[{"data":1,"prerenderedAt":473},["ShallowReactive",2],{"method-licfusion2019":3},{"method":4,"reference":51,"equipment":73,"figures":103,"results":104},{"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":21,"limitations":24,"sensors":26,"platform":31,"estimator":34,"association":35,"timeModel":36,"deskew":37,"loopClosure":38,"globalOptimization":39,"mapRepresentation":40,"prior":41,"outputGeometry":42,"compute":43,"codeUrl":44,"codeLicense":45,"relatedVersions":46},"licfusion2019","Zuo et al., 2019","LIC-Fusion","LIC-Fusion: LiDAR-Inertial-Camera Odometry",2019,"recent","C07","odometry","LIC-Fusion 在多狀態約束卡爾曼濾波器（MSCKF）架構中，緊密融合 IMU、稀疏視覺特徵，以及從光達掃描中擷取並追蹤的邊緣與平面特徵點。其特色是線上估計三種非同步感測器之間的空間外參與時間偏移，以因應低成本裝置的延遲與時鐘偏差。系統為純里程計，不維護全域地圖，也不使用迴圈。","An MSCKF-based LiDAR-inertial-camera odometry that fuses tracked LiDAR edge\u002Fplane features and sparse visual features with online spatio-temporal calibration of all sensors.","full_text_reviewed","peer_reviewed_published","background","not_reported",[20],"independent_reference",[22,23],"Online spatial and temporal calibration among IMU, camera and LiDAR (abstract","Sec. II) | Lower average ATE than MSCKF VIO and LOAM on an 800 m outdoor sequence with RTK GPS reference (Table I) | Only method that stayed usable in the violently shaken Indoor-C sequence (1.55 vs 49.94 for MSCKF and 2.44 for LOAM) (Table II)",[25],"No global map and no loop closure (Sec. III) | Evaluation limited to one outdoor sequence and indoor start-end drift (Sec. III) | LOAM had lower start-end error on Indoor-A (0.66 vs 0.98) and Indoor-B (0.46 vs 1.04) (Table II) | No runtime figures reported",[27,28,29,30],"3D LiDAR (Velodyne VLP-16)","IMU (Xsens MTi-300)","monochrome global-shutter camera","GNSS RTK (reference only)",[32,33],"wheeled UGV","handheld","MSCKF whose state holds the IMU state, camera-IMU and LiDAR-IMU extrinsics with time offsets (IMU clock as reference), and sliding windows of IMU clones at camera and LiDAR times; standard EKF update after measurement compression","LiDAR edge (high curvature) and surf (low curvature) points from scan rings tracked from the current scan to the previous scan by KD-tree nearest neighbours, giving point-to-line and point-to-plane distances with propagated covariance and chi-square Mahalanobis gating; FAST visual features tracked by KLT, triangulated from camera clones and used after MSCKF nullspace projection; all residuals compressed by Givens-rotation thin QR","discrete cloned poses; camera and LiDAR time offsets relative to the IMU clock estimated online","Not described: the full text contains no LiDAR motion-distortion compensation step; edge and surf features are taken from raw scan rings","none (purely odometry, no global map; Sec. III)","none","none (sliding window of cloned states; no global map maintained)","offline extrinsic calibration refined online","trajectory only (no map product reported)","not_reported in the full text; the method is described as single-thread",null,"not_verified",[47],{"relation":48,"title":49,"doi_or_url":50},"preprint","LIC-Fusion arXiv (v1 2019-09-09, v2 2019-11-01)","https:\u002F\u002Farxiv.org\u002Fabs\u002F1909.04102",{"id":5,"kind":52,"shortName":7,"title":8,"authors":53,"year":9,"venue":59,"venueType":60,"publisher":61,"volumeIssuePages":62,"doi":63,"arxivId":64,"url":50,"firstPublicDate":65,"publicationStatus":16,"metadataStatus":66,"fulltextStatus":15,"era":10,"classicReason":67,"codeUrl":44,"cluster":11,"topics":68,"mdpi":69,"verification":70,"label":6,"fulltextRoute":71,"versionRead":72,"addedByCensus":69},"method",[54,55,56,57,58],"Xingxing