[{"data":1,"prerenderedAt":557},["ShallowReactive",2],{"method-locus2021":3},{"method":4,"reference":70,"equipment":99,"figures":146,"results":147},{"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":24,"limitations":29,"sensors":35,"platform":39,"estimator":42,"association":43,"timeModel":44,"deskew":45,"loopClosure":46,"globalOptimization":47,"mapRepresentation":48,"prior":49,"outputGeometry":50,"compute":51,"codeUrl":52,"codeLicense":53,"relatedVersions":54},"locus2021","Palieri et al., 2021","LOCUS","LOCUS: A Multi-Sensor Lidar-Centric Solution for High-Precision Odometry and 3D Mapping in Real-Time",2021,"recent","C07","odometry_with_local_mapping","LOCUS 是以 LiDAR 為主的里程計：每顆 LiDAR 的點先依 IMU 或其他里程計做運動畸變校正，再依已知外參合併，經體素與隨機降採樣後，以多執行緒 GICP 依序做掃描對掃描與掃描對子地圖配準。其他感測來源（VIO、KIO、輪式慣性里程計或 IMU 旋轉）不做緊耦合，而是由健康監測依固定優先順序挑出仍健康者，只提供 GICP 的初始值；所有來源失效時退回純 LiDAR 里程計。系統另可依情境啟用平地假設以抑制 Z 向與俯仰、滾轉誤差，並曾是 CoSTAR 團隊贏得 DARPA SubT Urban Circuit 解決方案的關鍵元件。","LiDAR-centric odometry that motion-corrects and merges multiple LiDARs, filters the cloud and runs multithreaded GICP scan-to-scan and scan-to-submap registration, with a health-aware priority queue that loosely injects the best available VIO, KIO, WIO or IMU estimate as the GICP prior and falls back to pure LiDAR odometry when all sources fail.","full_text_reviewed","peer_reviewed_published","main_body","場域為停用的 Satsop 發電廠（Urban Alpha 與 Beta 課程，長而缺乏特徵的走廊與大空間）與 Bruceton 研究礦坑（Tunnel 課程），屬既有工業建築與地下環境，並非施工中工地。地圖誤差是先以 ICP 把重建地圖對齊 DARPA 提供的真值地圖，再計算雲到雲誤差的 RMSE，屬於有獨立幾何參考的地圖評估；軌跡參考則由 LOCUS 在真值地圖上配準產生。多感測器健康監測與逐級退回純 LiDAR 的設計，可供長時間巡檢或施工機器人參考（推論）。",[20,21,22,23],"underground_or_tunnel","completed_building","independent_reference","task_level_validation",[25,26,27,28],"Equal to or better than the compared open-source LiDAR odometry systems on all three field datasets, by APE and by map error against the DARPA ground-truth map (Sec. III-B1; Table II)","Only method with negligible degradation under WIO and IMU failure, WIO failure and a 10 s LiDAR gap (Table III; Sec. III-B2)","Husky dropped no LiDAR scans in the four live competition runs (Table V)","Live Spot run in Urban Alpha 2: mean APE 0.586 m and maximum APE 2.599 m (Sec. III-C2)",[30,31,32,33,34],"Highest CPU load among the compared methods (mean 2.72 cores; Table II; Sec. III-B3; Sec. IV)","LOAM-type feature filtering can give greater accuracy but needs about 25% more CPU and was not real time on the test computer (Sec. III-A)","Spot drops about 2 scans per second on its less powerful onboard computer (Table V; Sec. III-C2)","The health check used in the reported implementation is only a message-rate check (Sec. II-B1)","The reference trajectories were produced by running LOCUS scan matching on the DARPA ground-truth map, so the APE reference is not independent of the evaluated method family (dataset description, Sec. III) (inference)",[36,37,38],"one or more 360-degree 3D LiDARs (two Velodyne VLP16 on Husky, one flat and one pitched forward 30 deg; one VLP16 on Spot)","IMU (Vector Nav 100 on Husky), used for rotation priors and motion distortion correction","external odometry used loosely: wheel-inertial (WIO), visual-inertial (VIO) and kinematic-inertial (KIO) odometry",[40,41],"wheeled UGV (Clearpath Husky, skid-steer)","legged (Boston Dynamics Spot)","loosely coupled: a health monitor (in this