[{"data":1,"prerenderedAt":590},["ShallowReactive",2],{"method-dufomap2024":3},{"method":4,"reference":54,"equipment":75,"figures":123,"results":161},{"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":28,"sensors":34,"platform":37,"estimator":41,"association":42,"timeModel":41,"deskew":43,"loopClosure":41,"globalOptimization":41,"mapRepresentation":44,"prior":45,"outputGeometry":46,"compute":47,"codeUrl":48,"codeLicense":49,"relatedVersions":50},"dufomap2024","Duberg et al., 2024","DUFOMap","DUFOMap: Efficient Dynamic Awareness Mapping",2024,"recent","C06","map_representation_or_reconstruction","DUFOMap 不直接偵測動態物，而是辨識「曾被完整觀測為空」的空洞區域（void region）：以射線投射判斷體素是否被完整看空，一旦成立，其他時刻落在其中的點即為動態點。方法以 UFOMap 八元樹實作，並加入考慮量測雜訊與位姿誤差的保守判定，所有情境使用同一組參數，可線上或後處理執行，也能處理非連續的測量級掃描站資料。","DUFOMap marks voxels ever observed fully empty via ray casting and labels any later points inside them as dynamic, using one parameter set across sensors and working online or offline.","full_text_reviewed","peer_reviewed_published","main_body","以測量級 TLS（Leica RTC360，取自 MCD VIRAL 資料集）離散站點資料做定性示範（Sec. IV-A、V-B2、Fig. 1）；作者對 OctoMap 每體素樣本過少、ERASOR 固定高度門檻與 Dynablox 需連續資料等不適用性，是依方法原理所作的分析，而非實測比較。論文索引詞列有 Robotics and Automation in Construction，並以測量與營建產業的點雲需求作為動機（Sec. I）。另有 128 線光達火車站（DOALS）與 Livox Mid-360 兩層樓結構的定性資料（Sec. V-B1）。與工地 TLS 與移動掃描混合資料的清理相關（推論）；無工地資料或工程量測驗證。",[20,21],"public_benchmark","cross_site",[23,24,25,26,27],"Same parameters across all scenarios with accuracy better than or on par with compared methods (abstract)","Highest AA on KITTI 00 (98.34%), KITTI 01 (96.12%) and semi-indoor (90.94%); second on Argoverse 2 (92.70% versus 94.46% for Dynablox) (Table I)","Lowest run time per point cloud among the compared methods (Table II)","With KISS-ICP poses AA reached 99.03% on KITTI 00 (Table III)","Handles non-sequential, dense survey scanner data (Leica RTC360) qualitatively (Sec. V-B2)",[29,30,31,32,33],"Sparse LiDAR data lowers dynamic accuracy because neighbouring voxels must be observed (Sec. V-E)","A region must be seen void at least once, so parts of slowly moving large objects may be missed (Sec. V-E)","All methods, including DUFOMap, are influenced by pose quality; worse poses make scenes appear more dynamic (Sec. V-C, Table III)","Online use (DUFOMap*) lowers DA; on the semi-indoor data people standing still are labelled dynamic only once they have moved (Sec. V-A1, Table I)","Results depend on the noise and localization margins: without ds and dp SA fell to 14.89% on KITTI 00 (Sec. V-D, Table IV)",[35,36],"3D LiDAR","terrestrial laser scanner (survey data, qualitative)",[38,39,40],"vehicle","static survey scanner","not_verified","not_applicable","ray casting to classify voxels observed completely empty at least once (void regions); points inside void regions at other times are dynamic","not_reported","UFOMap octree voxels with a void flag","sensor poses supplied with point clouds","static map and dynamic point labels; online or post-processing","desktop Intel Core i9-12900KF; robot Intel NUC with Intel Core i7-8559U (Sec. IV-D); mean run time per point cloud 0.062 s on KITTI highway (64-channel) and 0.019 s on the 16-channel semi-indoor data, the lowest of the compared methods (Table II); about 20 Hz on the 4-core NUC with range limited to 20 