[{"data":1,"prerenderedAt":761},["ShallowReactive",2],{"method-fastlio2021":3},{"method":4,"reference":54,"equipment":74,"figures":111,"results":112},{"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":30,"platform":33,"estimator":36,"association":37,"timeModel":38,"deskew":39,"loopClosure":40,"globalOptimization":40,"mapRepresentation":41,"prior":40,"outputGeometry":42,"compute":43,"codeUrl":44,"codeLicense":45,"relatedVersions":46},"fastlio2021","Xu & Zhang, 2021","FAST-LIO","FAST-LIO: A Fast, Robust LiDAR-Inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter",2021,"recent","C05","odometry_with_local_mapping","FAST-LIO 以緊耦合迭代擴展卡爾曼濾波（iterated extended Kalman filter, iEKF）融合 LiDAR 特徵點與 IMU，並以 IMU 前向傳播與反向傳播（back-propagation）將掃描內每個點補償到掃描結束時刻，以處理運動畸變。作者提出與傳統等價、但計算量取決於狀態維度而非量測維度的卡爾曼增益公式，使大量特徵點可在機載電腦上即時融合。其前端仍沿用 LOAM 式邊緣與平面特徵，地圖為特徵點集合，無迴圈偵測。","A tightly-coupled iEKF LiDAR-inertial odometry with IMU-based back-propagation deskewing and a Kalman-gain formula whose cost scales with state dimension, enabling real-time fusion of many feature points on a small UAV computer.","full_text_reviewed","peer_reviewed_published","background","作者以手持方式繞行香港大學主樓（既有建築外部）約 140 m 後回到起點，以起訖點差估計漂移；未於施工中工地或以獨立參考量測評估點雲幾何。",[20,21],"controlled_experiment","completed_building",[23,24,25,26],"Kalman gain computed with complexity tied to state dimension, lowering cost when many LiDAR points are fused (abstract; Sec. III-C)","Stable odometry under hand-held shaking with angular rates often above 100 deg\u002Fs where compared LOAM variants degraded (Sec. IV-C)","New Kalman gain formula took 1.16 ms versus 1621 ms for the conventional formula at 1802 feature points (Table II)","On the LINS seaport data FAST-LIO used 7.3 ms per scan versus 34.5 ms for LINS while keeping 784 instead of 147 feature points, with better mapping accuracy shown qualitatively (Sec. IV-D; Fig. 7)",[28,29],"Front end depends on hand-engineered edge\u002Fplane feature extraction (Sec. III-A); the successor FAST-LIO2 motivates removing it because feature extraction depends on LiDAR scan pattern (FAST-LIO2 Sec. I, VIII)","No loop closure or global optimization; drift is reported only as start-end return error (Sec. IV) (inference: global consistency of maps not addressed)",[31,32],"3D LiDAR (solid-state Livox Avia; Velodyne VLP-16 in LINS data)","IMU (model on the authors' rig not reported; Xsens MTiG-710 in LINS data)",[34,35],"UAV","handheld","tightly-coupled iterated extended Kalman filter on manifold, with an equivalent Kalman-gain formula whose matrix inversion scales with state dimension rather than measurement dimension","LOAM-style edge and planar feature points; point-to-edge \u002F point-to-plane residuals to nearest map features found with a k-d tree","discrete poses (scan-end state) with per-point back-propagation","IMU forward propagation plus backward propagation that projects each feature point to the scan-end time","none","accumulated feature-point map (edge and plane points) organized by k-d tree","odometry and registered feature-point map; raw-point maps shown in figures; export format not_reported","real-time CPU on DJI Manifold 2-C (Intel i7-8550U); all iEKF iterations within 25 ms with >1,200 effective features (abstract)","https:\u002F\u002Fgithub.com\u002Fhku-mars\u002FFAST_LIO","GPL-2.0 (LICENSE file checked; same repository now also hosts FAST-LIO2)",[47,51],{"relation":48,"title":49,"doi_or_url":50},"preprint","FAST-LIO: A Fast, Robust LiDAR-inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter (arXiv v3)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.08196",{"relation":52,"title":53,"doi_or_url":44},"code_release","hku-mars\u002FFAST_LIO",{"id":5,"kind":55,"shortName":7,"title":8,"authors":56,"year":9,"venue":59,"venueType":60,"publisher":61,"volumeIssuePages":62,"doi":63,"arxivId":64,"url":65,"firstPublicDate":66,"publicationStatus":16,"metadataStatus":67,"fulltextStatus":15,"era":10,"classicReason":68,"codeUrl":44,"cluster":11,"topics":69,"mdpi":70,"verification":71,"label":6,"fulltextRoute":72,"versionRead":73,"addedByCensus":70},"method",[57,58],"Wei Xu","Fu Zhang","IEEE Robotics and Automation Letters","journal","IEEE","6(2):3317-3324","10.1109\u002Flra.2021.3064227","2010.08196","https:\u002F\u002Fapi.crossref.org\u002Fworks\u002F10.1109\u002FLRA.2021.3064227","2020-10-16","metadata_verified","not_applicable",[11],false,"confirmed","arXiv","arXiv v3 (2021-04-14); RA-L 6(2):3317-3324 version of record not read",[75,83,88,92,96,103,108],{"category":76,"model":77,"canonical":78,"role":79,"dataset":80,"specs":81,"locator":82},"lidar","Livox AVIA","Livox Avia","method input",null,"solid-state, 70 deg FoV, installed directly on the airframe","Fig. 1; Sec. IV-B",{"category":84,"model":85,"canonical":85,"role":79,"dataset":80,"specs":86,"locator":87},"imu","IMU (model not reported)","rigidly attached to the LiDAR with known extrinsic","Sec. III-B-2",{"category":89,"model":90,"canonical":90,"role":79,"dataset":80,"specs":91,"locator":82},"platform","customized small-scale quadrotor UAV","280 mm wheelbase",{"category":89,"model":93,"canonical":93,"role":79,"dataset":80,"specs":94,"locator":95},"handheld sensor suite (same LiDAR platform)","not_reported","Sec. IV-C; Sec. IV-D; Fig. 6",{"category":97,"model":98,"canonical":99,"role":100,"dataset":80,"specs":101,"locator":102},"compute","DJI Manifold 2-C","DJI Manifold 2C","compute for runtime","1.8 GHz quad-core Intel i7-8550U CPU, 8 GB RAM","Fig. 1; Sec. IV-B; Sec. IV-D",{"category":76,"model":104,"canonical":104,"role":105,"dataset":106,"specs":94,"locator":107},"Velodyne VLP-16","dataset sensor","LINS dataset (seaport)","Sec. IV-D; Fig. 7",{"category":84,"model":109,"canonical":110,"role":105,"dataset":106,"specs":94,"locator":107},"Xsens MTiG-710","Xsens MTi-G-710",[],{"totalRows":113,"groupCount":114,"groups":115,"others":712},88,12,[116,358,595,651],{"slug":117,"group":118,"sourceId":119,"sourceLabel":120,"table":121,"selfRows":122,"metrics":123,"seqs":140,"entrants":159,"cells":167,"outcomes":350,"locators":352,"hardware":353,"wordings":355,"notes":356},"locus2-2022-table-iii","locus2_2022:Table III","locus2_2022","Reinke et al., 2022","Table III",30,[124,128,132,135,137],{"label":125,"unit":126,"statistic":127,"alignment":94},"APE max [m]","m","max",{"label":129,"unit":130,"statistic":131,"alignment":94},"APE mean [%] (unit as printed)","% (as printed)","mean",{"label":133,"unit":134,"statistic":127,"alignment":68},"CPU [%] max (as printed)","% (100% = one