implementation a message-rate check above 1 Hz) selects the highest-priority healthy source from a static priority queue (Spot: VIO, KIO, IMU, none; Husky: VIO if present, WIO, IMU, none); its relative motion interpolated at LiDAR timestamps seeds multithreaded GICP scan-to-scan and then scan-to-submap registration; the odometry is the integration of the incremental transforms (Sec. II-B)","dense GICP on point clouds filtered by a 0.1 m voxel grid and a random downsampling filter (90%); scan-to-submap against a local region of the global map (Sec. II-A, II-B)","discrete per-scan poses; the selected odometry source is buffered and interpolated at LiDAR timestamps (Sec. II-B1)","motion distortion correction of each point informed by the IMU or an external odometry source, before multi-LiDAR merging (Sec. II-A)","none inside LOCUS; loop closures of all compared methods were disabled; in the competition the LOCUS output fed a robust odometry aggregator and a separate back-end SLAM (footnote 9, Sec. III-C2)","none within LOCUS","global point-cloud map stored in an octree (minimum resolution 0.001 m) accumulated every 1 m of translation or 30 deg of rotation (Sec. II-B3)","optional flat ground assumption (FGA), activated from context (e.g., single-floor exploration) or from an IMU monitor of near-zero roll and pitch; known rigid transforms between LiDARs for merging (Sec. II-A, II-C)","6-DoF odometry and an accumulated 3D point-cloud map (Figs. 1, 5)","per-scan timing and CPU load profiled on an Intel Hades Canyon NUC8i7HVKVA (4 x 1.9 GHz, 32 GB RAM, Ubuntu 18.04); LOCUS mean CPU load 2.72 cores, the highest of the compared methods; on the robots Husky used an AMD Ryzen 9 3900X (12 cores, 3.8 GHz) and Spot an Intel NUC7i7DN (4 cores, 1.9 GHz); GICP normal computation is over 70% of the computation and uses 4 threads by default (Sec. II-B4, III-B3, III-C1; Table II)","https:\u002F\u002Fgithub.com\u002FNeBula-Autonomy\u002FLOCUS","MIT (GitHub license metadata and LICENSE header, copyright 2022 NeBula Autonomy); public code follows the LOCUS 2.0 line",[55,59,63,67],{"relation":56,"title":57,"doi_or_url":58},"preprint","LOCUS (arXiv v1, accepted version)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2012.14447",{"relation":60,"title":61,"doi_or_url":62},"correction","Corrections to 'LOCUS: A Multi-Sensor Lidar-Centric Solution for High-Precision Odometry and 3D Mapping in Real-Time' [Apr 21 421-428], RA-L 6(2):3760; corrects the affiliations of Morrell, Ebadi, Nash and Agha-mohammadi, who had been wrongly listed with the Polytechnic University of Bari; no technical content changed","https:\u002F\u002Fdoi.org\u002F10.1109\u002FLRA.2021.3062780",{"relation":64,"title":65,"doi_or_url":66},"successor","LOCUS 2.0: Robust and Computationally Efficient Lidar Odometry for Real-Time 3D Mapping (Reinke et al., RA-L 2022; corpus id locus2_2022), whose line the public repository follows","https:\u002F\u002Fdoi.org\u002F10.1109\u002FLRA.2022.3181357",{"relation":68,"title":69,"doi_or_url":52},"code_release","NeBula-Autonomy\u002FLOCUS (repository README asks users to cite LOCUS 2.0 and