[{"data":1,"prerenderedAt":541},["ShallowReactive",2],{"method-iglio2024":3},{"method":4,"reference":53,"equipment":75,"figures":108,"results":109},{"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":32,"platform":35,"estimator":39,"association":40,"timeModel":41,"deskew":42,"loopClosure":43,"globalOptimization":43,"mapRepresentation":44,"prior":43,"outputGeometry":45,"compute":46,"codeUrl":47,"codeLicense":48,"relatedVersions":49},"iglio2024","Chen et al., 2024","iG-LIO","iG-LIO: An Incremental GICP-Based Tightly-Coupled LiDAR-Inertial Odometry",2024,"recent","C05","odometry_with_local_mapping","iG-LIO 將廣義 ICP（GICP）約束與 IMU 約束緊耦合於最大後驗（MAP）估計，以迭代式誤差狀態更新求解。作者以體素為基礎的表面共變異數估計器降低共變異數計算成本，並以增量式體素地圖儲存環境的機率模型，以減少最近鄰搜尋與地圖管理時間。作者強調所有資料集使用相同參數，效率高於 Faster-LIO 而精度相近。","Tightly couples GICP and IMU constraints in a MAP estimate with a voxel-based surface-covariance estimator and incremental voxel map, running faster than Faster-LIO with comparable accuracy.","full_text_reviewed","peer_reviewed_published","supplementary","not_reported（含校園大門手持重建與室內外資料）",[20,21],"public_benchmark","controlled_experiment",[23,24,25,26],"Identical parameters across all six datasets: NCLT, Newer College, ULHK, Botanic Garden, AVIA and self-collected GDUT (abstract; Sec. III)","Clearer map than FAST-LIO2 under manual flipping up to 183 deg\u002Fs attributed to midpoint-integration deskew (Sec. III-B-2)","1.2 to 1.5 times faster than Faster-LIO, 2.3 to 2.7 times faster than FastLIO2 and 1.5 to 3.5 times faster than DLIO on most sequences (Sec. III-A; Table II)","Returns to the start point in narrow indoor-outdoor handheld mapping where Faster-LIO and FastLIO2 drift 0.782 m and 1.537 m (Table IV; Sec. III-B-3)",[28,29,30,31],"Newer College ground truth has about 3 cm error when stationary, so small APE differences are treated as identical (Sec. III-B-2) (evaluation limitation)","Faster-LIO is slightly faster on 100 Hz Livox sequences avia_2 and avia_3 (Table II)","On the Livox avia Botanic Garden sequence bg_1*, APE 3.324 m is higher than the kd-tree variant iG-LIO* (2.032 m) (Table III)","AVIA and GDUT have no ground truth, so only end-to-end error is reported (Sec. III-B; Table IV)",[33,34],"3D LiDAR (mechanical and solid-state)","IMU",[36,37,38],"handheld","vehicle","wheeled UGV","MAP estimation combining IMU prior and GICP constraints, solved by Gauss-Newton iterations, with error-state covariance propagation analogous to the iterated error-state Kalman filter","GICP with voxel-based surface covariance estimator (VSCE); nearest neighbours via voxel hash indexes","discrete poses","IMU integration (midpoint integration) motion compensation before registration","none","incremental voxel map storing probabilistic (point and covariance) models","IMU-rate and LiDAR-rate odometry and voxel map; export format not_reported","CPU only: Intel i7-10875H (2.30 GHz x 16 cores), 32 GB RAM, ROS on Ubuntu 18.04; average 0.87 to 19.7 ms per scan; Faster-LIO slightly faster only on the 100 Hz avia_2 and avia_3 sequences (Sec. III; Sec. III-A; Table II)","https:\u002F\u002Fgithub.com\u002Fzijiechenrobotics\u002Fig_lio","GPL-2.0 (LICENSE file checked)",[50],{"relation":51,"title":52,"doi_or_url":47},"code_release","zijiechenrobotics\u002Fig_lio (includes early-access PDF)",{"id":5,"kind":54,"shortName":7,"title":8,"authors":55,"year":9,"venue":60,"venueType":61,"publisher":62,"volumeIssuePages":63,"doi":64,"arxivId":65,"url":66,"firstPublicDate":67,"publicationStatus":16,"metadataStatus":68,"fulltextStatus":15,"era":10,"classicReason":69,"codeUrl":47,"cluster":11,"topics":70,"mdpi":71,"verification":72,"label":6,"fulltextRoute":73,"versionRead":74,"addedByCensus":71},"method",[56,57,58,59],"Zijie