core)",{"label":136,"unit":134,"statistic":131,"alignment":68},"CPU [%] mean (as printed)",{"label":138,"unit":139,"statistic":127,"alignment":68},"max memory [GB]","GB",[141,145,148,151,154,157],{"dataset":142,"sequence":143,"environment":144},"NeBula odometry dataset (DARPA SubT, Team CoSTAR)","A: power plant, Elma WA (urban), Husky, 631.53 m","feature-poor corridors, large open spaces",{"dataset":142,"sequence":146,"environment":147},"C: power plant, Elma WA (urban), Husky, 757.40 m","feature-poor corridors, large and narrow spaces",{"dataset":142,"sequence":149,"environment":150},"F: Bruceton Mine, Pittsburgh PA (tunnel), Husky, 1569.73 m","self-similar repetitive geometry",{"dataset":142,"sequence":152,"environment":153},"H: Subway Station, Los Angeles CA (urban), Spot, 1777.45 m","3-level, multiple stairs, feature-poor corridors",{"dataset":142,"sequence":155,"environment":156},"I: Kentucky Underground Limestone Mine KY (cave), Spot, 768.82 m","large area, degraded lighting",{"dataset":142,"sequence":158,"environment":156},"J: Kentucky Underground Limestone Mine KY (cave), Husky, 2339.81 m",[160,163,164],{"name":161,"methodId":119,"linkable":162,"proposed":162,"self":70},"LOCUS 2.0",true,{"name":7,"methodId":5,"linkable":162,"proposed":70,"self":162},{"name":165,"methodId":166,"linkable":162,"proposed":70,"self":70},"LINS","lins2020",[168,172,175,178,181,184,186,188,190,192,194,196,198,200,202,204,206,208,210,212,214,216,218,220,222,224,225,227,229,231,233,235,237,239,241,243,245,247,249,251,253,255,257,259,261,262,264,266,268,270,272,274,276,278,280,282,284,286,288,290,292,294,296,298,300,302,303,305,307,309,311,313,315,317,319,320,322,324,326,328,330,332,334,336,338,340,342,344,346,348],[169,169,169,170,171,169,171,171,169],0,0.19,-1,[169,173,169,174,171,169,171,171,169],1,0.09,[169,176,169,177,171,169,169,171,169],2,102.38,[169,179,169,180,171,169,169,171,169],3,185.5,[169,182,169,183,171,169,169,171,169],4,1.06,[173,169,169,185,171,169,171,171,169],0.79,[173,173,169,187,171,169,171,171,169],0.3,[173,176,169,189,171,169,169,171,169],89.11,[173,179,169,191,171,169,169,171,169],126.4,[173,182,169,193,171,169,169,171,169],0.36,[176,169,169,195,171,169,171,171,169],0.43,[176,173,169,197,171,169,171,171,169],0.18,[176,176,169,199,171,169,169,171,169],40.84,[176,179,169,201,171,169,169,171,169],81.5,[176,182,169,203,171,169,169,171,169],0.42,[169,169,173,205,171,169,171,171,169],0.16,[169,173,173,207,171,169,171,171,169],0.24,[169,176,173,209,171,169,169,171,169],114.79,[169,179,173,211,171,169,169,171,169],198,[169,182,173,213,171,169,169,171,169],1.3,[173,169,173,215,171,169,171,171,169],2.21,[173,173,173,217,171,169,171,171,169],4.22,[173,176,173,219,171,169,169,171,169],76.46,[173,179,173,221,171,169,169,171,169],307.2,[173,182,173,223,171,169,169,171,169],0.99,[176,169,173,195,171,169,171,171,169],[176,173,173,226,171,169,171,171,169],0.6,[176,176,173,228,171,169,169,171,169],38.43,[176,179,173,230,171,169,169,171,169],75.3,[176,182,173,232,171,169,169,171,169],0.47,[169,169,176,234,171,169,171,171,169],0.67,[169,173,176,236,171,169,171,171,169],0.45,[169,176,176,238,171,169,169,171,169],119,[169,179,176,240,171,169,169,171,169],229.2,[169,182,176,242,171,169,169,171,169],1.98,[173,169,176,244,169,169,171,