LOCUS)",{"id":5,"kind":71,"shortName":7,"title":8,"authors":72,"year":9,"venue":83,"venueType":84,"publisher":85,"volumeIssuePages":86,"doi":87,"arxivId":88,"url":89,"firstPublicDate":90,"publicationStatus":16,"metadataStatus":91,"fulltextStatus":15,"era":10,"classicReason":92,"codeUrl":52,"cluster":11,"topics":93,"mdpi":94,"verification":95,"label":6,"fulltextRoute":96,"versionRead":97,"addedByCensus":98},"method",[73,74,75,76,77,78,79,80,81,82],"Matteo Palieri","Benjamin Morrell","Abhishek Thakur","Kamak Ebadi","Jeremy Nash","Arghya Chatterjee","Christoforos Kanellakis","Luca Carlone","Cataldo Guaragnella","Ali-akbar Agha-mohammadi","IEEE Robotics and Automation Letters","journal","IEEE","6(2):421-428","10.1109\u002Flra.2020.3044864","2012.14447","https:\u002F\u002Fdoi.org\u002F10.1109\u002FLRA.2020.3044864","2020-12-14","metadata_verified","not_applicable",[11],false,"confirmed","arXiv","arXiv v1 (2020-12-28, marked 'Accepted for publication at RA-L'); IEEE version of record consulted through NTU access for metadata and the Table II image (values identical); correction notice abstract read",true,[100,108,112,117,121,125,130,134,140,143],{"category":101,"model":102,"canonical":103,"role":104,"dataset":105,"specs":106,"locator":107},"lidar","Velodyne VLP16","Velodyne VLP-16","method input","DARPA SubT Husky datasets (CoSTAR)","two units on Husky, one flat and one pitched forward 30 deg; scans recorded at 10 Hz; about 0.1 s per scan","Sec. III dataset description; footnote 1",{"category":101,"model":102,"canonical":103,"role":104,"dataset":109,"specs":110,"locator":111},null,"one unit on Spot","Sec. III-C1",{"category":113,"model":114,"canonical":114,"role":104,"dataset":105,"specs":115,"locator":116},"imu","Vector Nav 100","recorded at 50 Hz in the Urban datasets and 100 Hz in the Tunnel dataset","Sec. III dataset description",{"category":118,"model":119,"canonical":119,"role":104,"dataset":105,"specs":120,"locator":116},"wheel_or_leg_odometry","wheel-inertial odometry (WIO) of the Husky","recorded at 50 Hz in the Urban datasets",{"category":122,"model":123,"canonical":123,"role":104,"dataset":109,"specs":124,"locator":111},"other","Spot VIO and KIO from the Boston Dynamics API","VIO chosen for Spot because it was more accurate than KIO in the authors' tests",{"category":126,"model":127,"canonical":127,"role":104,"dataset":109,"specs":128,"locator":129},"platform","Clearpath Husky","skid-steer wheeled ground rover","Sec. III; Fig. 1",{"category":126,"model":131,"canonical":131,"role":104,"dataset":109,"specs":132,"locator":133},"Spot","legged robot","Sec. III-C1; Fig. 1",{"category":135,"model":136,"canonical":136,"role":137,"dataset":109,"specs":138,"locator":139},"compute","Intel Hades Canyon NUC8i7HVKVA","compute for runtime","4 x 1.9 GHz, 32 GB RAM, Ubuntu 18.04 LTS; used for the efficiency comparison","Sec. III-B3",{"category":135,"model":141,"canonical":141,"role":137,"dataset":109,"specs":142,"locator":111},"AMD RYZEN 9 3900X","12 cores, 3.8 GHz, onboard Husky",{"category":135,"model":144,"canonical":144,"role":137,"dataset":109,"specs":145,"locator":111},"Intel NUC7i7DN","4 cores, 1.9 GHz, onboard Spot",[],{"totalRows":148,"groupCount":149,"groups":150,"others":556},43,4,[151,423,474,530],{"slug":152,"group":153,"sourceId":5,"sourceLabel":6,"table":154,"selfRows":155,"metrics":156,"seqs":176,"entrants":185,"cells":206,"outcomes":413,"locators":417,"hardware":418,"wordings":420,"notes":421},"locus2021-table-ii","locus2021:Table II","Table