Chen","Yong Xu","Shenghai Yuan","Lihua Xie","IEEE Robotics and Automation Letters","journal","IEEE","9(2):1883-1890","10.1109\u002Flra.2024.3349915",null,"https:\u002F\u002Fapi.crossref.org\u002Fworks\u002F10.1109\u002FLRA.2024.3349915","2024-01-04","metadata_verified","not_applicable",[11],false,"corrected","author copy","Author-posted IEEE RA-L accepted version (early access, 'accepted January 2024') from the authors' GitHub, read in full; IEEE Xplore version of record (RA-L 9(2):1883-1890) opened via NTU institutional access in Chrome: Sec. III text and Tables III-IV checked identical",[76,83,88,93,97,102],{"category":77,"model":78,"canonical":79,"role":80,"dataset":65,"specs":81,"locator":82},"lidar","Livox avia","Livox Avia","method input","handheld small-FOV solid-state LiDAR (Sec. III-B-3; Sec. III-C-3); captures the self-collected GDUT data (Sec. III-B-1)","Sec. III-B-1; Sec. III-B-3; Sec. III-C-3",{"category":77,"model":78,"canonical":79,"role":84,"dataset":85,"specs":86,"locator":87},"dataset sensor","AVIA (from FastLIO2 and R3LIVE); Botanic Garden","handheld; avia_2 and avia_3 sampled at 100 Hz; also used in Botanic Garden '*' sequences","Sec. III; Sec. III-B-1; Sec. III-B-5; Tables II-IV",{"category":77,"model":89,"canonical":89,"role":84,"dataset":90,"specs":91,"locator":92},"Velodyne HDL-32E","NCLT; ULHK","360 deg mechanical LiDAR","Sec. III-B-1; Sec. III-B-4",{"category":77,"model":94,"canonical":94,"role":84,"dataset":95,"specs":91,"locator":96},"Ouster OS1-64","Newer College (NCD)","Sec. III-B-1",{"category":77,"model":98,"canonical":98,"role":84,"dataset":99,"specs":100,"locator":101},"Velodyne VLP-16","Botanic Garden","not_reported","Sec. III-B-5",{"category":103,"model":104,"canonical":104,"role":105,"dataset":65,"specs":106,"locator":107},"compute","Intel i7-10875H","compute for runtime","2.30 GHz x 16 cores, 32 GB RAM, ROS on Ubuntu 18.04","Sec. III",[],{"totalRows":110,"groupCount":111,"groups":112,"others":540},26,3,[113,387,483],{"slug":114,"group":115,"sourceId":5,"sourceLabel":6,"table":116,"selfRows":117,"metrics":118,"seqs":126,"entrants":166,"cells":182,"outcomes":380,"locators":382,"hardware":383,"wordings":384,"notes":385},"iglio2024-table-iii","iglio2024:Table III","Table III",16,[119,124],{"label":120,"unit":121,"statistic":122,"alignment":123},"Absolute pose error (RMSE, meters)","m","RMSE","SE3",{"label":120,"unit":121,"statistic":122,"alignment":125},"first-pose",[127,131,133,135,137,139,142,144,146,148,150,154,156,160,162,164],{"dataset":128,"sequence":129,"environment":130},"NCLT","nclt_1","campus (Velodyne HDL-32E)",{"dataset":128,"sequence":132,"environment":130},"nclt_2",{"dataset":128,"sequence":134,"environment":130},"nclt_3",{"dataset":128,"sequence":136,"environment":130},"nclt_4",{"dataset":128,"sequence":138,"environment":130},"nclt_5",{"dataset":95,"sequence":140,"environment":141},"ncd_1","handheld campus (Ouster OS1-64)",{"dataset":95,"sequence":143,"environment":141},"ncd_2",{"dataset":95,"sequence":145,"environment":141},"ncd_3",{"dataset":95,"sequence":147,"environment":141},"ncd_4",{"dataset":95,"sequence":149,"environment":141},"ncd_5",{"dataset":151,"sequence":152,"environment":153},"ULHK (UrbanLoco)","ulhk_1","dense urban dynamic scenes (Velodyne HDL-32E)",{"dataset":151,"sequence":155,"environment":153},"ulhk_2",{"dataset":157,"sequence":158,"environment":159},"Botanic Garden (BG)","bg_1","botanic