Zuo","Patrick Geneva","Woosik Lee","Yong Liu","Guoquan Huang","2019 IEEE\u002FRSJ International Conference on Intelligent Robots and Systems (IROS)","conference","IEEE","pp. 5848-5854","10.1109\u002Firos40897.2019.8967746","1909.04102","2019-09-09","metadata_verified","not_applicable",[11],false,"confirmed","arXiv","arXiv 1909.04102v2 (2019-11-01, accepted IROS preprint, 6 pages); IROS 2019 version of record not compared",[74,81,84,87,94,98],{"category":75,"model":76,"canonical":77,"role":78,"dataset":79,"specs":18,"locator":80},"imu","Xsens MTi-300 AHRS IMU","Xsens MTi-300","method input","self-collected indoor and outdoor sequences","Sec. III; Fig. 2",{"category":82,"model":83,"canonical":83,"role":78,"dataset":79,"specs":18,"locator":80},"lidar","Velodyne VLP-16",{"category":85,"model":86,"canonical":86,"role":78,"dataset":79,"specs":29,"locator":80},"camera","Blackfly BFLY-PGE-23S6M",{"category":88,"model":89,"canonical":89,"role":90,"dataset":91,"specs":92,"locator":93},"gnss","RTK GPS","reference or ground truth","self-collected outdoor sequence","centimeter-level accuracy, used as outdoor ground truth","Sec. III-A",{"category":95,"model":96,"canonical":96,"role":78,"dataset":91,"specs":97,"locator":93},"platform","custom Ackermann robot platform","outdoor carrier of the sensor rig",{"category":95,"model":99,"canonical":99,"role":78,"dataset":100,"specs":101,"locator":102},"handheld sensor rig (held at chest height)","self-collected indoor sequences","indoor carrying mode","Sec. III-B",[],{"totalRows":105,"groupCount":106,"groups":107,"others":472},27,4,[108,325,380,435],{"slug":109,"group":110,"sourceId":111,"sourceLabel":112,"table":113,"selfRows":114,"metrics":115,"seqs":122,"entrants":139,"cells":154,"outcomes":318,"locators":320,"hardware":321,"wordings":322,"notes":323},"licfusion2-2020-table-vi","licfusion2_2020:Table VI","licfusion2_2020","Zuo et al., 2020","Table VI",14,[116,119],{"label":117,"unit":118,"statistic":18,"alignment":18},"averaged ATE, orientation (deg)","deg",{"label":120,"unit":121,"statistic":18,"alignment":18},"averaged ATE, position (m)","m",[123,127,129,131,133,135,137],{"dataset":124,"sequence":125,"environment":126},"Vicon Room sequences (authors' data)","Seq 1 (42.62 m)","indoor Vicon motion-capture room (cluttered)",{"dataset":124,"sequence":128,"environment":126},"Seq 2 (84.16 m)",{"dataset":124,"sequence":130,"environment":126},"Seq 3 (33.92 m)",{"dataset":124,"sequence":132,"environment":126},"Seq 4 (53.14 m)",{"dataset":124,"sequence":134,"environment":126},"Seq 5 (49.74 m)",{"dataset":124,"sequence":136,"environment":126},"Seq 6 (87.87 m)",{"dataset":124,"sequence":138,"environment":126},"Average of Seq 1-6",[140,143,145,147,150,153],{"name":141,"methodId":111,"linkable":142,"proposed":142,"self":69},"LIC-Fusion 