[{"data":1,"prerenderedAt":381},["ShallowReactive",2],{"method-fflins2023":3},{"method":4,"reference":60,"equipment":85,"figures":124,"results":125},{"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":23,"limitations":29,"sensors":34,"platform":37,"estimator":40,"association":41,"timeModel":42,"deskew":43,"loopClosure":44,"globalOptimization":45,"mapRepresentation":46,"prior":47,"outputGeometry":48,"compute":49,"codeUrl":50,"codeLicense":51,"relatedVersions":52},"fflins2023","Tang et al., 2023","FF-LINS","FF-LINS: A Consistent Frame-to-Frame Solid-State-LiDAR-Inertial State Estimator",2023,"recent","C05","odometry_with_local_mapping","FF-LINS 認為把掃描配準到自建全域地圖（frame-to-map）會讓 LiDAR 慣性估計器把原本不可觀的全域偏航與位置錯誤地當成可觀，造成不一致。它採以 INS 為中心的架構：先以 INS 機械編排的高頻位姿去畸變並選取關鍵影格，再把兩關鍵影格間所有非重複掃描的影格累積成較稠密的關鍵影格點雲地圖；最新關鍵影格的點直接與滑動視窗內其他關鍵影格地圖做點到平面關聯，形成相對位姿約束，與 IMU 預積分一起在因子圖中最佳化，並線上估計 LiDAR 與 IMU 外參及時間延遲。","Consistent solid-state LiDAR-inertial estimator that replaces frame-to-map registration with direct frame-to-frame point-to-plane factors between the newest keyframe and INS-accumulated keyframe point-cloud maps in a sliding-window factor graph, with online LiDAR-IMU extrinsic and time-delay calibration.","full_text_reviewed","peer_reviewed_published","supplementary","論文未涉及營建場域；自建資料是約 1.5 m\u002Fs 低速輪式機器人在校園、建物周邊、操場與公園的序列，參考為 GNSS-RTK 加導航級 IMU 後處理軌跡（0.02 m、0.01 deg）。PA-LVIO [palvio2026] 在 i2Nav-Robot 與 MARS-LVIG 資料上以 FF-LINS 為比較基準。其一致性估計與線上外參及時間延遲校正，對需要再融合 GNSS 或 BIM 等絕對資訊的工地機器人有參考價值（推論）。",[20,21,22],"public_benchmark","independent_reference","cross_site",[24,25,26,27,28],"On the Robot dataset FF-LINS had lower ARE and ATE than FAST-LIO2 on all four sequences, e.g. campus 0.41 deg and 1.51 m versus 3.55 deg and 4.42 m (Table III)","Yaw standard deviation grows over time as expected for an unobservable state, whereas FAST-LIO2's does not, which the authors present as evidence of consistency (Fig. 1)","Online extrinsic and time-delay calibration converged and strongly improved accuracy: R3LIVE end-to-end errors 1.20, 2.41 and 2.51 m with calibration versus 12.18, 14.16 and 17.14 m without (Table II, Fig. 7)","Succeeded in narrow indoor passages of hku_main_building where LIO-SAM failed (Sec. IV-B-2)","Runs 4 to 6 times faster than real time on a desktop CPU (Sec. IV-D)",[30,31,32,33],"FAST-LIO2 obtained a much lower end-to-end error on hkust_campus_01 (0.14 m versus 2.51 m), which the authors attribute to FAST-LIO2 matching its own earlier map (Table II)","LiLi-OM dataset evaluation uses meter-level GPS start and end points, which the authors call inaccurate and only qualitative (Sec. IV-B-1)","Measurement covariance was set offline from error statistics with ground-truth poses (sigma about 0.1 m) (Sec. III-C-2)","Odometry only without loop closure; designed around non-repetitive solid-state LiDARs, although authors claim applicability to others (Sec. V)",[35,36],"solid-state non-repetitive LiDAR (Livox Mid-70 in the Robot dataset; Livox Horizon and Livox AVIA in public datasets)","MEMS IMU (ADI ADIS16465 in the Robot dataset; built-in Livox IMUs in public datasets)",[38,39],"wheeled UGV (low-speed robot, about 1.5 m\u002Fs)","not_reported (LiLi-OM and R3LIVE public datasets; carrier not described in this paper)","sliding-window factor graph (10 IMU preintegration intervals) solved with Levenberg-Marquardt in Ceres, tightly coupling LiDAR frame-to-frame point-to-plane factors with IMU preintegration and marginalization prior; LiDAR-IMU extrinsics and time delay estimated online; INS-centric update only at LiDAR keyframes (Sec. II, III-C)","direct frame-to-frame: each point of the newest keyframe is projected into the accumulated keyframe point-cloud maps of the other keyframes in the window; plane fitted to 5 nearest points and accepted if all lie within 