[{"data":1,"prerenderedAt":341},["ShallowReactive",2],{"method-glio2024":3},{"method":4,"reference":57,"equipment":79,"figures":104,"results":105},{"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":32,"platform":36,"estimator":38,"association":39,"timeModel":40,"deskew":41,"loopClosure":42,"globalOptimization":43,"mapRepresentation":44,"prior":45,"outputGeometry":46,"compute":47,"codeUrl":48,"codeLicense":49,"relatedVersions":50},"glio2024","Liu et al., 2024","GLIO","GLIO: Tightly-Coupled GNSS\u002FLiDAR\u002FIMU Integration for Continuous and Drift-Free State Estimation of Intelligent Vehicles in Urban Areas",2024,"recent","C07","full_slam_with_global_correction","GLIO 在因子圖中緊耦合 GNSS 原始量測、LiDAR 與 IMU：第一階段以滑動視窗融合基準站差分後的雙差虛擬距離、都卜勒、IMU 預積分與 LiDAR 掃描對地圖平面因子；第二階段在獨立執行緒上對關鍵影格做批次最佳化，每個關鍵影格與 12 個相鄰影格建立掃描對多掃描約束，並逐步排除離群量測。GNSS 提供全域約束消除漂移，LiDAR 與 IMU 則在高樓遮蔽、GNSS 品質差時維持連續估測。","Tightly coupled GNSS raw-measurement, LiDAR and IMU factor graph with a sliding-window first stage (DD pseudorange, Doppler, preintegration, scan-to-map factors) and a batch second stage with scan-to-multiscan LiDAR factors, giving continuous and drift-free vehicle state estimates in urban canyons.","full_text_reviewed","peer_reviewed_published","supplementary","GLIO 針對高樓林立、GNSS 訊號受遮蔽的都市峽谷，與城市施工現場周邊的 GNSS 條件相近（推論）。不過誤差為公尺級，適合提供全域參考或抑制長距離漂移，不足以單獨支撐公分級點雲量測；論文也沒有評估地圖精度。",[20,21],"public_benchmark","independent_reference",[23,24,25,26,27],"On UrbanNav TST, GLIO-DS 2D mean error 1.21 m and 3D mean 2.07 m versus 7.53 and 18.02 m for RTKLIB and 3.24 and 5.28 m for LIO-SAM with DGNSS (Table I)","On UrbanNav Whampoa (over 25 min and 4.5 km), GLIO-DS 3D mean 3.34 m versus 7.44 m for LIO-SAM with DGNSS and 28.36 m for LILI-OM (Table II)","The second stage reduces errors strongly compared with the single-stage variant (GLIO-SS) on both sequences (Tables I-II)","Real-time at 10 Hz (Sec. IV)","Author-reported improvement of more than 70 % over GNSS-only positioning and standalone LIO (Abstract)",[29,30,31],"Maximum error of nearly 10 m remains because unhealthy GNSS observations are not fully mitigated (Sec. IV)","Depends on reference-station corrections","Evaluated only on two vehicle sequences of one dataset",[33,34,35],"low-cost GNSS receiver raw pseudorange and Doppler (u-blox F9P) with reference-station corrections","IMU (Xsens Ti-10)","3D LiDAR (Velodyne HDL-32E)",[37],"vehicle (UrbanNav dataset, Hong Kong)","two-stage factor graph optimization in Ceres on a LILI-OM base: (1) sliding-window fusion of double-differenced pseudorange, Doppler, IMU preintegration, scan-to-map planar LiDAR factors and marginalization; (2) batch optimization over keyframes with scan-to-multiscan LiDAR factors and relative attitude constraints, run on a separate thread (max 50 iterations or 3 s) with outlier exclusion (Sec. III)","planar LiDAR features (100 per keyframe in the first stage; 25 random planar features per frame pair with 12 adjacent keyframes in the second stage); GNSS raw measurements double-differenced with a reference station; GNSS epochs associated to LiDAR keyframes by interpolation (Sec. III)","discrete LiDAR keyframes at 10 Hz; GNSS at 10 Hz and IMU at 100 Hz linked through preintegration and interpolation","not described; the LiDAR front end is