[{"data":1,"prerenderedAt":264},["ShallowReactive",2],{"method-he2023ikfom":3},{"method":4,"reference":60,"equipment":81,"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":24,"limitations":29,"sensors":34,"platform":38,"estimator":42,"association":43,"timeModel":44,"deskew":45,"loopClosure":46,"globalOptimization":46,"mapRepresentation":47,"prior":46,"outputGeometry":48,"compute":49,"codeUrl":50,"codeLicense":51,"relatedVersions":52},"he2023ikfom","He et al., 2023b","IKFoM","Symbolic Representation and Toolkit Development of Iterated Error-State Extended Kalman Filters on Manifolds",2023,"recent","C03","estimation_framework_or_library","本文提出在流形上建構迭代誤差狀態擴展卡爾曼濾波（IESEKF）的通用符號化方法：以 ⊞、⊟ 與 ⊕ 運算把機器人系統寫成離散時間的流形標準形式，使濾波各步驟中的流形約束與系統特定部分分離，並證明其最小參數化在整個工作空間內沒有奇異點。作者據此開發 C++ 工具包 IKFoM，支援 R^n、SO(3)、SE_N(3) 與 S^2 等原始流形及其組合，使用者只需提供系統描述即可呼叫預測與更新。期刊版以兩個緊耦合 LiDAR 慣性系統驗證：重新實作 FAST-LIO 並加入線上 LiDAR 與 IMU 外參估計（六組資料的漂移與手推版本相當，執行時間略增），以及以 IKFoM 取代 LINS 的手推濾波器（在 LIO-SAM Campus 資料上執行時間較短）。","A generic symbolic formulation and C++ toolkit (IKFoM) for iterated error-state EKFs on compound manifolds that separates manifold constraints from system models; verified by re-implementing FAST-LIO with online LiDAR-IMU extrinsics and LINS, matching hand-derived drift with slightly higher (FAST-LIO) or lower (LINS) runtime.","full_text_reviewed","peer_reviewed_published","main_body","not_reported。TIE 版實驗為室內 UAV 飛行、辦公區手持快速晃動（起訖點附近有 Vicon）與戶外隨機行走，另以 LIO-SAM Campus 公開序列比較運算時間；皆為既有建物或校園環境，沒有營建工地，也未評估點雲幾何精度。",[20,21,22,23],"controlled_experiment","completed_building","independent_reference","public_benchmark",[25,26,27,28],"Minimal parameterization of the error state that is singularity-free in the whole workspace (Theorem 1, Sec. III-F)","Odometry drift of 0.055 to 0.496% over six datasets, comparable to 0.015 to 0.558% for the hand-derived FAST-LIO filter (Table I)","Online LiDAR-IMU extrinsic estimates agree across six datasets, with translation uncertainty 1 to 5 cm and rotation uncertainty below 3.5 deg (Sec. IV-C2; Fig. 7)","The IKFoM re-implementation of LINS runs faster than the original hand-derived filter (Table III)",[30,31,32,33],"(inference) Filter without loop closure or global optimization; long-term drift is not addressed by the estimator itself","Drift is judged from start and end pose coincidence, with Vicon ground truth only near the start and end of V2-01 (Sec. IV-C1)","The toolkit-based FAST-LIO implementation is slightly slower than the hand-derived version because of toolkit overhead and six extra extrinsic states (Sec. IV-C3; Table II)","Filter stability depends on the chosen boxplus and oplus operations and on the system; its analysis is left out of scope (Sec. III-G)",[35,36,37],"3D LiDAR (Livox AVIA solid-state LiDAR)","IMU (built into the Livox AVIA)","spinning multiline LiDAR and IMU data of the public LIO-SAM Campus sequences for the LINS re-implementation (sensor models not stated)",[39,40,41],"UAV (indoor flight, V1)","handheld (UAV held by hand and quickly shaken in V2-01; the carrying mode in V3 outdoor random walk is not stated in the TIE text)","public dataset (LIO-SAM Campus-small and Campus-large)","iterated error-state extended Kalman filter on compound manifolds in canonical form x_{k+1} = x_k oplus (dt f(x_k,u_k,w_k)); toolkit supports R^n, SO(3), SE_N(3) and S^2(r) primitives; experiment state manifold R3 x R3 x SO(3) x R3 x R3 x S2 x SO(3) x R3 with at most 5 iterations (Sec. III-H; Sec. IV-B; Sec. IV-C)","point-to-plane residuals of LiDAR points to planar features of the map, identical to FAST-LIO [14] (Sec. IV-A, Eq. 30); the LINS re-implementation keeps the original LINS feature extraction and outlier rejection (Sec. IV-C3)","discrete time (canonical form x_{k+1} = x_k oplus dt f(x_k,u_k,w_k))","not_reported in the TIE article (system taken from FAST-LIO [14]; motion compensation is not described)","none","global point cloud map (as in FAST-LIO)","IMU state (position, velocity, rotation, biases, gravity), online