0.1 m; Huber loss plus chi-square rejection between two optimizations (Sec. III-B, III-C-4)","discrete keyframe states with IMU preintegration and an estimated LiDAR-IMU time delay (Sec. III-C-1)","interpolated INS poses from mechanization undistort each frame before 0.5 m voxel downsampling (Sec. III-A-2)","none (authors state it could be added for large-scale mapping, Sec. V)","none","per-keyframe point-cloud maps accumulated from all frames since the previous keyframe with INS poses, voxel-downsampled at 0.5 m; no global map is used for state estimation (Sec. III-A-3)","INS initialization with zero position and yaw, roll and pitch from accelerometers; extrinsics and time delay assumed uncalibrated and estimated online (Sec. II, IV-A)","continuous INS-rate poses between keyframes, keyframe states, online LiDAR-IMU extrinsics and time delay; keyframe point clouds","real time on a desktop AMD R7-3700X: about 0.6 ms preprocessing per frame, 2.6 to 2.7 ms frame-to-frame association and 32.6 to 45.6 ms factor-graph optimization per keyframe (keyframes every 300 to 370 ms); runs at 4 to 6 times real-time speed (Table IV, Sec. IV-D)","https:\u002F\u002Fgithub.com\u002Fi2Nav-WHU\u002FFF-LINS","GPL-3.0 (LICENSE file checked)",[53,57],{"relation":54,"title":55,"doi_or_url":56},"preprint","arXiv 2307.06632 v1 (2023-07-13)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.06632",{"relation":58,"title":59,"doi_or_url":50},"code_release","i2Nav-WHU\u002FFF-LINS (code and Robot dataset)",{"id":5,"kind":61,"shortName":7,"title":8,"authors":62,"year":9,"venue":69,"venueType":70,"publisher":71,"volumeIssuePages":72,"doi":73,"arxivId":74,"url":75,"firstPublicDate":76,"publicationStatus":16,"metadataStatus":77,"fulltextStatus":15,"era":10,"classicReason":78,"codeUrl":50,"cluster":11,"topics":79,"mdpi":80,"verification":81,"label":6,"fulltextRoute":82,"versionRead":83,"addedByCensus":84},"method",[63,64,65,66,67,68],"Hailiang Tang","Tisheng Zhang","Xiaoji Niu","Liqiang Wang","Linfu Wei","Jingnan Liu","IEEE Robotics and Automation Letters","journal","IEEE","8(12):8525-8532","10.1109\u002Flra.2023.3329625","2307.06632","https:\u002F\u002Fdoi.org\u002F10.1109\u002FLRA.2023.3329625","2023-07-13","metadata_verified","not_applicable",[11],false,"corrected","arXiv","arXiv v1 (2023-07-13; only version); RA-L version of record not read",true,[86,94,99,104,108,113,117],{"category":87,"model":88,"canonical":89,"role":90,"dataset":91,"specs":92,"locator":93},"lidar","Livox Mid-70","Livox MID70","method input","FF-LINS Robot dataset","solid-state LiDAR, 10 Hz; described as containing only one scanning line","Sec. IV-A; Fig. 5",{"category":95,"model":96,"canonical":96,"role":90,"dataset":91,"specs":97,"locator":98},"imu","ADI ADIS16465","industrial-grade MEMS IMU, gyroscope bias instability 2 deg\u002Fhr, 200 Hz; hardware-triggered synchronization with the LiDAR","Sec. IV-A",{"category":100,"model":101,"canonical":101,"role":102,"dataset":91,"specs":103,"locator":98},"gnss","GNSS\u002FINS integrated navigation system with GNSS-RTK and a navigation-grade IMU (models not stated)","reference or ground truth","post-processed ground truth, 0.02 m position and 0.01 deg attitude",{"category":105,"model":106,"canonical":106,"role":90,"dataset":91,"specs":107,"locator":93},"platform","low-speed wheeled robot","average speed around 1.5 m\u002Fs",{"category":87,"model":109,"canonical":109,"role":110,"dataset":111,"specs":112,"locator":98},"Livox Horizon","dataset sensor","LiLi-OM dataset","with built-in IMU",{"category":87,"model":114,"canonical":115,"role":110,"dataset":116,"specs":112,"locator":98},"Livox AVIA","Livox Avia","R3LIVE dataset",{"category":118,"model":119,"canonical":119,"role":120,"dataset":121,"specs":122,"locator":123},"compute","AMD R7-3700X","compute