2.0",true,{"name":144,"methodId":44,"linkable":69,"proposed":69,"self":69},"OpenVINS-IC",{"name":146,"methodId":44,"linkable":69,"proposed":69,"self":69},"Proposed-LI",{"name":148,"methodId":149,"linkable":142,"proposed":69,"self":69},"LOAM","loam2014",{"name":151,"methodId":152,"linkable":142,"proposed":69,"self":69},"LIO-MAP","liomapping2019",{"name":7,"methodId":5,"linkable":142,"proposed":69,"self":142},[155,159,162,164,166,169,171,174,176,178,180,183,185,188,189,191,193,195,197,199,201,203,205,207,209,211,212,214,216,218,220,222,224,226,228,230,232,234,236,238,240,242,244,246,248,250,252,254,256,258,260,262,264,266,268,270,272,273,274,276,278,279,280,281,282,284,286,288,290,292,294,296,297,299,301,303,304,305,306,308,310,312,314,316],[156,156,156,157,158,156,158,158,156],0,2.537,-1,[156,160,156,161,158,156,158,158,156],1,0.097,[156,156,160,163,158,156,158,158,156],1.87,[156,160,160,165,158,156,158,158,156],0.145,[156,156,167,168,158,156,158,158,156],2,1.94,[156,160,167,170,158,156,158,158,156],0.101,[156,156,172,173,158,156,158,158,156],3,2.081,[156,160,172,175,158,156,158,158,156],0.116,[156,156,106,177,158,156,158,158,156],2.71,[156,160,106,179,158,156,158,158,156],0.104,[156,156,181,182,158,156,158,158,156],5,3.32,[156,160,181,184,158,156,158,158,156],0.113,[156,156,186,187,158,156,158,158,156],6,2.41,[156,160,186,184,158,156,158,158,156],[160,156,156,190,158,156,158,158,156],2.625,[160,160,156,192,158,156,158,158,156],0.094,[160,156,160,194,158,156,158,158,156],1.741,[160,160,160,196,158,156,158,158,156],0.177,[160,156,167,198,158,156,158,158,156],3.131,[160,160,167,200,158,156,158,158,156],0.273,[160,156,172,202,158,156,158,158,156],2.404,[160,160,172,204,158,156,158,158,156],0.115,[160,156,106,206,158,156,158,158,156],2.962,[160,160,106,208,158,156,158,158,156],0.129,[160,156,181,210,158,156,158,158,156],3.953,[160,160,181,208,158,156,158,158,156],[160,156,186,213,158,156,158,158,156],2.803,[160,160,186,215,158,156,158,158,156],0.153,[167,156,156,217,158,156,158,158,156],2.333,[167,160,156,219,158,156,158,158,156],0.199,[167,156,160,221,158,156,158,158,156],3.325,[167,160,160,223,158,156,158,158,156],0.444,[167,156,167,225,158,156,158,158,156],2.81,[167,160,167,227,158,156,158,158,156],0.306,[167,156,172,229,158,156,158,158,156],5.335,[167,160,172,231,158,156,158,158,156],0.272,[167,156,106,233,158,156,158,158,156],3.332,[167,160,106,235,158,156,158,158,156],0.44,[167,156,181,237,158,156,158,158,156],4.866,[167,160,181,239,158,156,158,158,156],0.412,[167,156,186,241,158,156,158,158,156],3.667,[167,160,186,243,158,156,158,158,156],0.345,[172,156,156,245,158,156,158,158,156],5.88,[172,160,156,247,158,156,158,158,156],0.156,[172,156,160,249,158,156,158,158,156],6.414,[172,160,160,251,158,156,158,158,156],0.134,[172,156,167,253,158,156,158,158,156],15.384,[172,160,167,255,158,156,158,158,156],0.333,[172,156,172,257,158,156,158,158,156],6.354,[172,160,172,259,158,156,158,158,156],0.15,[172,156,106,261,158,156,158,158,156],5.542,[172,160,106,263,158,156,158,158,156],0.14,[172,156,181,265,158,156,158,158,156],7.095,[172,160,181,267,158,156,158,158,156],0.188,[172,156,186,269,158,156,158,158,156],7.778,[172,160,186,271,158,156,158,158,156],0.183,[106,156,156,44,156,156,158,158,156],[106,160,156,44,156,156,158,158,156],[106,156,160,275,158,156,158,158,156],5.608,[106,160,160,277,158,156,158,158,156],0.214,[106,156,167,44,156,156,158,158,156],[106,160,167,44,156,156,158,158,156],[106,156,172,44,156,156,158,158,156],[106,160,172,44,156,156,158,158,156],[106,156,106,283,158,156,158,158,156],4.89,[106,160,106,285,158,156,158,158,156],0.17,[106,156,181,287,158,156,158,158,156],12.862,[106,160,181,289,158,156,158,158,156],0.238,[106,156,186,291,158,156,158,158,156],7.786,[106,160,186,293,158,156,158,158,156],0.207,[181,156,156,295,158,156,158,158,156],2.345,[181,160,156,161,158,156,158,158