171,169],48555.33,[173,173,176,246,169,169,171,171,169],9268.71,[173,176,176,248,171,169,169,171,169],156.73,[173,179,176,250,171,169,169,171,169],401.3,[173,182,176,252,171,169,169,171,169],11.31,[176,169,176,254,169,169,171,171,169],52.73,[176,173,176,256,169,169,171,171,169],23.35,[176,176,176,258,171,169,169,171,169],28.1,[176,179,176,260,171,169,169,171,169],52.3,[176,182,176,232,171,169,169,171,169],[169,169,179,263,171,169,171,171,169],0.57,[169,173,179,265,171,169,171,171,169],0.23,[169,176,179,267,171,169,169,171,169],61.05,[169,179,179,269,171,169,169,171,169],169.9,[169,182,179,271,171,169,169,171,169],2.42,[173,169,179,273,171,169,171,171,169],5.92,[173,173,179,275,171,169,171,171,169],5.69,[173,176,179,277,171,169,169,171,169],75.15,[173,179,179,279,171,169,169,171,169],160.8,[173,182,179,281,171,169,169,171,169],0.62,[176,169,179,283,171,169,171,171,169],12.11,[176,173,179,285,171,169,171,171,169],8.05,[176,176,179,287,171,169,169,171,169],39.19,[176,179,179,289,171,169,169,171,169],97.9,[176,182,179,291,171,169,169,171,169],0.61,[169,169,182,293,171,169,171,171,169],1.39,[169,173,182,295,171,169,171,171,169],1.95,[169,176,182,297,171,169,169,171,169],72.11,[169,179,182,299,171,169,169,171,169],141.6,[169,182,182,301,171,169,169,171,169],1.01,[173,169,182,223,171,169,171,171,169],[173,173,182,304,171,169,171,171,169],1.44,[173,176,182,306,171,169,169,171,169],117.87,[173,179,182,308,171,169,169,171,169],167.8,[173,182,182,310,171,169,169,171,169],0.8,[176,169,182,312,171,169,171,171,169],0.86,[176,173,182,314,171,169,171,171,169],0.85,[176,176,182,316,171,169,169,171,169],75.9,[176,179,182,318,171,169,169,171,169],101.4,[176,182,182,314,171,169,169,171,169],[169,169,321,271,171,169,171,171,169],5,[169,173,321,323,171,169,171,171,169],3.88,[169,176,321,325,171,169,169,171,169],107.72,[169,179,321,327,171,169,169,171,169],185,[169,182,321,329,171,169,169,171,169],2.13,[173,169,321,331,171,169,171,171,169],1.72,[173,173,321,333,171,169,171,171,169],2.6,[173,176,321,335,171,169,169,171,169],126.72,[173,179,321,337,171,169,169,171,169],332.5,[173,182,321,339,171,169,169,171,169],2.54,[176,169,321,341,171,169,171,171,169],3.56,[176,173,321,343,171,169,171,171,169],5.79,[176,176,321,345,171,169,169,171,169],73.76,[176,179,321,347,171,169,169,171,169],176.5,[176,182,321,349,171,169,169,171,169],1.85,[351],"failed (authors state only LOCUS 2.0 does not fail in tunnel dataset F)",[121],[354],"not_reported (the computer used for Table III is not stated in the paper)",[],[357],"Underground datasets A, C, F, H, I, J (Table I); LOCUS 2.0 versus FAST-LIO and LINS; column labels reproduced as printed (APE max [m], APE mean [%], CPU [%] max and mean, max memory [GB]); many printed 'max' values are below 'mean' values; ground truth from LOCUS 1.0 against survey-grade maps",{"slug":359,"group":360,"sourceId":361,"sourceLabel":362,"table":363,"selfRows":364,"metrics":365,"seqs":392,"entrants":419,"cells":429,"outcomes":588,"locators":589,"hardware":591,"wordings":592,"notes":593},"r2live2021-table-i","r2live2021:Table I","r2live2021","Lin et al., 2021","Table I",24,[366,369,372,374,376,378,380,382,384,386,388,390],{"label":367,"unit":368,"statistic":94,"alignment":94},"RRE (deg) over 50 m