for runtime",null,"desktop PC, ROS, multi-threaded","Sec. IV-A; Sec. IV-D",[],{"totalRows":126,"groupCount":127,"groups":128,"others":380},37,4,[129,218,273,327],{"slug":130,"group":131,"sourceId":5,"sourceLabel":6,"table":132,"selfRows":133,"metrics":134,"seqs":142,"entrants":152,"cells":159,"outcomes":212,"locators":213,"hardware":214,"wordings":215,"notes":216},"fflins2023-table-iii","fflins2023:Table III","Table III",16,[135,139],{"label":136,"unit":137,"statistic":138,"alignment":138},"ATE","m","not_reported",{"label":140,"unit":141,"statistic":138,"alignment":138},"ARE (absolute rotation error)","deg",[143,146,148,150],{"dataset":91,"sequence":144,"environment":145},"campus (1.33 km, 934 s)","campus, building surroundings, playground and park with pedestrians, bicycles and vehicles; wheeled robot",{"dataset":91,"sequence":147,"environment":145},"building (2.56 km, 1825 s)",{"dataset":91,"sequence":149,"environment":145},"playground (1.33 km, 969 s)",{"dataset":91,"sequence":151,"environment":145},"park (1.46 km, 1326 s)",[153,156,158],{"name":154,"methodId":155,"linkable":84,"proposed":80,"self":80},"FAST-LIO2","fastlio2_2022",{"name":157,"methodId":5,"linkable":84,"proposed":80,"self":84},"FF-LINS-WO (without online calibration)",{"name":7,"methodId":5,"linkable":84,"proposed":84,"self":84},[160,164,167,170,172,174,176,178,180,182,184,186,188,190,192,194,196,198,200,202,204,206,208,210],[161,161,161,162,163,161,163,163,161],0,4.42,-1,[161,161,165,166,163,161,163,163,161],1,3.12,[161,161,168,169,163,161,163,163,161],2,1.59,[161,161,171,127,163,161,163,163,161],3,[165,161,161,173,163,161,163,163,161],2.17,[165,161,165,175,163,161,163,163,161],2.24,[165,161,168,177,163,161,163,163,161],1.79,[165,161,171,179,163,161,163,163,161],2.08,[168,161,161,181,163,161,163,163,161],1.51,[168,161,165,183,163,161,163,163,161],1.9,[168,161,168,185,163,161,163,163,161],1.27,[168,161,171,187,163,161,163,163,161],1.44,[161,165,161,189,163,161,163,163,161],3.55,[161,165,165,191,163,161,163,163,161],3.13,[161,165,168,193,163,161,163,163,161],2.84,[161,165,171,195,163,161,163,163,161],3.24,[165,165,161,197,163,161,163,163,161],2.45,[165,165,165,199,163,161,163,163,161],2.23,[165,165,168,201,163,161,163,163,161],2.55,[165,165,171,203,163,161,163,163,161],2.4,[168,165,161,205,163,161,163,163,161],0.41,[168,165,165,207,163,161,163,163,161],0.65,[168,165,168,209,163,161,163,163,161],0.77,[168,165,171,211,163,161,163,163,161],0.9,[],[132],[],[],[217],"Private Robot dataset (Livox Mid-70, ADIS16465) with post-processed GNSS-RTK\u002FINS ground truth; ARE and ATE; LiLi-OM and LIO-SAM could not be run",{"slug":219,"group":220,"sourceId":5,"sourceLabel":6,"table":221,"selfRows":222,"metrics":223,"seqs":232,"entrants":242,"cells":244,"outcomes":266,"locators":267,"hardware":268,"wordings":270,"notes":271},"fflins2023-table-iv","fflins2023:Table IV","Table IV",12,[224,228,230],{"label":225,"unit":226,"statistic":227,"alignment":78},"Keyframe interval","ms","mean",{"label":229,"unit":226,"statistic":227,"alignment":78},"Frame-to-frame association",{"label":231,"unit":226,"statistic":227,"alignment":78},"Factor graph optimization",[233,236,238,240],{"dataset":91,"sequence":234,"environment":235},"campus","per keyframe",{"dataset":91,"sequence":237,"environment":235},"building",{"dataset":91,"sequence":239,"environment":235},"playground",{"dataset":91,"sequence":241,"environment":235},"park",[243],{"name":7,"methodId":5,"linkable":84,"proposed":84,"self":84},[245,247,248,250,252,254,256,257,258,260,262,264],[161,161,161,246,163,161,161,163,161],300,[161,161,165,246,163,161,161,163,161],[161,161,168,249,163,161,161,163,161],310,[161,161,171,251,163,161,161,163,161],370,[161,165,161,253,163,161,161,163,161],2.7,[161,165,165,255,163,161,161,163,161],2.6,[161,165,168,253,163,161,161,163,161],[161,165,171,253,163,161,161,163,161],[161,168,161,259,163,161,161,163,161],38.5,[161,168,165,261,163,161,161,163,161],37.5,[161,168,168,263,163,161,161,163,161],45.6,[161,168,171,265,163,161,161,163,161],32.6,[],[221],[269],"AMD