II",28,[157,162,165,168,172,175],{"label":158,"unit":159,"statistic":160,"alignment":161},"APE max","m","max","not_reported",{"label":163,"unit":159,"statistic":164,"alignment":161},"APE mean","mean",{"label":166,"unit":159,"statistic":167,"alignment":161},"APE std","std",{"label":169,"unit":159,"statistic":170,"alignment":171},"ME (map error) RMSE","RMSE","SE3",{"label":173,"unit":174,"statistic":160,"alignment":161},"CPU load (number of cores)","cores",{"label":173,"unit":174,"statistic":164,"alignment":161},[177,180,182],{"dataset":105,"sequence":178,"environment":179},"Urban Alpha course","decommissioned power plant, Satsop (Elma, WA): long feature-poor corridors and large open spaces",{"dataset":105,"sequence":181,"environment":179},"Urban Beta course",{"dataset":105,"sequence":183,"environment":184},"Tunnel Safety Research course","Bruceton Research Mine, Pittsburgh: self-similar and self-repetitive tunnels",[186,187,189,191,194,197,200,203],{"name":7,"methodId":5,"linkable":98,"proposed":98,"self":98},{"name":188,"methodId":5,"linkable":98,"proposed":98,"self":98},"LOCUS FGA",{"name":190,"methodId":109,"linkable":94,"proposed":94,"self":94},"BLAM",{"name":192,"methodId":193,"linkable":98,"proposed":94,"self":94},"ALOAM","aloam_software",{"name":195,"methodId":196,"linkable":98,"proposed":94,"self":94},"FLOAM","floam2021",{"name":198,"methodId":199,"linkable":98,"proposed":94,"self":94},"Cartographer","cartographer2016",{"name":201,"methodId":202,"linkable":98,"proposed":94,"self":94},"LIO-Mapping","liomapping2019",{"name":204,"methodId":205,"linkable":98,"proposed":94,"self":94},"LIO-SAM","liosam2020",[207,211,214,217,220,222,224,226,228,230,232,234,236,237,240,241,243,245,247,249,251,253,255,256,257,258,259,260,261,263,265,267,269,271,273,275,277,279,281,283,285,287,289,291,293,295,297,299,301,303,305,307,309,311,313,315,317,319,321,323,325,327,329,331,333,335,337,339,341,343,345,347,349,351,353,355,357,359,361,363,365,367,369,371,372,375,376,377,379,380,382,384,386,388,390,392,393,395,397,399,400,401,402,403,404,405,406,408,410,411],[208,208,208,209,210,208,210,210,208],0,1.69,-1,[208,212,208,213,210,208,210,210,208],1,0.62,[208,215,208,216,210,208,210,210,208],2,0.57,[208,218,208,219,210,208,210,210,208],3,0.29,[208,208,212,221,210,208,210,210,208],1.51,[208,212,212,223,210,208,210,210,208],0.88,[208,215,212,225,210,208,210,210,208],0.51,[208,218,212,227,210,208,210,210,208],0.69,[208,208,215,229,210,208,210,210,208],3.39,[208,212,215,231,210,208,210,210,208],1.67,[208,215,215,233,210,208,210,210,208],0.76,[208,218,215,235,210,208,210,210,208],0.63,[208,149,212,229,210,208,208,210,208],[208,238,212,239,210,208,208,210,208],5,2.72,[212,208,208,235,210,208,210,210,208],[212,212,208,242,210,208,210,210,208],0.26,[212,215,208,244,210,208,210,210,208],0.18,[212,218,208,246,210,208,210,210,208],0.28,[212,208,212,248,210,208,210,210,208],1.2,[212,212,212,250,210,208,210,210,208],0.58,[212,215,212,252,210,208,210,210,208],0.39,[212,218,212,254,210,208,210,210,208],0.48,[212,208,215,109,208,208,210,210,208],[212,212,215,109,208,208,210,210,208],[212,215,215,109,208,208,210,210,208],[212,218,215,109,208,208,210,210,208],[212,149,212,229,210,208,208,210,208],[212,238,212,239,210,208,208,210,208],[215,208,208,262,210,208,210,210,208],3.44,[215,212,208,264,210,208,210,210,208],1.01,[215,215,208,266,210,208,210,210,208],0.