LiDAR-IMU extrinsic, and a point cloud map","onboard computer with a 1.8 GHz quad-core Intel i7-8550U and 8 GB RAM; average time per iteration 6.0 to 36.3 ms for IKFoM versus 5.7 to 34.5 ms for the hand-derived FAST-LIO filter; LINS re-implementation 8.4 and 10.3 ms versus 15.5 and 21.8 ms (Sec. IV; Tables II-III)","https:\u002F\u002Fgithub.com\u002Fhku-mars\u002FIKFoM","GPL-2.0",[53,57],{"relation":54,"title":55,"doi_or_url":56},"preprint","Kalman Filters on Differentiable Manifolds (arXiv:2102.03804, v1 2021-02-07, v3 2021-06-26)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.03804",{"relation":58,"title":59,"doi_or_url":50},"code_release","IKFoM toolkit",{"id":5,"kind":61,"shortName":7,"title":8,"authors":62,"year":9,"venue":66,"venueType":67,"publisher":68,"volumeIssuePages":69,"doi":70,"arxivId":71,"url":72,"firstPublicDate":73,"publicationStatus":16,"metadataStatus":74,"fulltextStatus":15,"era":10,"classicReason":75,"codeUrl":50,"cluster":11,"topics":76,"mdpi":77,"verification":78,"label":6,"fulltextRoute":79,"versionRead":80,"addedByCensus":77},"component",[63,64,65],"Dongjiao He","Wei Xu","Fu Zhang","IEEE Transactions on Industrial Electronics","journal","IEEE","70(12):12533-12544","10.1109\u002Ftie.2023.3237872","2102.03804","https:\u002F\u002Fapi.crossref.org\u002Fworks\u002F10.1109\u002Ftie.2023.3237872","2021-02-07","metadata_verified","not_applicable",[11],false,"corrected","NTU institutional (Chrome)","Version of record, IEEE Trans. Ind. Electron. 70(12):12533-12544 (Date of Publication 2023-01-23), IEEE Xplore HTML full text (document 10024988) read in Chrome with National Taiwan University access; Tables I-III read from the IEEE table images; the online Supplementary Material (proofs, manifold examples, extra results) was not read",[82,90,94,100,106],{"category":83,"model":84,"canonical":85,"role":86,"dataset":87,"specs":88,"locator":89},"lidar","Livox AVIA","Livox Avia","method input",null,"solid-state LiDAR with a built-in IMU","Sec. IV",{"category":91,"model":92,"canonical":92,"role":86,"dataset":87,"specs":93,"locator":89},"imu","not_reported (built-in IMU of the Livox AVIA)","not_reported",{"category":95,"model":96,"canonical":96,"role":97,"dataset":87,"specs":98,"locator":99},"compute","Intel i7-8550U onboard computer","compute for runtime","1.8 GHz quad-core CPU, 8 GB RAM","Sec. IV; Sec. IV-C3",{"category":101,"model":102,"canonical":102,"role":103,"dataset":87,"specs":104,"locator":105},"other","Vicon motion capture system","reference or ground truth","covers the start and end area of V2-01","Sec. IV-C1; Fig. 6",{"category":107,"model":108,"canonical":108,"role":86,"dataset":87,"specs":109,"locator":110},"platform","UAV (model not reported)","flown indoors (V1); held by hand and quickly shaken in V2-01; carrying mode in V3 outdoor random walk not stated in the TIE text","Sec. IV-C",[],{"totalRows":113,"groupCount":114,"groups":115,"others":263},14,3,[116,187,234],{"slug":117,"group":118,"sourceId":5,"sourceLabel":6,"table":119,"selfRows":120,"metrics":121,"seqs":125,"entrants":143,"cells":150,"outcomes":181,"locators":182,"hardware":183,"wordings":184,"notes":185},"he2023ikfom-table-i","he2023ikfom:Table I","Table I",6,[122],{"label":123,"unit":124,"statistic":93,"alignment":46},"odometry drift (%)","%",[126,130,132,135,138,141],{"dataset":127,"sequence":128,"environment":129},"own datasets (trial 01 by the authors; trial 02 from the FAST-LIO paper)","V1-01","indoor UAV flight",{"dataset":127,"sequence":131,"environment":129},"V1-02",{"dataset":127,"sequence":133,"environment":134},"V2-01","indoor quick-shake with the UAV held by hand",{"dataset":127,"sequence":136,"environment":137},"V2-02","indoor quick-shake (trial 02 from the FAST-LIO paper; holding mode not stated in the TIE text)",{"dataset":127,"sequence":139,"environment":140},"V3-01","outdoor random walk",{"dataset":127,"sequence":142,"environment":140},"V3-02",[144,147],{"name":145,"methodId":5,"linkable":146,"proposed":146,"self":146},"IKFoM-based",true,{"name":148,"methodId":149,"linkable":146,"proposed":77,"self":77},"Hand-derived [14] (FAST-LIO)","fastlio2021",[151,155,158,160,162,165,167,169,171,174,176,179],[152,152,152,153,154,152,154,154,152],0,0.414,-1,[156,152,152,157,154,152,154,154,152],1,0.527,[152,152,156,159,154,152,154,154,152],0.071,[156,152,156,161,154,152,154,154,152],0.328,[152,152,163,164,154,152,154,154,152],2,0.079,[156,152,163,166,154,152,154,154,152],0.015,[152,152,114,168,154,152,154,154,152],0.496,[156,152,114,170,154,152,154,154,152],0.558,[152,152,172,173,154,152,154,154,152],4,0.063,[156,152,172,175,154,152,154,154,152],0.06,[152,152,177,178,154,152,154,154,152],5,0.055,[156,152,177,180,154,152,154,154,152],0.076,[],[119],[],[],[186],"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",{"slug":188,"group":189,"sourceId":5,"sourceLabel":6,"table":190,"selfRows":120,"metrics":191,"seqs":206,"entrants":213,"cells":215,"outcomes":227,"locators":228,"hardware":229,"wordings":231,"notes":232},"he2023ikfom-table-ii","he2023ikfom:Table II","Table II",[192,196,198,200,202,204],{"label":193,"unit":194,"statistic":195,"alignment":93},"average running time per iteration (printed as 6.6±0.0047)","ms","mean",{"label":197,"unit":194,"statistic":195,"alignment":93},"average running time per iteration (printed as 6.7±0.0046)",{"label":199,"unit":194,"statistic":195,"alignment":93},"average running time per iteration (printed as 6.0±0.0027)",{"label":201,"unit":194,"statistic":195,"alignment":93},"average running time per iteration (printed as 18.6±0.0098)",{"label":203,"unit":194,"statistic":195,"alignment":93},"average running time per iteration (printed as 36.3±0.0095)",{"label":205,"unit":194,"statistic":195,"alignment":93},"average running time per iteration (printed as 23.1±0.0068)",[207,208,209,210,211,212],{"dataset":127,"sequence":128,"environment":129},{"dataset":127,"sequence":131,"environment":129},{"dataset":127,"sequence":133,"environment":134},{"dataset":127,"sequence":136,"environment":137},{"dataset":127,"sequence":139,"environment":140},{"dataset":127,"sequence":142,"environment":140},[214],{"name":145,"methodId":5,"linkable":146,"proposed":146,"self":146},[216,218,220,221,223,225],[152,152,152,217,154,152,152,154,152],6.6,[152,156,156,219,154,152,152,154,152],6.7,[152,163,163,120,154,152,152,154,152],[152,114,114,222,154,152,152,154,152],18.6,[152,172,172,224,154,152,152,154,152],36.3,[152,177,177,226,154,152,152,154,152],23.1,[],[190],[230],"onboard computer, 1.8 GHz quad-core Intel i7-8550U, 8 GB RAM",[],[233],"Average running time of one complete iteration of LiDAR-inertial navigation, IKFoM-based (six more states for extrinsics) versus hand-derived FAST-LIO, both on the UAV onboard computer; '±' term printed without definition",{"slug":235,"group":236,"sourceId":5,"sourceLabel":6,"table":237,"selfRows":163,"metrics":238,"seqs":243,"entrants":250,"cells":252,"outcomes":257,"locators":258,"hardware":259,"wordings":260,"notes":261},"he2023ikfom-table-iii","he2023ikfom:Table III","Table III",[239,241],{"label":240,"unit":194,"statistic":195,"alignment":93},"average running time per iteration (printed as 8.4±0.0042)",{"label":242,"unit":194,"statistic":195,"alignment":93},"average running time per iteration (printed as 10.3±0.0044)",[244,248],{"dataset":245,"sequence":246,"environment":247},"LIO-SAM open sequences","Campus-small","public LIO-SAM sequence (environment not described in the TIE text)",{"dataset":245,"sequence":249,"environment":247},"Campus-large",[251],{"name":145,"methodId":5,"linkable":146,"proposed":146,"self":146},[253,255],[152,152,152,254,154,152,152,154,152],8.4,[152,156,156,256,154,152,152,154,152],10.3,[],[237],[230],[],[262],"Average running time of one complete state-estimation iteration of LINS with its hand-derived IESEKF replaced by IKFoM versus the original LINS, on LIO-SAM open sequences, same onboard computer; '±' term printed without definition",[],1790510654674]