adopted from LILI-OM","no explicit loop closure; global drift is removed by GNSS factors","second-stage batch factor graph over keyframes with GNSS and scan-to-multiscan LiDAR factors (Sec. III)","LiDAR keyframe feature map in the global (GNSS) frame","reference-station GNSS corrections; lever arm from prior calibration (Fig. 6 caption)","vehicle trajectory in a global frame and a LiDAR feature map","preprocessing under 40 ms, first stage about 30 ms, total under 100 ms per frame, real time at 10 Hz; computer not specified (Sec. IV)","https:\u002F\u002Fgithub.com\u002FXikunLiu-huskit\u002FGLIO","not specified (no license file detected by the GitHub API)",[51,54],{"relation":52,"title":53,"doi_or_url":48},"preprint","GLIO author manuscript (submitted version) in the GitHub repository",{"relation":55,"title":56,"doi_or_url":48},"code_release","XikunLiu-huskit\u002FGLIO",{"id":5,"kind":58,"shortName":7,"title":8,"authors":59,"year":9,"venue":63,"venueType":64,"publisher":65,"volumeIssuePages":66,"doi":67,"arxivId":68,"url":69,"firstPublicDate":70,"publicationStatus":16,"metadataStatus":71,"fulltextStatus":15,"era":10,"classicReason":72,"codeUrl":48,"cluster":11,"topics":73,"mdpi":74,"verification":75,"label":6,"fulltextRoute":76,"versionRead":77,"addedByCensus":78},"method",[60,61,62],"Xikun Liu","Weisong Wen","Li-Ta Hsu","IEEE Transactions on Intelligent Vehicles","journal","IEEE","9(1), pp. 1412-1422","10.1109\u002Ftiv.2023.3323648",null,"https:\u002F\u002Fdoi.org\u002F10.1109\u002FTIV.2023.3323648","2023-10-13","metadata_verified","not_applicable",[11],false,"corrected","author copy (code repository)","Author manuscript (submitted version, PDF in the code repository, created 2023-10-26) read in full; IEEE version of record checked for section structure and Tables I-II (identical values)",true,[80,87,91,96,100],{"category":81,"model":82,"canonical":82,"role":83,"dataset":84,"specs":85,"locator":86},"gnss","F9P","method input","UrbanNav (Hong Kong)","low-cost receiver; raw single-frequency GPS, BeiDou, Galileo and GLONASS at 10 Hz","Sec. IV",{"category":88,"model":89,"canonical":89,"role":83,"dataset":84,"specs":90,"locator":86},"imu","Ti-10","100 Hz",{"category":92,"model":93,"canonical":94,"role":83,"dataset":84,"specs":95,"locator":86},"lidar","HDL-32E","Velodyne HDL-32E","10 Hz",{"category":81,"model":97,"canonical":97,"role":98,"dataset":84,"specs":99,"locator":86},"SPAN-CPT","reference or ground truth","multi-frequency multi-constellation GNSS RTK with a tactical-grade IMU; post-processed ground truth",{"category":81,"model":101,"canonical":101,"role":83,"dataset":84,"specs":102,"locator":103},"GNSS reference station","corrections used to remove systematic errors from pseudoranges","Abstract; Sec. III",[],{"totalRows":106,"groupCount":107,"groups":108,"others":340},27,3,[109,222,308],{"slug":110,"group":111,"sourceId":5,"sourceLabel":6,"table":112,"selfRows":113,"metrics":114,"seqs":132,"entrants":136,"cells":149,"outcomes":215,"locators":216,"hardware":218,"wordings":219,"notes":220},"glio2024-table-i","glio2024:Table I","Table I",12,[115,120,123,126,128,130],{"label":116,"unit":117,"statistic":118,"alignment":119},"2D MEAN positioning error","m","mean","not_reported",{"label":121,"unit":117,"statistic":122,"alignment":119},"2D