garden, unstructured",{"dataset":157,"sequence":161,"environment":159},"bg_2",{"dataset":157,"sequence":163,"environment":159},"bg_1* (Livox avia)",{"dataset":157,"sequence":165,"environment":159},"bg_2* (Livox avia)",[167,169,171,173,176,179],{"name":7,"methodId":5,"linkable":168,"proposed":168,"self":168},true,{"name":170,"methodId":65,"linkable":71,"proposed":71,"self":71},"iG-LIO* (kd-tree surface covariance variant, ablation)",{"name":172,"methodId":65,"linkable":71,"proposed":71,"self":71},"NDT-LIO (ablation)",{"name":174,"methodId":175,"linkable":168,"proposed":71,"self":71},"Faster-LIO","fasterlio2022",{"name":177,"methodId":178,"linkable":168,"proposed":71,"self":71},"FastLIO2","fastlio2_2022",{"name":180,"methodId":181,"linkable":168,"proposed":71,"self":71},"DLIO","dlio2023",[183,187,190,193,195,198,201,203,205,207,209,211,213,215,217,219,221,223,225,227,229,231,233,235,237,239,241,243,245,247,249,251,253,255,257,259,261,264,265,267,269,271,273,276,278,279,281,283,285,288,289,290,292,293,295,297,298,300,302,304,306,309,311,313,315,317,318,321,323,325,327,329,331,334,336,338,340,342,344,347,349,351,353,354,356,359,361,363,365,367,368,371,373,375,377,379],[184,184,184,185,186,184,186,186,184],0,1.673,-1,[188,184,184,189,186,184,186,186,184],1,1.795,[191,184,184,192,186,184,186,186,184],2,2.365,[111,184,184,194,186,184,186,186,184],1.855,[196,184,184,197,186,184,186,186,184],4,1.734,[199,184,184,200,186,184,186,186,184],5,2.104,[184,184,188,202,186,184,186,186,184],1.23,[188,184,188,204,186,184,186,186,184],1.209,[191,184,188,206,186,184,186,186,184],2.005,[111,184,188,208,186,184,186,186,184],1.279,[196,184,188,210,186,184,186,186,184],1.485,[199,184,188,212,186,184,186,186,184],1.392,[184,184,191,214,186,184,186,186,184],1.558,[188,184,191,216,186,184,186,186,184],1.56,[191,184,191,218,186,184,186,186,184],4.173,[111,184,191,220,186,184,186,186,184],2.141,[196,184,191,222,186,184,186,186,184],2.454,[199,184,191,224,186,184,186,186,184],2.581,[184,184,111,226,186,184,186,186,184],1.496,[188,184,111,228,186,184,186,186,184],1.514,[191,184,111,230,186,184,186,186,184],2.583,[111,184,111,232,186,184,186,186,184],1.544,[196,184,111,234,186,184,186,186,184],2.112,[199,184,111,236,186,184,186,186,184],2.311,[184,184,196,238,186,184,186,186,184],0.956,[188,184,196,240,186,184,186,186,184],1.062,[191,184,196,242,186,184,186,186,184],0.991,[111,184,196,244,186,184,186,186,184],0.933,[196,184,196,246,186,184,186,186,184],0.89,[199,184,196,248,186,184,186,186,184],1.088,[184,184,199,250,186,184,186,186,184],0.322,[188,184,199,252,186,184,186,186,184],0.317,[191,184,199,254,186,184,186,186,184],0.371,[111,184,199,256,186,184,186,186,184],0.34,[196,184,199,258,186,184,186,186,184],0.353,[199,184,199,260,186,184,186,186,184],0.361,[184,184,262,263,186,184,186,186,184],6,0.375,[188,184,262,260,186,184,186,186,184],[191,184,262,266,186,184,186,186,184],0.424,[111,184,262,268,186,184,186,186,184],0.373,[196,184,262,270,186,184,186,186,184],0.376,[199,184,262,272,186,184,186,186,184],0.393,[184,184,274,275,186,184,186,186,184],7,0.099,[188,184,274,277,186,184,186,186,184],0.101,[191,184,274,277,186,184,186,186,184],[111,184,274,280,186,184,186,186,184],0.119,[196,184,274,282,186,184,186,186,184],0.125,[199,184,274,284,186,184,186,186,184],0.105,[184,184,286,287,186,184,186,186,184],8,0.083,[188,184,286,287,186,184,186,186,184],[191,184,286,287,186,184,186,186,184],[111,184,286,291,186,184,186,186,184],0.079,[196,184,286,291,186,184,186,186,184],[199,184,286,294,186,184,186,186,184],0.121,[184,184,296,282,186,184,186,186,184],9,[188,184,296,282,186