m, versus less than 10 Hz for Dynablox (Sec. V-A2)","https:\u002F\u002Fgithub.com\u002FKTH-RPL\u002Fdufomap","BSD-3-Clause (LICENSE file)",[51],{"relation":52,"title":53,"doi_or_url":48},"code_release","KTH-RPL\u002Fdufomap",{"id":5,"kind":55,"shortName":7,"title":8,"authors":56,"year":9,"venue":61,"venueType":62,"publisher":63,"volumeIssuePages":64,"doi":65,"arxivId":66,"url":67,"firstPublicDate":68,"publicationStatus":16,"metadataStatus":69,"fulltextStatus":15,"era":10,"classicReason":41,"codeUrl":48,"cluster":11,"topics":70,"mdpi":71,"verification":72,"label":6,"fulltextRoute":73,"versionRead":74,"addedByCensus":71},"method",[57,58,59,60],"Daniel Duberg","Qingwen Zhang","Mingkai Jia","Patric Jensfelt","IEEE Robotics and Automation Letters","journal","IEEE","9(6):5038-5045","10.1109\u002Flra.2024.3387658","2403.01449","https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.01449","2024-03-03","metadata_verified",[11],false,"corrected","arXiv","arXiv v2 (2024-04-12), accepted RA-L preprint (header 'accepted March 2024'); IEEE version of record 9(6):5038-5045 not read",[76,84,89,95,101,106,112,119],{"category":77,"model":78,"canonical":79,"role":80,"dataset":81,"specs":82,"locator":83},"lidar","HDL-64E","Velodyne HDL-64E","dataset sensor","KITTI (SemanticKITTI labels and poses)","64-channel; about 0.1 million points per scan, 30 deg vertical FoV (as compared in Sec. IV-A)","Sec. IV-A",{"category":77,"model":85,"canonical":86,"role":80,"dataset":87,"specs":88,"locator":83},"VLP-32C (two sensors)","Velodyne VLP-32C","Argoverse 2 big city","urban driving data with various dynamic objects",{"category":77,"model":90,"canonical":91,"role":80,"dataset":92,"specs":93,"locator":94},"VLP-16","Velodyne VLP-16","Semi-indoor (self-collected)","sparse 16-channel LiDAR in a highly structured semi-indoor environment","Sec. IV-A, Fig. 5",{"category":96,"model":97,"canonical":97,"role":80,"dataset":98,"specs":99,"locator":100},"tls_scanner","Leica RTC360","MCD VIRAL (part)","survey 3D laser scanner; about 1.3 million points per scan, 300 deg vertical FoV; discrete stations with large height differences","Sec. I, Sec. IV-A, Fig. 1",{"category":77,"model":102,"canonical":102,"role":80,"dataset":103,"specs":104,"locator":105},"128-channel LiDAR (model not reported)","DOALS","highly dynamic train station","Sec. IV-A, Sec. V-B1, Fig. 6",{"category":77,"model":107,"canonical":108,"role":80,"dataset":109,"specs":110,"locator":111},"Livox Mid-360","Livox MID-360","two-floor structure sequence","two-floor structure","Sec. IV-A, Fig. 7",{"category":113,"model":114,"canonical":114,"role":115,"dataset":116,"specs":117,"locator":118},"compute","Intel Core i9-12900KF","compute for runtime",null,"desktop; main experiments","Sec. IV-D",{"category":113,"model":120,"canonical":120,"role":115,"dataset":116,"specs":121,"locator":122},"Intel NUC with Intel Core i7-8559U","robot computer, 4-core CPU","Sec. IV-D, Sec. V-A2",[124,137,143,151],{"refId":5,"refLabel":6,"fig":125,"whatZh":126,"license":127,"licenseUrl":128,"sourceUrl":129,"src":130,"width":131,"height":132,"thumb":133,"thumbWidth":134,"thumbHeight":135,"modified":136},"Fig. 1","Leica RTC360 測量級掃描所得點雲地圖受走動行人影響；DUFOMap 偵測動態點（分群著色）並輸出清理後地圖","CC BY-NC-SA 