MAX positioning error","max",{"label":124,"unit":117,"statistic":125,"alignment":119},"2D STD positioning error","std",{"label":127,"unit":117,"statistic":118,"alignment":119},"3D MEAN positioning error",{"label":129,"unit":117,"statistic":122,"alignment":119},"3D MAX positioning error",{"label":131,"unit":117,"statistic":125,"alignment":119},"3D STD positioning error",[133],{"dataset":84,"sequence":134,"environment":135},"TST","urban canyon",[137,139,142,145,147],{"name":138,"methodId":68,"linkable":74,"proposed":74,"self":74},"RTKLIB (GNSS RTK)",{"name":140,"methodId":141,"linkable":78,"proposed":74,"self":74},"LIO (LILI-OM, aligned to the world frame by ground truth)","liliom2021",{"name":143,"methodId":144,"linkable":78,"proposed":74,"self":74},"LIO-GNSS (LIO-SAM with DGNSS from RTKLIB)","liosam2020",{"name":146,"methodId":5,"linkable":78,"proposed":78,"self":78},"GLIO-SS (single-stage only)",{"name":148,"methodId":5,"linkable":78,"proposed":78,"self":78},"GLIO-DS (full two-stage)",[150,154,157,160,162,165,168,170,172,174,176,178,180,182,184,186,188,190,192,194,195,197,199,201,203,205,207,209,211,213],[151,151,151,152,153,151,153,153,151],0,7.53,-1,[151,155,151,156,153,151,153,153,151],1,46.27,[151,158,151,159,153,151,153,153,151],2,7.76,[151,107,151,161,153,151,153,153,151],18.02,[151,163,151,164,153,151,153,153,151],4,93.69,[151,166,151,167,153,151,153,153,151],5,14.7,[155,151,151,169,153,151,153,153,151],2.21,[155,155,151,171,153,151,153,153,151],4.47,[155,158,151,173,153,151,153,153,151],1.19,[155,107,151,175,153,151,153,153,151],10.33,[155,163,151,177,153,151,153,153,151],30.15,[155,166,151,179,153,151,153,153,151],9.35,[158,151,151,181,153,151,153,153,151],3.24,[158,155,151,183,153,151,153,153,151],5.26,[158,158,151,185,153,151,153,153,151],0.61,[158,107,151,187,153,151,153,153,151],5.28,[158,163,151,189,153,151,153,153,151],19.01,[158,166,151,191,153,151,153,153,151],3.01,[107,151,151,193,153,151,153,153,151],1.53,[107,155,151,163,153,151,153,153,151],[107,158,151,196,153,151,153,153,151],1.11,[107,107,151,198,153,151,153,153,151],9.64,[107,163,151,200,153,151,153,153,151],28.38,[107,166,151,202,153,151,153,153,151],9.1,[163,151,151,204,153,151,153,153,151],1.21,[163,155,151,206,153,151,153,153,151],2.57,[163,158,151,208,153,151,153,153,151],0.39,[163,107,151,210,153,151,153,153,151],2.07,[163,163,151,212,153,151,153,153,151],4.69,[163,166,151,214,153,151,153,153,151],0.88,[],[217],"Table I; Sec. IV",[],[],[221],"UrbanNav dataset; positioning error against NovAtel SPAN-CPT RTK\u002FINS ground truth in metres; UrbanNav TST: starts under an overpass, dense tall office buildings, heavy traffic, narrow roads with limited sky view",{"slug":223,"group":224,"sourceId":5,"sourceLabel":6,"table":225,"selfRows":113,"metrics":226,"seqs":233,"entrants":236,"cells":242,"outcomes":301,"locators":302,"hardware":304,"wordings":305,"notes":306},"glio2024-table-ii","glio2024:Table II","Table