R7-3700X desktop",[],[272],"Average running times of FF-LINS on the Robot dataset; frame preprocessing about 0.6 ms per frame (text)",{"slug":274,"group":275,"sourceId":5,"sourceLabel":6,"table":276,"selfRows":277,"metrics":278,"seqs":281,"entrants":289,"cells":296,"outcomes":320,"locators":322,"hardware":323,"wordings":324,"notes":325},"fflins2023-table-ii","fflins2023:Table II","Table II",6,[279],{"label":280,"unit":137,"statistic":138,"alignment":45},"end-to-end error",[282,285,287],{"dataset":116,"sequence":283,"environment":284},"hku_main_building","HKU main building narrow indoor passages and HKUST campus",{"dataset":116,"sequence":286,"environment":284},"hkust_campus_00",{"dataset":116,"sequence":288,"environment":284},"hkust_campus_01",[290,293,294,295],{"name":291,"methodId":292,"linkable":84,"proposed":80,"self":80},"LIO-SAM","liosam2020",{"name":154,"methodId":155,"linkable":84,"proposed":80,"self":80},{"name":157,"methodId":5,"linkable":84,"proposed":80,"self":84},{"name":7,"methodId":5,"linkable":84,"proposed":84,"self":84},[297,298,300,302,304,306,308,310,312,314,316,318],[161,161,161,121,161,161,163,163,161],[161,161,165,299,163,161,163,163,161],3.29,[161,161,168,301,163,161,163,163,161],20.82,[165,161,161,303,163,161,163,163,161],2.5,[165,161,165,305,163,161,163,163,161],3.69,[165,161,168,307,163,161,163,163,161],0.14,[168,161,161,309,163,161,163,163,161],12.18,[168,161,165,311,163,161,163,163,161],14.16,[168,161,168,313,163,161,163,163,161],17.14,[171,161,161,315,163,161,163,163,161],1.2,[171,161,165,317,163,161,163,163,161],2.41,[171,161,168,319,163,161,163,163,161],2.51,[321],"failed",[276],[],[],[326],"R3LIVE dataset (Livox AVIA) end-to-end errors; LiLi-OM could not be run; FF-LINS-WO disables online extrinsic and time-delay calibration",{"slug":328,"group":329,"sourceId":5,"sourceLabel":6,"table":330,"selfRows":171,"metrics":331,"seqs":334,"entrants":342,"cells":350,"outcomes":374,"locators":375,"hardware":376,"wordings":377,"notes":378},"fflins2023-table-i","fflins2023:Table I","Table I",[332],{"label":333,"unit":137,"statistic":138,"alignment":45},"distance error of starting-ending distance versus GPS",[335,338,340],{"dataset":111,"sequence":336,"environment":337},"Schloss-1","outdoor sequences (Schloss and East)",{"dataset":111,"sequence":339,"environment":337},"Schloss-2",{"dataset":111,"sequence":341,"environment":337},"East",[343,346,347,349],{"name":344,"methodId":345,"linkable":84,"proposed":80,"self":80},"LiLi-OM","liliom2021",{"name":291,"methodId":292,"linkable":84,"proposed":80,"self":80},{"name":348,"methodId":155,"linkable":84,"proposed":80,"self":80},"FAST_LIO2",{"name":7,"methodId":5,"linkable":84,"proposed":84,"self":84},[351,353,354,356,358,360,362,364,366,368,370,372],[161,161,161,352,163,161,163,163,161],1.36,[161,161,165,185,163,161,163,163,161],[161,161,168,355,163,161,163,163,161],15.43,[165,161,161,357,163,161,163,163,161],0.47,[165,161,165,359,163,161,163,163,161],0.36,[165,161,168,361,163,161,163,163,161],25.16,[168,161,161,363,163,161,163,163,161],1.1,[168,161,165,365,163,161,163,163,161],6.59,[168,161,168,367,163,161,163,163,161],8.3,[171,161,161,369,163,161,163,163,161],0.23,[171,161,165,371,163,161,163,163,161],1.14,[171,161,168,373,163,161,163,163,161],2.81,[],[330],[],[],[379],"LiLi-OM dataset without ground truth: error of the estimated starting-ending distance relative to meter-level GPS start and end fixes; LiLi-OM and LIO-SAM without loop closure; extrinsics and time delay treated as uncalibrated",[],1790510655500]