,[131,183,193,251,185,183,185,185,183],0.3146,[131,183,131,253,185,183,185,185,183],1.0227,[131,183,198,255,185,183,185,185,183],0.2566,[131,183,201,126,183,183,185,185,183],[198,183,183,258,185,183,185,185,183],2.6667,[198,183,187,260,185,183,185,185,183],0.4138,[198,183,190,262,185,183,185,185,183],11.7363,[198,183,193,264,185,183,185,185,183],0.3124,[198,183,131,266,185,183,185,185,183],1.0311,[198,183,198,268,185,183,185,185,183],0.2522,[198,183,201,126,183,183,185,185,183],[201,183,183,271,185,183,185,185,183],1.7408,[201,183,187,273,185,183,185,185,183],0.4901,[201,183,190,275,185,183,185,185,183],13.7607,[201,183,193,277,185,183,185,185,183],0.2944,[201,183,131,279,185,183,185,185,183],0.9376,[201,183,198,281,185,183,185,185,183],0.2361,[201,183,201,126,183,183,185,185,183],[137,183,183,284,185,183,185,185,183],1.1608,[137,183,187,286,185,183,185,185,183],0.4118,[137,183,190,288,185,183,185,185,183],12.853,[137,183,193,290,185,183,185,185,183],0.2533,[137,183,131,292,185,183,185,185,183],0.8991,[137,183,198,294,185,183,185,185,183],0.217,[137,183,201,126,183,183,185,185,183],[297],"failed",[136],[],[],[302],"M2DGR structured urban sequences; ATE (m); A-LOAM, LeGO-LOAM, LIO-SAM and LINS copied from the M2DGR paper; FAST-LIO2, Faster-LIO, VoxelMap and VoxelMap++ computed with evo; same parameters for all sequences; X = failed",{"slug":304,"group":305,"sourceId":5,"sourceLabel":6,"table":306,"selfRows":198,"metrics":307,"seqs":310,"entrants":323,"cells":330,"outcomes":381,"locators":382,"hardware":383,"wordings":384,"notes":385},"voxelmappp2024-table-ii","voxelmappp2024:Table II","Table II",[308],{"label":309,"unit":141,"statistic":142,"alignment":45},"end-to-end error",[311,315,317,319,321],{"dataset":312,"sequence":313,"environment":314},"VoxelMap++ own datasets","loopE (329.4 m)","unstructured forest and grassland at the UESTC library entrance",{"dataset":312,"sequence":316,"environment":314},"loopF (373.6 m)",{"dataset":312,"sequence":318,"environment":314},"loopG (311.8 m)",{"dataset":312,"sequence":320,"environment":314},"loopH (519.6 m)",{"dataset":312,"sequence":322,"environment":314},"loopI (284.4 m)",[324,326,327,328,329],{"name":325,"methodId":126,"linkable":85,"proposed":85,"self":85},"LIO-Livox",{"name":173,"methodId":174,"linkable":89,"proposed":85,"self":85},{"name":176,"methodId":177,"linkable":89,"proposed":85,"self":85},{"name":179,"methodId":63,"linkable":89,"proposed":85,"self":85},{"name":7,"methodId":5,"linkable":89,"proposed":89,"self":89},[331,333,335,337,339,341,343,345,347,349,351,353,355,357,359,361,363,365,367,369,371,373,375,377,379],[183,183,183,332,185,183,185,185,183],5.9141,[183,183,187,334,185,183,185,185,183],2.816,[183,183,190,336,185,183,185,185,183],3.6342,[183,183,193,338,185,183,185,185,183],2.2157,[183,183,131,340,185,183,185,185,183],1.2061,[187,183,183,342,185,183,185,185,183],3.2134,[187,183,187,344,185,183,185,185,183],0.083,[187,183,190,346,185,183,185,185,183],4.2893,[187,183,193,348,185,183,185,185,183],0.8175,[187,183,131,350,185,183,185,185,183],0.1591,[190,183,183,352,185,183,185,185,183],5.7331,[190,183,187,354,185,183,185,185,183],1.3926,[190,183,190,356,185,183,185,185,183],1.6632,[190,183,193,358,185,183,185,185,183],1.705,[190,183,131,360,185,183,185,185,183],0.4856,[193,183,183,362,185,183,185,185,183],1.6847,[193,183,187,364,185,183,185,185,183],0.9577,[193,183,190,366,185,183,185,185,183],0.4433,[193,183,193,368,185,183,185,185,183],0.0492,[193,183,131,370,185,183,185,185,183],0.0894,[131,183,183,372,185,183,185,185,183],0.0336,[131,183,187,374,185,183,185,185,183],0.0441,[131,183,190,376,185,183,185,185,183],0.0389,[131,183,193,378,185,183,185,185,183],0.0734,[131,183,131,380,185,183,185,185,183],0.0406,[],[306],[],[],[386],"Own Livox HAP data pushed on a cart around closed loops; end-to-end error between start and terminal point (no RTK available)",{"slug":388,"group":389,"sourceId":5,"sourceLabel":6,"table":390,"selfRows":198,"metrics":391,"seqs":393,"entrants":405,"cells":411,"outcomes":462,"locators":463,"hardware":464,"wordings":465,"notes":466},"voxelmappp2024-table-iii","voxelmappp2024:Table