sub-sequences","deg",{"label":370,"unit":371,"statistic":94,"alignment":94},"RTE (%) over 50 m sub-sequences","%",{"label":373,"unit":368,"statistic":94,"alignment":94},"RRE (deg) over 100 m sub-sequences",{"label":375,"unit":371,"statistic":94,"alignment":94},"RTE (%) over 100 m sub-sequences",{"label":377,"unit":368,"statistic":94,"alignment":94},"RRE (deg) over 150 m sub-sequences",{"label":379,"unit":371,"statistic":94,"alignment":94},"RTE (%) over 150 m sub-sequences",{"label":381,"unit":368,"statistic":94,"alignment":94},"RRE (deg) over 200 m sub-sequences",{"label":383,"unit":371,"statistic":94,"alignment":94},"RTE (%) over 200 m sub-sequences",{"label":385,"unit":368,"statistic":94,"alignment":94},"RRE (deg) over 250 m sub-sequences",{"label":387,"unit":371,"statistic":94,"alignment":94},"RTE (%) over 250 m sub-sequences",{"label":389,"unit":368,"statistic":94,"alignment":94},"RRE (deg) over 300 m sub-sequences",{"label":391,"unit":371,"statistic":94,"alignment":94},"RTE (%) over 300 m sub-sequences",[393,397,399,401,403,405,407,409,411,413,415,417],{"dataset":394,"sequence":395,"environment":396},"R2LIVE Experiment-4 (authors' data, D-GPS RTK)","Experiment-4 (a), 50 m sub-sequences","outdoor handheld, fast rotation",{"dataset":394,"sequence":398,"environment":396},"Experiment-4 (a), 100 m sub-sequences",{"dataset":394,"sequence":400,"environment":396},"Experiment-4 (a), 150 m sub-sequences",{"dataset":394,"sequence":402,"environment":396},"Experiment-4 (a), 200 m sub-sequences",{"dataset":394,"sequence":404,"environment":396},"Experiment-4 (a), 250 m sub-sequences",{"dataset":394,"sequence":406,"environment":396},"Experiment-4 (a), 300 m sub-sequences",{"dataset":394,"sequence":408,"environment":396},"Experiment-4 (b), 50 m sub-sequences",{"dataset":394,"sequence":410,"environment":396},"Experiment-4 (b), 100 m sub-sequences",{"dataset":394,"sequence":412,"environment":396},"Experiment-4 (b), 150 m 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Table I",[],[],[594],"Version of record Table I: relative rotation error (RRE, deg) and relative translation error (RTE, %) over all sub-sequences of each length, two fast-rotating handheld sequences (130 and 200 deg\u002Fs, mapping to (a) and (b) not stated) with D-GPS RTK ground truth; rows R2LIVE-LIO, R2LIVE-VIO and R2LIVE-LC (the latter undefined in the text) omitted here. Supersedes the median values in the arXiv v1 Fig. 11 caption.",{"slug":596,"group":597,"sourceId":5,"sourceLabel":6,"table":598,"selfRows":114,"metrics":599,"seqs":603,"entrants":617,"cells":622,"outcomes":645,"locators":646,"hardware":647,"wordings":648,"notes":649},"fastlio2021-table-ii","fastlio2021:Table II","Table II",[600],{"label":601,"unit":602,"statistic":94,"alignment":68},"Kalman gain computation time","ms",[604,607,609,611,613,615],{"dataset":605,"sequence":606,"environment":94},"not_reported (own data)","feature number = 307",{"dataset":605,"sequence":608,"environment":94},"feature number = 717",{"dataset":605,"sequence":610,"environment":94},"feature number = 998",{"dataset":605,"sequence":612,"environment":94},"feature number = 1243",{"dataset":605,"sequence":614,"environment":94},"feature number = 1453",{"dataset":605,"sequence":616,"environment":94},"feature number = 