,184,186,186,184],[191,184,296,299,186,184,186,186,184],0.134,[111,184,296,301,186,184,186,186,184],0.141,[196,184,296,303,186,184,186,186,184],0.127,[199,184,296,305,186,184,186,186,184],0.157,[184,184,307,308,186,184,186,186,184],10,1.153,[188,184,307,310,186,184,186,186,184],1.271,[191,184,307,312,186,184,186,186,184],1.338,[111,184,307,314,186,184,186,186,184],1.272,[196,184,307,316,186,184,186,186,184],1.196,[199,184,307,206,186,184,186,186,184],[184,184,319,320,186,184,186,186,184],11,1.776,[188,184,319,322,186,184,186,186,184],1.77,[191,184,319,324,186,184,186,186,184],1.807,[111,184,319,326,186,184,186,186,184],1.906,[196,184,319,328,186,184,186,186,184],1.804,[199,184,319,330,186,184,186,186,184],4.254,[184,188,332,333,186,184,186,186,184],12,1.675,[188,188,332,335,186,184,186,186,184],1.758,[191,188,332,337,186,184,186,186,184],1.704,[111,188,332,339,186,184,186,186,184],1.697,[196,188,332,341,186,184,186,186,184],1.911,[199,188,332,343,186,184,186,186,184],1.798,[184,188,345,346,186,184,186,186,184],13,1.606,[188,188,345,348,186,184,186,186,184],1.625,[191,188,345,350,186,184,186,186,184],1.782,[111,188,345,352,186,184,186,186,184],2.086,[196,188,345,185,186,184,186,186,184],[199,188,345,355,186,184,186,186,184],2.245,[184,188,357,358,186,184,186,186,184],14,3.324,[188,188,357,360,186,184,186,186,184],2.032,[191,188,357,362,186,184,186,186,184],4.651,[111,188,357,364,186,184,186,186,184],9.825,[196,188,357,366,186,184,186,186,184],37.625,[199,188,357,65,184,184,186,186,184],[184,188,369,370,186,184,186,186,184],15,2.891,[188,188,369,372,186,184,186,186,184],3.263,[191,188,369,374,186,184,186,186,184],4.617,[111,188,369,376,186,184,186,186,184],3.526,[196,188,369,378,186,184,186,186,184],4.124,[199,188,369,65,184,184,186,186,184],[381],"not_run ('-': method did not participate)",[116],[],[],[386],"Absolute pose error (RMSE, m); identical iG-LIO parameters for all sequences; BG sequences evaluated with origin alignment, others with SE(3) alignment; '*' marks Livox avia sequences",{"slug":388,"group":389,"sourceId":5,"sourceLabel":6,"table":390,"selfRows":262,"metrics":391,"seqs":396,"entrants":410,"cells":416,"outcomes":475,"locators":477,"hardware":478,"wordings":480,"notes":481},"iglio2024-table-ii","iglio2024:Table II","Table II",[392],{"label":393,"unit":394,"statistic":395,"alignment":43},"Time (ms) per scan","ms","mean",[397,399,401,403,405,408],{"dataset":128,"sequence":129,"environment":398},"see dataset",{"dataset":400,"sequence":140,"environment":398},"NCD",{"dataset":402,"sequence":152,"environment":398},"ULHK",{"dataset":404,"sequence":158,"environment":398},"BG",{"dataset":406,"sequence":407,"environment":398},"AVIA","avia_1",{"dataset":406,"sequence":409,"environment":398},"avia_2",[411,412,413,414,415],{"name":7,"methodId":5,"linkable":168,"proposed":168,"self":168},{"name":170,"methodId":65,"linkable":71,"proposed":71,"self":71},{"name":174,"methodId":175,"linkable":168,"proposed":71,"self":71},{"name":177,"methodId":178,"linkable":168,"proposed":71,"self":71},{"name":180,"methodId":181,"linkable":168,"proposed":71,"self":71},[417,419,421,423,425,427,429,431,433,435,437,439,441,443,445,447,449,451,453,455,457,459,461,463,465,466,468,470,472,474],[184,184,184,418,186,184,184,186,184],8.524,[188,184,184,420,186,184,184,186,184],13.716,[191,184,184,422,186,184,184,186,184],10.07,[111,184,184,424,186,184,184,186,184],22.536,[196,184,184,426,186,184,184,186,184],31.808,[184,184,188,428,186,184,184,186,184],15.543,[188,184,188,430,186,184,184,186,184],26.835,[191,184,188,432,186,184,184,186,184