4.0","https:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby-nc-sa\u002F4.0\u002F","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2403.01449v2\u002Fbackground.png","\u002Ffigure-files\u002Fdufomap2024\u002Ffig-1.webp",1400,788,"\u002Ffigure-files\u002Fdufomap2024\u002Ffig-1.thumb.webp",480,270,"resized to at most 1400 px wide and converted to WebP",{"refId":5,"refLabel":6,"fig":138,"whatZh":139,"license":127,"licenseUrl":128,"sourceUrl":140,"src":141,"width":131,"height":132,"thumb":142,"thumbWidth":134,"thumbHeight":135,"modified":136},"Fig. 5","自行蒐集的稀疏 VLP-16 半室內資料：人工標註真值與 ERASOR、OctoMap、Dynablox、DUFOMap 的動態點判定及清理後地圖","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2403.01449v2\u002Fsemindoor.png","\u002Ffigure-files\u002Fdufomap2024\u002Ffig-5.webp","\u002Ffigure-files\u002Fdufomap2024\u002Ffig-5.thumb.webp",{"refId":5,"refLabel":6,"fig":144,"whatZh":145,"license":127,"licenseUrl":128,"sourceUrl":146,"src":147,"width":131,"height":148,"thumb":149,"thumbWidth":134,"thumbHeight":150,"modified":136},"Fig. 6(b)","DOALS 火車站高動態場景經 DUFOMap 清理後的地圖","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2403.01449v2\u002Fdoals_station_seq2_dufo.png","\u002Ffigure-files\u002Fdufomap2024\u002Ffig-6-b.webp",754,"\u002Ffigure-files\u002Fdufomap2024\u002Ffig-6-b.thumb.webp",259,{"refId":5,"refLabel":6,"fig":152,"whatZh":153,"license":127,"licenseUrl":128,"sourceUrl":154,"src":155,"width":156,"height":157,"thumb":158,"thumbWidth":134,"thumbHeight":159,"modified":160},"Fig. 7(b)","兩層樓複雜結構經 DUFOMap 清理後的地圖，依高度著色並移除部分牆面以利檢視","https:\u002F\u002Farxiv.org\u002Fhtml\u002F2403.01449v2\u002Ftwo_floor_dufo_color.png","\u002Ffigure-files\u002Fdufomap2024\u002Ffig-7-b.webp",1167,981,"\u002Ffigure-files\u002Fdufomap2024\u002Ffig-7-b.thumb.webp",403,"converted to WebP",{"totalRows":162,"groupCount":163,"groups":164,"others":584},51,5,[165,365,419,522],{"slug":166,"group":167,"sourceId":5,"sourceLabel":6,"table":168,"selfRows":169,"metrics":170,"seqs":179,"entrants":193,"cells":211,"outcomes":359,"locators":360,"hardware":361,"wordings":362,"notes":363},"dufomap2024-table-i","dufomap2024:Table I","Table I",24,[171,175,177],{"label":172,"unit":173,"statistic":43,"alignment":174},"SA (static accuracy, share of static points correctly kept)","%","none",{"label":176,"unit":173,"statistic":43,"alignment":174},"DA (dynamic accuracy, share of dynamic points correctly labelled)",{"label":178,"unit":173,"statistic":43,"alignment":174},"AA (associated accuracy, sqrt(SA x DA))",[180,183,186,190],{"dataset":81,"sequence":181,"environment":182},"00 small town","small town (HDL-64E)",{"dataset":81,"sequence":184,"environment":185},"01 highway","highway (HDL-64E)",{"dataset":187,"sequence":188,"environment":189},"Argoverse 2","big city","urban big city (two VLP-32C)",{"dataset":92,"sequence":191,"environment":192},"semi-indoor","highly structured semi-indoor area, sparse 16-channel LiDAR (VLP-16)",[194,198,201,204,206,209],{"name":195,"methodId":196,"linkable":197,"proposed":71,"self":71},"Removert [8]","removert2020",true,{"name":199,"methodId":200,"linkable":197,"proposed":71,"self":71},"ERASOR [9]","erasor2021",{"name":202,"methodId":203,"linkable":197,"proposed":71,"self":71},"OctoMap [16]","hornung2013octomap",{"name":205,"methodId":5,"linkable":197,"proposed":197,"self":197},"DUFOMap (Ours)",{"name":207,"methodId":208,"linkable":197,"proposed":71,"self":71},"Dynablox [17]","dynablox2023",{"name":210,"methodId":5,"linkable":197,"proposed":197,"self":197},"DUFOMap* (Ours, online)",[212,216,219,222,224,226,228,230,232,234,237,239,241,243,245,247,249,251,253,255,257,259,261,263,265,267,269,271,273,275,277,279,281,283,285,287,289,291,293,295,297,299,301,303,305,307,309,311,312,315,317,319,321,323,325,327,329,330,332,334,336,338,340,342,344,346,348,350,352,354,356,358],[213,213,213,214,215,213,215,215,213],0,99.44,-1,[213,217,213,218,215,213,215,215,213],1,41.53,[213,220,213,221,215,213,215,215,213],2,64.26,[213,213,217,223,215,213,215,215,213],97.81,[213,217,217,225,215,213,215,215,213],39.56,[213,220,217,227,215,213,215,215,213],62.2,[213,213,220,229,215,213,215,215,213],98.97,[213,217,220,231,215,213,215,215,213],31.16,[213,220,220,233,215,213,215,215,213],55.53,[213,213,235,236,215,213,215,215,213],3,99.96,[213,217,235,238,215,213,215,215,213],12.15,[213,220,235,240,215,213,215,215,213],34.85,[217,213,213,242,215,213,215,215,213],66.7,[217,217,213,244,215,213,215,215,213],98.54,[217,220,213,246,215,213,215,215,213],81.07,[217,213,217,248,215,213,215,215,213],98.12,[217,217,217,250,215,213,215,215,213],90.94,[217,220,217,252,215,213,215,215,213],94.46,[217,213,220,254,215,213,215,215,213],77.51,[217,217,220,256,215,213,215,215,213],99.18,[217,220,220,258,215,213,215,215,213],87.68,[217,213,235,260,215,213,215,215,213],94.9,[217,217,235,262,215,213,215,215,213],66.26,[217,220,235,264,215,213,215,215,213],79.3,[220,213,213,266,215,213,215,215,213],68.05,[220,217,213,268,215,213,215,215,213],99.69,[220,220,213,270,215,213,215,215,213],82.37,[220,213,217,272,215,213,215,215,213],55.55,[220,217,217,274,215,213,215,215,213],99.59,[220,220,217,276,215,213,215,215,213],74.38,[220,213,220,278,215,213,215,215,213],69.04,[220,217,220,280,215,213,215,215,213],97.5,[220,220,220,282,215,213,215,215,213],82.04,[220,213,235,284,215,213,215,215,213],88.97,[220,217,235,286,215,213,215,215,213],82.18,[220,220,235,288,215,213,215,215,213],85.51,[235,213,213,290,215,213,215,215,213],97.96,[235,217,213,292,215,213,215,215,213],98.72,[235,220,213,294,215,213,215,215,213],98.34,[235,213,217,296,215,213,215,215,213],98.09,[235,217,217,298,215,213,215,215,213],94.2,[235,220,217,300,215,213,215,215,213],96.12,[235,213,220,302,215,213,215,215,213],96.67,[235,217,220,304,215,213,215,215,213],88.9,[235,220,220,306,215,213,215,215,213],92.7,[235,213,235,308,215,213,215,215,213],99.64,[235,217,235,310,215,213,215,215,213],83,[235,220,235,250,215,213,215,215,213],[313,213,213,314,215,213,215,215,213],4,96.76,[313,217,213,316,215,213,215,215,213],90.68,[313,220,213,318,215,213,215,215,213],93.67,[313,213,217,320,215,213,215,215,213],96.33,[313,217,217,322,215,213,215,215,213],68.01,[313,220,217,324,215,213,215,215,213],80.94,[313,213,220,326,215,213,215,215,213],96.08,[313,217,220,328,215,213,215,215,213],92.87,[313,220,220,252,215,213,215,215,213],[313,213,235,331,215,213,215,215,213],98.81,[313,217,235,333,215,213,215,215,213],36.49,[313,220,235,335,215,213,215,215,213],60.05,[163,213,213,337,215,213,215,215,213],98.37,[163,217,213,339,215,213,215,215,213],92.37,[163,220,213,341,215,213,215,215,213],95.31,[163,213,217,343,215,213,215,215,213],98.48,[163,217,217,345,215,213,215,215,213],81.34,[163,220,217,347,215,213,215,215,213],89.5,[163,213,220,349,215,213,215,215,213],98.66,[163,217,220,351,215,213,215,215,213],73.98,[163,220,220,353,215,213,215,215,213],85.43,[163,213,235,355,215,213,215,215,213],99.94,[163,217,235,357,215,213,215,215,213],54.76,[163,220,235,351,215,213,215,215,213],[],[168],[],[],[364],"Point-wise