,156],[181,156,160,298,158,156,158,158,156],1.879,[181,160,160,300,158,156,158,158,156],0.173,[181,156,167,302,158,156,158,158,156],1.973,[181,160,167,179,158,156,158,158,156],[181,156,172,44,156,156,158,158,156],[181,160,172,44,156,156,158,158,156],[181,156,106,307,158,156,158,158,156],2.743,[181,160,106,309,158,156,158,158,156],0.1,[181,156,181,311,158,156,158,158,156],3.788,[181,160,181,313,158,156,158,158,156],0.131,[181,156,186,315,158,156,158,158,156],2.546,[181,160,186,317,158,156,158,158,156],0.121,[319],"failed",[113],[],[],[324],"Averaged ATE of 5 runs on 6 Vicon-room sequences (cluttered room, Vicon ground truth), orientation (deg) and position (m); ATE computed following Zhang and Scaramuzza [23]; '-' = translational error above 20 m. The Average column is printed by the authors (for LIO-MAP and LIC-Fusion it averages only the sequences that did not fail).",{"slug":326,"group":327,"sourceId":111,"sourceLabel":112,"table":328,"selfRows":329,"metrics":330,"seqs":346,"entrants":363,"cells":365,"outcomes":373,"locators":375,"hardware":376,"wordings":377,"notes":378},"licfusion2-2020-table-v","licfusion2_2020:Table V","Table V",7,[331,334,336,338,340,342,344],{"label":332,"unit":121,"statistic":333,"alignment":39},"averaged start-to-end drift error vector as printed: (-0.740, 0.0401, 0.222) m","mean",{"label":335,"unit":121,"statistic":333,"alignment":39},"averaged start-to-end drift error vector as printed: (0.293, 0.984, -0.656) m",{"label":337,"unit":121,"statistic":333,"alignment":39},"averaged start-to-end drift error vector as printed: (1.216, 1.831, -0.465) m",{"label":339,"unit":121,"statistic":333,"alignment":39},"averaged start-to-end drift error vector as printed: (-1.117, 0.607, 0.529) m",{"label":341,"unit":121,"statistic":333,"alignment":39},"averaged start-to-end drift error vector as printed: (-0.382, -2.248, -0.905) m",{"label":343,"unit":121,"statistic":333,"alignment":39},"averaged start-to-end drift error vector as printed: (-3.295, -1.934, 0.585) m",{"label":345,"unit":121,"statistic":333,"alignment":39},"averaged start-to-end drift error vector as printed: (-0.912, -0.847, 0.377) m",[347,351,353,355,357,359,361],{"dataset":348,"sequence":349,"environment":350},"Teaching Building sequences (authors' data)","Seq 1 (about 108 m)","indoor teaching building (completed building), corridors and stairs",{"dataset":348,"sequence":352,"environment":350},"Seq 2 (about 124 m)",{"dataset":348,"sequence":354,"environment":350},"Seq 3 (about 237 m)",{"dataset":348,"sequence":356,"environment":350},"Seq 4 (about 195 m)",{"dataset":348,"sequence":358,"environment":350},"Seq 5 (about 85 m)",{"dataset":348,"sequence":360,"environment":350},"Seq 6 (about 140 m)",{"dataset":348,"sequence":362,"environment":350},"Seq 7 (about 83 m)",[364],{"name":7,"methodId":5,"linkable":142,"proposed":69,"self":142},[366,367,368,369,370,371,372],[156,156,156,44,156,156,158,158,156],[156,160,160,44,156,156,158,158,156],[156,167,167,44,156,156,158,158,156],[156,172,172,44,156,156,158,158,156],[156,106,106,44,156,156,158,158,156],[156,181,181,44,156,156,158,158,156],[156,186,186,44,156,156,158,158,156],[374],"other: three-component vector printed, no scalar reported",[328],[],[],[379],"Averaged start-to-end drift of 5 runs on 7 teaching-building sequences (Zhejiang University) that start and end at the same position; each cell printed as a three-component vector in