],18.932,[111,184,188,434,186,184,184,186,184],40.777,[196,184,188,436,186,184,184,186,184],47.384,[184,184,191,438,186,184,184,186,184],6.3,[188,184,191,440,186,184,184,186,184],9.396,[191,184,191,442,186,184,184,186,184],7.23,[111,184,191,444,186,184,184,186,184],10.425,[196,184,191,446,186,184,184,186,184],11.145,[184,184,111,448,186,184,184,186,184],5.958,[188,184,111,450,186,184,184,186,184],10.579,[191,184,111,452,186,184,184,186,184],7.367,[111,184,111,454,186,184,184,186,184],12.345,[196,184,111,456,186,184,184,186,184],14.37,[184,184,196,458,186,184,184,186,184],3.82,[188,184,196,460,186,184,184,186,184],6.12,[191,184,196,462,186,184,184,186,184],4.433,[111,184,196,464,186,184,184,186,184],5.957,[196,184,196,65,184,184,184,186,184],[184,184,199,467,186,184,184,186,184],0.869,[188,184,199,469,186,184,184,186,184],1.25,[191,184,199,471,186,184,184,186,184],0.65,[111,184,199,473,186,184,184,186,184],0.901,[196,184,199,65,184,184,184,186,184],[476],"not_run ('-': did not participate)",[390],[479],"Intel i7-10875H CPU (2.30 GHz x 16 cores), 32 GB RAM, ROS on Ubuntu 18.04",[],[482],"Average processing time per scan (ms); only 6 of 20 sequences kept (nclt_1, ncd_1, ulhk_1, bg_1, avia_1 and the 100 Hz avia_2; bg_1*, bg_2*, avia_3, gdut_1 and the other NCLT, NCD, ULHK and BG runs omitted); feature-count columns omitted; voxel size 0.5 m for iG-LIO",{"slug":484,"group":485,"sourceId":5,"sourceLabel":6,"table":486,"selfRows":196,"metrics":487,"seqs":490,"entrants":502,"cells":508,"outcomes":533,"locators":535,"hardware":536,"wordings":537,"notes":538},"iglio2024-table-iv","iglio2024:Table IV","Table IV",[488],{"label":489,"unit":121,"statistic":100,"alignment":43},"End to end errors (meters)",[491,493,495,498],{"dataset":406,"sequence":407,"environment":492},"handheld Livox avia, large indoor-outdoor loop (0.96 km)",{"dataset":406,"sequence":409,"environment":494},"handheld Livox avia at 100 Hz (0.14 km)",{"dataset":406,"sequence":496,"environment":497},"avia_3","handheld Livox avia at 100 Hz (0.09 km)",{"dataset":499,"sequence":500,"environment":501},"GDUT (self-collected)","gdut_1","handheld Livox avia, self-collected GDUT sequence (0.27 km)",[503,504,505,506,507],{"name":7,"methodId":5,"linkable":168,"proposed":168,"self":168},{"name":170,"methodId":65,"linkable":71,"proposed":71,"self":71},{"name":172,"methodId":65,"linkable":71,"proposed":71,"self":71},{"name":174,"methodId":175,"linkable":168,"proposed":71,"self":71},{"name":177,"methodId":178,"linkable":168,"proposed":71,"self":71},[509,510,511,512,514,516,517,518,519,521,523,524,525,526,527,528,529,530,531,532],[184,184,184,65,184,184,186,186,184],[188,184,184,65,184,184,186,186,184],[191,184,184,65,184,184,186,186,184],[111,184,184,513,186,184,186,186,184],0.782,[196,184,184,515,186,184,186,186,184],1.537,[184,184,188,65,184,184,186,186,184],[188,184,188,65,184,184,186,186,184],[191,184,188,65,184,184,186,186,184],[111,184,188,520,186,184,186,186,184],0.177,[196,184,188,522,186,184,186,186,184],0.226,[184,184,191,65,184,184,186,186,184],[188,184,191,65,184,184,186,186,184],[191,184,191,65,184,184,186,186,184],[111,184,191,65,184,184,186,186,184],[196,184,191,65,184,184,186,186,184],[184,184,111,65,184,184,186,186,184],[188,184,111,65,184,184,186,186,184],[191,184,111,65,184,184,186,186,184],[111,184,111,65,184,184,186,186,184],[196,184,111,65,184,184,186,186,184],[534],"below 0.1 m (reported as '\u003C0.1')",[486],[],[],[539],"End-to-end drift (m) for loops starting and ending at the same place; no ground truth available for AVIA and GDUT",[],1790510658275]