1802",[618,620],{"name":619,"methodId":5,"linkable":162,"proposed":70,"self":162},"Old Formula (conventional Kalman gain)",{"name":621,"methodId":5,"linkable":162,"proposed":162,"self":162},"New Formula (FAST-LIO)",[623,625,627,629,631,633,634,636,637,639,641,643],[169,169,169,624,171,169,171,171,169],7.1,[173,169,169,626,171,169,171,171,169],0.07,[169,169,173,628,171,169,171,171,169],23.4,[173,169,173,630,171,169,171,171,169],0.11,[169,169,176,632,171,169,171,171,169],109.3,[173,169,176,449,171,169,171,171,169],[169,169,179,635,171,169,171,171,169],251,[173,169,179,551,171,169,171,171,169],[169,169,182,638,171,169,171,171,169],1219,[173,169,182,640,171,169,171,171,169],0.59,[169,169,321,642,171,169,171,171,169],1621,[173,169,321,644,171,169,171,171,169],1.16,[],[598],[],[],[650],"Running time of the Kalman gain computation with the conventional versus the proposed formula, same pipeline and number of feature points",{"slug":652,"group":653,"sourceId":654,"sourceLabel":655,"table":363,"selfRows":442,"metrics":656,"seqs":659,"entrants":677,"cells":682,"outcomes":706,"locators":707,"hardware":708,"wordings":709,"notes":710},"he2023ikfom-table-i","he2023ikfom:Table I","he2023ikfom","He et al., 2023b",[657],{"label":658,"unit":371,"statistic":94,"alignment":40},"odometry drift (%)",[660,664,666,669,672,675],{"dataset":661,"sequence":662,"environment":663},"own datasets (trial 01 by the authors; trial 02 from the FAST-LIO paper)","V1-01","indoor UAV flight",{"dataset":661,"sequence":665,"environment":663},"V1-02",{"dataset":661,"sequence":667,"environment":668},"V2-01","indoor quick-shake with the UAV held by hand",{"dataset":661,"sequence":670,"environment":671},"V2-02","indoor quick-shake (trial 02 from the FAST-LIO paper; holding mode not stated in the TIE text)",{"dataset":661,"sequence":673,"environment":674},"V3-01","outdoor random walk",{"dataset":661,"sequence":676,"environment":674},"V3-02",[678,680],{"name":679,"methodId":654,"linkable":162,"proposed":162,"self":70},"IKFoM-based",{"name":681,"methodId":5,"linkable":162,"proposed":70,"self":162},"Hand-derived [14] (FAST-LIO)",[683,685,687,689,691,693,695,697,699,701,702,704],[169,169,169,684,171,169,171,171,169],0.414,[173,169,169,686,171,169,171,171,169],0.527,[169,169,173,688,171,169,171,171,169],0.071,[173,169,173,690,171,169,171,171,169],0.328,[169,169,176,692,171,169,171,171,169],0.079,[173,169,176,694,171,169,171,171,169],0.015,[169,169,179,696,171,169,171,171,169],0.496,[173,169,179,698,171,169,171,171,169],0.558,[169,169,182,700,171,169,171,171,169],0.063,[173,169,182,579,171,169,171,171,169],[169,169,321,703,171,169,171,171,169],0.055,[173,169,321,705,171,169,171,171,169],0.076,[],[363],[],[],[711],"Odometry drift (%) of the FAST-LIO based LiDAR-inertial system implemented with IKFoM (with online extrinsics) versus the hand-derived IESEKF of FAST-LIO, from start and end pose coincidence, six datasets, at most 5 iterations",[713,717,724,730,736,743,748,754],{"group":714,"slug":715,"sourceLabel":655,"table":598,"selfRows":442,"datasets":716},"he2023ikfom:Table II","he2023ikfom-table-ii",[661],{"group":718,"slug":719,"sourceLabel":720,"table":721,"selfRows":176,"datasets":722},"cai2021ikdtree:Text Sec. 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