III","Table III",[392],{"label":309,"unit":141,"statistic":142,"alignment":45},[394,397,399,401,403],{"dataset":312,"sequence":395,"environment":396},"loop1 (202.9 m)","indoor building with long degenerate corridors",{"dataset":312,"sequence":398,"environment":396},"loop2 (219.7 m)",{"dataset":312,"sequence":400,"environment":396},"loop3 (315.2 m)",{"dataset":312,"sequence":402,"environment":396},"loop4 (317.1 m)",{"dataset":312,"sequence":404,"environment":396},"loop5 (405.0 m)",[406,407,408,409,410],{"name":325,"methodId":126,"linkable":85,"proposed":85,"self":85},{"name":173,"methodId":174,"linkable":89,"proposed":85,"self":85},{"name":176,"methodId":177,"linkable":89,"proposed":85,"self":85},{"name":179,"methodId":63,"linkable":89,"proposed":85,"self":85},{"name":7,"methodId":5,"linkable":89,"proposed":89,"self":89},[412,414,416,418,420,422,424,426,428,430,432,434,436,438,440,442,444,446,448,450,452,454,456,458,460],[183,183,183,413,185,183,185,185,183],7.1192,[183,183,187,415,185,183,185,185,183],18.5059,[183,183,190,417,185,183,185,185,183],4.2932,[183,183,193,419,185,183,185,185,183],10.027,[183,183,131,421,185,183,185,185,183],19.6729,[187,183,183,423,185,183,185,185,183],2.3812,[187,183,187,425,185,183,185,185,183],2.8175,[187,183,190,427,185,183,185,185,183],4.2703,[187,183,193,429,185,183,185,185,183],13.2844,[187,183,131,431,185,183,185,185,183],7.107,[190,183,183,433,185,183,185,185,183],2.3488,[190,183,187,435,185,183,185,185,183],1.1595,[190,183,190,437,185,183,185,185,183],8.1295,[190,183,193,439,185,183,185,185,183],1.6455,[190,183,131,441,185,183,185,185,183],10.1804,[193,183,183,443,185,183,185,185,183],9.3457,[193,183,187,445,185,183,185,185,183],1.1388,[193,183,190,447,185,183,185,185,183],3.8561,[193,183,193,449,185,183,185,185,183],5.3355,[193,183,131,451,185,183,185,185,183],14.2049,[131,183,183,453,185,183,185,185,183],0.5305,[131,183,187,455,185,183,185,185,183],0.5671,[131,183,190,457,185,183,185,185,183],0.2543,[131,183,193,459,185,183,185,185,183],1.3094,[131,183,131,461,185,183,185,185,183],1.4478,[],[390],[],[],[467],"Own Livox HAP data pushed on a cart around closed loops; end-to-end error between start and terminal point",{"slug":469,"group":470,"sourceId":5,"sourceLabel":6,"table":471,"selfRows":131,"metrics":472,"seqs":480,"entrants":487,"cells":492,"outcomes":525,"locators":526,"hardware":527,"wordings":529,"notes":530},"voxelmappp2024-table-iv","voxelmappp2024:Table IV","Table IV",[473,477],{"label":474,"unit":475,"statistic":476,"alignment":83},"Avg. comp. time","ms","mean",{"label":478,"unit":479,"statistic":142,"alignment":83},"Mem usage","MB",[481,484],{"dataset":312,"sequence":482,"environment":483},"small scale (loop1, 202.9 m)","indoor corridor",{"dataset":312,"sequence":485,"environment":486},"large scale (loopE, 329.4 m)","forest and grassland",[488,489,490,491],{"name":173,"methodId":174,"linkable":89,"proposed":85,"self":85},{"name":176,"methodId":177,"linkable":89,"proposed":85,"self":85},{"name":179,"methodId":63,"linkable":89,"proposed":85,"self":85},{"name":7,"methodId":5,"linkable":89,"proposed":89,"self":89},[493,495,497,499,501,503,505,507,509,511,513,515,517,519,521,523],[183,183,183,494,185,183,183,185,183],11.5985,[183,187,183,496,185,183,183,185,183],201.86,[183,183,187,498,185,183,183,185,183],35.4858,[183,187,187,500,185,183,183,185,183],228.23,[187,183,183,502,185,183,183,185,183],7.3461,[187,187,183,504,185,183,183,185,183],135.11,[187,183,187,506,185,183,183,185,183],15.291,[187,187,187,508,185,183,183,185,183],200.21,[190,183,183,510,185,183,183,185,183],5.683,[190,187,183,512,185,183,183,185,183],158.06,[190,183,187,514,185,183,183,185,183],14.5815,[190,187,187,516,185,183,183,185,183],201.22,[193,183,183,518,185,183,183,185,183],4.8763,[193,187,183,520,185,183,183,185,183],126.45,[193,183,187,522,185,183,183,185,183],13.8323,[193,187,187,524,185,183,183,185,183],195.91,[],[471],[528],"laptop, 2.9 GHz 8 cores, 16 GiB",[],[531],"Resource usage: average computation time per scan and memory usage",[],1790510660952]