II",[227,228,229,230,231,232],{"label":116,"unit":117,"statistic":118,"alignment":119},{"label":121,"unit":117,"statistic":122,"alignment":119},{"label":124,"unit":117,"statistic":125,"alignment":119},{"label":127,"unit":117,"statistic":118,"alignment":119},{"label":129,"unit":117,"statistic":122,"alignment":119},{"label":131,"unit":117,"statistic":125,"alignment":119},[234],{"dataset":84,"sequence":235,"environment":135},"Whampoa",[237,238,239,240,241],{"name":138,"methodId":68,"linkable":74,"proposed":74,"self":74},{"name":140,"methodId":141,"linkable":78,"proposed":74,"self":74},{"name":143,"methodId":144,"linkable":78,"proposed":74,"self":74},{"name":146,"methodId":5,"linkable":78,"proposed":78,"self":78},{"name":148,"methodId":5,"linkable":78,"proposed":78,"self":78},[243,245,247,249,251,253,255,257,259,261,263,265,267,269,271,273,275,277,279,281,283,285,287,289,290,292,294,296,298,300],[151,151,151,244,153,151,153,153,151],7.74,[151,155,151,246,153,151,153,153,151],49.37,[151,158,151,248,153,151,153,153,151],10.11,[151,107,151,250,153,151,153,153,151],9.9,[151,163,151,252,153,151,153,153,151],98.85,[151,166,151,254,153,151,153,153,151],15.67,[155,151,151,256,153,151,153,153,151],12.63,[155,155,151,258,153,151,153,153,151],45.51,[155,158,151,260,153,151,153,153,151],12.55,[155,107,151,262,153,151,153,153,151],28.36,[155,163,151,264,153,151,153,153,151],69.06,[155,166,151,266,153,151,153,153,151],16.94,[158,151,151,268,153,151,153,153,151],2.68,[158,155,151,270,153,151,153,153,151],5.73,[158,158,151,272,153,151,153,153,151],1.44,[158,107,151,274,153,151,153,153,151],7.44,[158,163,151,276,153,151,153,153,151],23.91,[158,166,151,278,153,151,153,153,151],5.84,[107,151,151,280,153,151,153,153,151],4.4,[107,155,151,282,153,151,153,153,151],15.27,[107,158,151,284,153,151,153,153,151],3.8,[107,107,151,286,153,151,153,153,151],14.72,[107,163,151,288,153,151,153,153,151],30.09,[107,166,151,159,153,151,153,153,151],[163,151,151,291,153,151,153,153,151],1.68,[163,155,151,293,153,151,153,153,151],4.17,[163,158,151,295,153,151,153,153,151],0.96,[163,107,151,297,153,151,153,153,151],3.34,[163,163,151,299,153,151,153,153,151],9.15,[163,166,151,169,153,151,153,153,151],[],[303],"Table II; Sec. IV",[],[],[307],"UrbanNav dataset; positioning error against NovAtel SPAN-CPT RTK\u002FINS ground truth in metres; UrbanNav Whampoa: over 25 min and 4.5 km from open sky into dense urban areas with overpasses, billboards and dynamic objects",{"slug":309,"group":310,"sourceId":5,"sourceLabel":6,"table":311,"selfRows":107,"metrics":312,"seqs":320,"entrants":322,"cells":324,"outcomes":331,"locators":335,"hardware":336,"wordings":337,"notes":338},"glio2024-sec-iv-timing","glio2024:Sec. IV timing","Sec. IV timing",[313,316,318],{"label":314,"unit":315,"statistic":119,"alignment":72},"preprocessing of GNSS, IMU and LiDAR features for both stages per frame","ms",{"label":317,"unit":315,"statistic":119,"alignment":72},"first stage fusion time per frame",{"label":319,"unit":315,"statistic":119,"alignment":72},"total time consumption per frame",[321],{"dataset":84,"sequence":119,"environment":135},[323],{"name":7,"methodId":5,"linkable":78,"proposed":78,"self":78},[325,327,329],[151,151,151,326,151,151,153,153,151],40,[151,155,151,328,155,151,153,153,151],30,[151,158,151,330,158,151,153,153,151],100,[332,333,334],"upper bound as printed ('less than 40ms')","approximate as printed ('around 30ms')","upper bound as printed ('less than 100ms')",[86],[],[],[339],"Timing stated in the text (computer not specified)",[],1790510657271]