metres without axis labels; '-' = severe failure with final drift norm larger than 30 m. No scalar is printed, so value is null and the printed vector is kept in metric_as_written.",{"slug":381,"group":382,"sourceId":5,"sourceLabel":6,"table":383,"selfRows":106,"metrics":384,"seqs":388,"entrants":399,"cells":405,"outcomes":429,"locators":430,"hardware":431,"wordings":432,"notes":433},"licfusion2019-table-ii","licfusion2019:Table II","Table II",[385],{"label":386,"unit":387,"statistic":333,"alignment":39},"average trajectory start-end error","m (unit implied, not printed)",[389,392,394,397],{"dataset":100,"sequence":390,"environment":391},"Indoor-A (39m)","indoor, handheld",{"dataset":100,"sequence":393,"environment":391},"Indoor-B (86m)",{"dataset":100,"sequence":395,"environment":396},"Indoor-C (55m)","indoor, handheld, violent shaking",{"dataset":100,"sequence":398,"environment":391},"Indoor-D (189m)",[400,403,404],{"name":401,"methodId":402,"linkable":142,"proposed":69,"self":69},"MSCKF","mourikis2007msckf",{"name":7,"methodId":5,"linkable":142,"proposed":142,"self":142},{"name":148,"methodId":149,"linkable":142,"proposed":69,"self":69},[406,408,410,412,414,416,418,420,421,423,425,427],[156,156,156,407,158,156,158,158,156],0.99,[160,156,156,409,158,156,158,158,156],0.98,[167,156,156,411,158,156,158,158,156],0.66,[156,156,160,413,158,156,158,158,156],1.55,[160,156,160,415,158,156,158,158,156],1.04,[167,156,160,417,158,156,158,158,156],0.46,[156,156,167,419,158,156,158,158,156],49.94,[160,156,167,413,158,156,158,158,156],[167,156,167,422,158,156,158,158,156],2.44,[156,156,172,424,158,156,158,158,156],46.03,[160,156,172,426,158,156,158,158,156],3.68,[167,156,172,428,158,156,158,158,156],5.99,[],[383],[],[],[434],"Indoor handheld sequences at chest height, normal to low light, slow to aggressive motion; no ground truth; average start-end error after returning to the start; unit not printed in the table (sequence lengths given in m)",{"slug":436,"group":437,"sourceId":5,"sourceLabel":6,"table":438,"selfRows":167,"metrics":439,"seqs":445,"entrants":449,"cells":453,"outcomes":466,"locators":467,"hardware":468,"wordings":469,"notes":470},"licfusion2019-table-i","licfusion2019:Table I","Table I",[440,442],{"label":441,"unit":121,"statistic":333,"alignment":18},"average of average absolute trajectory errors (ATE)",{"label":443,"unit":121,"statistic":444,"alignment":18},"1 sigma of the ATE","std",[446],{"dataset":91,"sequence":447,"environment":448},"outdoor (~800 m)","outdoor, sensor rig on a custom Ackermann robot platform (about 800 m, 4 min)",[450,451,452],{"name":401,"methodId":402,"linkable":142,"proposed":69,"self":69},{"name":7,"methodId":5,"linkable":142,"proposed":142,"self":142},{"name":148,"methodId":149,"linkable":142,"proposed":69,"self":69},[454,456,458,460,462,464],[156,156,156,455,158,156,158,158,156],10.75,[156,160,156,457,158,156,158,158,156],3.56,[160,156,156,459,158,156,158,158,156],4.06,[160,160,156,461,158,156,158,158,156],3.42,[167,156,156,463,158,156,158,158,156],23.08,[167,160,156,465,158,156,158,158,156],2.63,[],[438],[],[],[471],"Outdoor ~800 m, 4 min sequence on an Ackermann robot with RTK GPS ground truth (centimeter level); average over 6 runs of the average ATE and its 1-sigma; alignment for the ATE not stated (Fig. 4 MSE uses a best-fit transform); LOAM output benefits from implicit loop closure through its global map",[],1790510658474]