94,[215,218,208,268,210,208,210,210,208],0.43,[215,208,212,270,210,208,210,210,208],3.89,[215,212,212,272,210,208,210,210,208],2.27,[215,215,212,274,210,208,210,210,208],0.89,[215,218,212,276,210,208,210,210,208],1.27,[215,208,215,278,210,208,210,210,208],171.34,[215,212,215,280,210,208,210,210,208],35.45,[215,215,215,282,210,208,210,210,208],51.91,[215,218,215,284,210,208,210,210,208],5.37,[215,149,212,286,210,208,208,210,208],1.14,[215,238,212,288,210,208,208,210,208],0.93,[218,208,208,290,210,208,210,210,208],4.33,[218,212,208,292,210,208,210,210,208],1.38,[218,215,208,294,210,208,210,210,208],1.19,[218,218,208,296,210,208,210,210,208],0.6,[218,208,212,298,210,208,210,210,208],2.58,[218,212,212,300,210,208,210,210,208],2.11,[218,215,212,302,210,208,210,210,208],0.44,[218,218,212,304,210,208,210,210,208],0.99,[218,208,215,306,210,208,210,210,208],18.61,[218,212,215,308,210,208,210,210,208],10.01,[218,215,215,310,210,208,210,210,208],6.01,[218,218,215,312,210,208,210,210,208],6.11,[218,149,212,314,210,208,208,210,208],1.65,[218,238,212,316,210,208,208,210,208],1.41,[149,208,208,318,210,208,210,210,208],29.49,[149,212,208,320,210,208,210,210,208],9.19,[149,215,208,322,210,208,210,210,208],8.96,[149,218,208,324,212,208,210,210,208],1.73,[149,208,212,326,210,208,210,210,208],40.64,[149,212,212,328,210,208,210,210,208],3.94,[149,215,212,330,210,208,210,210,208],8.42,[149,218,212,332,212,208,210,210,208],3.73,[149,208,215,334,210,208,210,210,208],85.31,[149,212,215,336,210,208,210,210,208],32.49,[149,215,215,338,210,208,210,210,208],25.73,[149,218,215,340,210,208,210,210,208],20.16,[149,149,212,342,210,208,208,210,208],1.76,[149,238,212,344,210,208,208,210,208],1.44,[238,208,208,346,210,208,210,210,208],5.84,[238,212,208,348,210,208,210,210,208],2.91,[238,215,208,350,210,208,210,210,208],1.6,[238,218,208,352,210,208,210,210,208],1.05,[238,208,212,354,210,208,210,210,208],2.64,[238,212,212,356,210,208,210,210,208],1.37,[238,215,212,358,210,208,210,210,208],0.67,[238,218,212,360,210,208,210,210,208],0.31,[238,208,215,362,210,208,210,210,208],50.05,[238,212,215,364,210,208,210,210,208],14.31,[238,215,215,366,210,208,210,210,208],13.45,[238,218,215,368,210,208,210,210,208],14.25,[238,149,212,370,210,208,208,210,208],1.75,[238,238,212,223,210,208,208,210,208],[373,208,208,374,210,208,210,210,208],6,2.12,[373,212,208,304,210,208,210,210,208],[373,215,208,225,210,208,210,210,208],[373,218,208,378,210,208,210,210,208],0.45,[373,208,212,350,210,208,210,210,208],[373,212,212,381,210,208,210,210,208],1.18,[373,215,212,383,210,208,210,210,208],0.22,[373,218,212,385,210,208,210,210,208],0.61,[373,208,215,387,210,208,210,210,208],3.31,[373,212,215,389,210,208,210,210,208],1.99,[373,215,215,391,210,208,210,210,208],0.55,[373,218,215,233,210,208,210,210,208],[373,149,212,394,210,208,208,210,208],1.8,[373,238,212,396,210,208,208,210,208],1.53,[398,208,208,109,215,208,210,210,208],7,[398,212,208,109,215,208,210,210,208],[398,215,208,109,215,208,210,210,208],[398,218,208,109,215,208,210,210,208],[398,208,212,109,215,208,210,210,208],[398,212,212,109,215,208,210,210,208],[398,215,212,109,215,208,210,210,208],[398,218,212,109,215,208,210,210,208],[398,208,215,407,210,208,210,210,208],2.45,[398,212,215,409,210,208,210,210,208],1.26,[398,215,215,250,210,208,210,210,208],[398,218,215,412,210,208,210,210,208],0.52,[414,415,416],"not_run (FGA variant not reported on the Tunnel dataset)","value marked * in Table II: failure leads to a low map error","failed (authors could not get LIO-SAM working on the Urban datasets, likely because the 50 Hz IMU rate is below the recommended 200 Hz)",[154],[419],"Intel Hades Canyon NUC8i7HVKVA (4 x 1.9 GHz, 32 GB RAM, Ubuntu 18.04)",[],[422],"Husky field datasets from the SubT Urban (Alpha, Beta courses at the Satsop power plant) and Tunnel (Safety Research course, Bruceton mine) circuits; APE via evo against a reference from LOCUS scan matching on the DARPA ground-truth map; ME = RMSE of cloud-to-cloud error after ICP alignment of the map to the DARPA ground-truth map; loop closures disabled; FLOAM and LIO-Mapping ran with one LiDAR in Urban Alpha, LIO-SAM with one LiDAR; CPU loads from Urban Beta (LIO-SAM from Tunnel)",{"slug":424,"group":425,"sourceId":5,"sourceLabel":6,"table":426,"selfRows":427,"metrics":428,"seqs":432,"entrants":445,"cells":450,"outcomes":466,"locators":467,"hardware":468,"wordings":471,"notes":472},"locus2021-table-v","locus2021:Table V","Table V",10,[429],{"label":430,"unit":431,"statistic":164,"alignment":161},"number of dropped LiDAR scans per second","scans\u002Fs",[433,437,439,441,443],{"dataset":434,"sequence":435,"environment":436},"DARPA SubT Urban Circuit competition runs","Alpha 1","Satsop power plant, live autonomous exploration",{"dataset":434,"sequence":438,"environment":436},"Alpha 2",{"dataset":434,"sequence":440,"environment":436},"Beta 1",{"dataset":434,"sequence":442,"environment":436},"Beta 2",{"dataset":434,"sequence":444,"environment":436},"Average",[446,448],{"name":447,"methodId":5,"linkable":98,"proposed":98,"self":98},"LOCUS (Husky, live)",{"name":449,"methodId":5,"linkable":98,"proposed":98,"self":98},"LOCUS (Spot, live)",[451,452,453,454,455,456,458,460,462,464],[208,208,208,208,210,208,208,210,208],[208,208,212,208,210,208,208,210,208],[208,208,215,208,210,208,208,210,208],[208,208,218,208,210,208,208,210,208],[208,208,149,208,210,208,208,210,208],[212,208,208,457,210,208,212,210,208],2.082,[212,208,212,459,210,208,212,210,208],2.205,[212,208,215,461,210,208,212,210,208],1.833,[212,208,218,463,210,208,212,210,208],2.016,[212,208,149,465,210,208,212,210,208],2.034,[],[426],[469,470],"AMD Ryzen 9 3900X, 12 cores, 3.8 GHz","Intel NUC7i7DN, 4 cores, 1.9 GHz",[],[473],"Average number of LiDAR scans dropped per second during the four live competition runs (10 Hz input, no buffering, so a drop means processing exceeded 0.1 s); Husky integrated WIO with 2 LiDARs, Spot integrated VIO with 1 LiDAR (Table IV settings)",{"slug":475,"group":476,"sourceId":5,"sourceLabel":6,"table":477,"selfRows":218,"metrics":478,"seqs":482,"entrants":490,"cells":498,"outcomes":520,"locators":525,"hardware":526,"wordings":527,"notes":528},"locus2021-table-iii","locus2021:Table III","Table III",[479],{"label":480,"unit":481,"statistic":161,"alignment":161},"robustness test result (categorical)","category",[483,486,488],{"dataset":105,"sequence":484,"environment":485},"WIO\u002FIMU failure","Urban courses with artificially injected sensor failure",{"dataset":105,"sequence":487,"environment":485},"WIO failure",{"dataset":105,"sequence":489,"environment":485},"LiDAR drop (10 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