dynamic point removal accuracy (%) following the DynamicMap benchmark protocol; Removert, ERASOR, OctoMap and DUFOMap evaluated offline, Dynablox and DUFOMap* online (each scan classified with the map built so far); DUFOMap uses the same parameters for all data (voxel 0.1 m, ds 0.2 m, dp 1), Removert and ERASOR per-dataset optimized parameters; KITTI labels and poses from SemanticKITTI",{"slug":366,"group":367,"sourceId":5,"sourceLabel":6,"table":368,"selfRows":369,"metrics":370,"seqs":374,"entrants":376,"cells":387,"outcomes":413,"locators":414,"hardware":415,"wordings":416,"notes":417},"dufomap2024-table-iv","dufomap2024:Table IV","Table IV",15,[371,372,373],{"label":172,"unit":173,"statistic":43,"alignment":174},{"label":176,"unit":173,"statistic":43,"alignment":174},{"label":178,"unit":173,"statistic":43,"alignment":174},[375],{"dataset":81,"sequence":181,"environment":182},[377,379,381,383,385],{"name":378,"methodId":5,"linkable":197,"proposed":197,"self":197},"DUFOMap (w\u002Fo ds, dp, v = 0.1)",{"name":380,"methodId":5,"linkable":197,"proposed":197,"self":197},"DUFOMap (ds = 0.2, v = 0.1)",{"name":382,"methodId":5,"linkable":197,"proposed":197,"self":197},"DUFOMap (dp = 1, v = 0.1)",{"name":384,"methodId":5,"linkable":197,"proposed":197,"self":197},"DUFOMap (ds = 0.2, dp = 1, v = 0.2)",{"name":386,"methodId":5,"linkable":197,"proposed":197,"self":197},"DUFOMap (ds = 0.2, dp = 1, v = 0.1)",[388,390,392,394,396,397,399,401,402,404,406,408,410,411,412],[213,213,213,389,215,213,215,215,213],14.89,[213,217,213,391,215,213,215,215,213],99.99,[213,220,213,393,215,213,215,215,213],38.58,[217,213,213,395,215,213,215,215,213],30.29,[217,217,213,391,215,213,215,215,213],[217,220,213,398,215,213,215,215,213],55.03,[220,213,213,400,215,213,215,215,213],91.89,[220,217,213,229,215,213,215,215,213],[220,220,213,403,215,213,215,215,213],95.37,[235,213,213,405,215,213,215,215,213],92.97,[235,217,213,407,215,213,215,215,213],98.24,[235,220,213,409,215,213,215,215,213],95.57,[313,213,213,290,215,213,215,215,213],[313,217,213,292,215,213,215,215,213],[313,220,213,294,215,213,215,215,213],[],[368],[],[],[418],"Ablation on KITTI 00: sensor-noise margin ds (m), localization margin dp (voxels) and voxel size v (m); SA, DA, AA in %",{"slug":420,"group":421,"sourceId":5,"sourceLabel":6,"table":422,"selfRows":423,"metrics":424,"seqs":428,"entrants":436,"cells":443,"outcomes":516,"locators":517,"hardware":518,"wordings":519,"notes":520},"dufomap2024-table-iii","dufomap2024:Table III","Table III",9,[425,426,427],{"label":172,"unit":173,"statistic":43,"alignment":174},{"label":176,"unit":173,"statistic":43,"alignment":174},{"label":178,"unit":173,"statistic":43,"alignment":174},[429,432,434],{"dataset":430,"sequence":431,"environment":182},"KITTI (SemanticKITTI labels)","00 (KITTI GT poses)",{"dataset":430,"sequence":433,"environment":182},"00 (SuMa poses (SemanticKITTI))",{"dataset":430,"sequence":435,"environment":182},"00 (KISS-ICP poses)",[437,438,439,441,442],{"name":195,"methodId":196,"linkable":197,"proposed":71,"self":71},{"name":199,"methodId":200,"linkable":197,"proposed":71,"self":71},{"name":440,"methodId":203,"linkable":197,"proposed":71,"self":71},"Octomap [16]",{"name":207,"methodId":208,"linkable":197,"proposed":71,"self":71},{"name":205,"methodId":5,"linkable":197,"proposed":197,"self":197},[444,445,447,449,450,451,452,454,456,458,460,462,464,465,466,467,469,471,473,475,477,479,480,481,482,484,486,488,490,492,493,494,495,496,498,500,502,504,506,507,508,509,510,512,514],[213,213,213,256,215,213,215,215,213],[213,217,213,446,215,213,215,215,213],41.71,[213,220,213,448,215,213,215,215,213],64.32,[213,213,217,214,215,213,215,215,213],[213,217,217,218,215,213,215,215,213],[213,220,217,221,215,213,215,215,213],[213,213,220,453,215,213,215,215,213],99.55,[213,217,220,455,215,213,215,215,213],41.45,[213,220,220,457,215,213,215,215,213],64.23,[217,213,213,459,215,213,215,215,213],63.83,[217,217,213,461,215,213,215,215,213],98.35,[217,220,213,463,215,213,215,215,213],79.23,[217,213,217,242,215,213,215,215,213],[217,217,217,244,215,213,215,215,213],[217,220,217,246,215,213,215,215,213],[217,213,220,468,215,213,215,215,213],67.86,[217,217,220,470,215,213,215,215,213],98.68,[217,220,220,472,215,213,215,215,213],81.83,[220,213,213,474,215,213,215,215,213],54.81,[220,217,213,476,215,213,215,215,213],99.56,[220,220,213,478,215,213,215,215,213],73.87,[220,213,217,266,215,213,215,215,213],[220,217,217,268,215,213,215,215,213],[220,220,217,270,215,213,215,215,213],[220,213,220,483,215,213,215,215,213],62.28,[220,217,220,485,215,213,215,215,213],99.85,[220,220,220,487,215,213,215,215,213],78.85,[235,213,213,489,215,213,215,215,213],95.5,[235,217,213,491,215,213,215,215,213],89.34,[235,220,213,339,215,213,215,215,213],[235,213,217,314,215,213,215,215,213],[235,217,217,316,215,213,215,215,213],[235,220,217,318,215,213,215,215,213],[235,213,220,497,215,213,215,215,213],98.31,[235,217,220,499,215,213,215,215,213],90.97,[235,220,220,501,215,213,215,215,213],94.57,[313,213,213,503,215,213,215,215,213],92.57,[313,217,213,505,215,213,215,215,213],98.52,[313,220,213,489,215,213,215,215,213],[313,213,217,290,215,213,215,215,213],[313,217,217,292,215,213,215,215,213],[313,220,217,294,215,213,215,215,213],[313,213,220,511,215,213,215,215,213],99.33,[313,217,220,513,215,213,215,215,213],98.73,[313,220,220,515,215,213,215,215,213],99.03,[],[422],[],[],[521],"Influence of the pose source on dynamic point removal, KITTI sequence 00: KITTI odometry ground-truth poses, SemanticKITTI poses estimated by SuMa, and KISS-ICP poses; SA, DA, AA in %",{"slug":523,"group":524,"sourceId":5,"sourceLabel":6,"table":525,"selfRows":220,"metrics":526,"seqs":531,"entrants":537,"cells":543,"outcomes":567,"locators":568,"hardware":569,"wordings":571,"notes":582},"dufomap2024-table-ii","dufomap2024:Table II","Table II",[527],{"label":528,"unit":529,"statistic":530,"alignment":174},"Run time per point cloud [s]","s","mean",[532,535],{"dataset":533,"sequence":184,"environment":534},"KITTI","highway, 64-channel LiDAR",{"dataset":92,"sequence":191,"environment":536},"16-channel 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