[{"data":1,"prerenderedAt":684},["ShallowReactive",2],{"method-palvio2026":3},{"method":4,"reference":57,"equipment":77,"figures":147,"results":148},{"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":25,"sensors":31,"platform":35,"estimator":39,"association":40,"timeModel":41,"deskew":42,"loopClosure":43,"globalOptimization":44,"mapRepresentation":45,"prior":44,"outputGeometry":46,"compute":47,"codeUrl":48,"codeLicense":49,"relatedVersions":50},"palvio2026","Tang et al., 2026","PA-LVIO","PA-LVIO: Real-Time LiDAR-Visual-Inertial Odometry and Mapping with Pose-Only Bundle Adjustment",2026,"recent","C07","odometry_with_local_mapping","PA-LVIO 提出僅含位姿的光束法平差（pose-only bundle adjustment），把光達與視覺的多幀幾何約束轉為幀間位姿約束，在滑動視窗因子圖中與 IMU 預積分緊密融合，以降低計算量。另加入不需邊緣化的幀對地圖（frame-to-map）光達位姿約束抑制漂移，並以 IMU 為中心線上估計光達、相機的時空參數，達到像素級對齊後產生 RGB 上色點雲地圖。","A sliding-window LVIO that converts LiDAR and visual multi-frame constraints into pose-only factors, adds a marginalization-free frame-to-map LiDAR factor, and calibrates spatio-temporal parameters online to render RGB point clouds.","full_text_reviewed","preprint","supplementary","not_reported（i2Nav-Robot 為建物、停車場、操場與街道序列；MARS-LVIG 為 80 至 130 m 高度的無人機序列；R3LIVE 資料集與自採 HandNav 為手持序列，HandNav 拍攝武漢大學傳統建築；未見營建工地）",[20],"public_benchmark",[22,23,24],"Evaluated on 28 sequences exceeding 50 km across wheeled robot, UAV and handheld data (abstract)","Online LiDAR-camera rotation estimates consistent across ten sequences (standard deviation below 0.04 deg per axis) (Table V)","Real-time on onboard ARM computer (abstract; Sec. IV)",[26,27,28,29,30],"Marginalization-free F2M measurement may reduce odometry accuracy in certain circumstances (Sec. V; Sec. IV-B4)","Factor graph optimization is computationally heavy; MSCKF variant planned (Sec. V)","Mapping quality evaluated only qualitatively on selected scenes (Sec. IV-C2; Figs. 9-10)","On the short campus02 and hku-park0 sequences (under 500 s) the end-to-end error is decimeter-level (0.18 m and 0.13 m) while FAST-LIO2, R3LIVE and FAST-LIVO2 reach centimeter level (Table III; Sec. IV-B3)","No loop-closure or place-recognition module is described in the pipeline; drift is addressed by the marginalization-free F2M pose factor (Sec. II; Sec. III-D)",[32,33,34],"3D LiDAR (Livox AVIA 10 Hz on MARS-LVIG, R3LIVE and HandNav data; Hesai AT128 10 Hz on i2Nav-Robot)","IMU (MEMS; BMI088 on MARS-LVIG, R3LIVE and HandNav; ADIS16465 on i2Nav-Robot; 200 Hz)","RGB camera (models not reported; 1280x1024 to 2448x2048 at 10 to 15 Hz)",[36,37,38],"wheeled UGV","UAV","handheld","INS-centric sliding-window factor graph optimization with pose-only bundle adjustment factors for LiDAR and visual measurements, IMU preintegration, and a marginalization-free frame-to-map LiDAR pose factor","same-plane LiDAR points associated across keyframes (following BA-LINS); visual features tracked with INS prior; frame-to-map LiDAR pose optimization against a global ikd-Tree map","discrete poses; LiDAR frames projected to visual keyframe times; online camera-IMU and LiDAR time-delay and extrinsic calibration","point clouds undistorted using high-rate INS pose (Sec. II)","none (authors note place recognition or loop closure could be added; Sec. I)","none","global point cloud map in ikd-Tree; RGB-rendered point-cloud map","RGB-rendered point-cloud map (abstract, Sec. III end)","desktop AMD Ryzen 9 9950X with CUDA-accelerated OpenCV on an NVIDIA RTX 5090 (Sec. IV-A; Sec. IV-D names RTX 5080); NVIDIA Orin NX (6-core CPU, 8 GB) on HandNav with images resized to 900x600: average LiDAR 3.27 vs 20.40 ms, visual 7.07 vs 28.91 ms and FGO 39.06 vs 85.35 ms per frame on PC vs ARM (Table VI), equivalent 33.2 Hz and 13.0 Hz (Table VII); on i2Nav-Robot the F2M pose optimization averages 3.19 ms and the loosely coupled FGO 26.96 ms vs 33.95 ms tightly coupled (Table IV)","https:\u002F\u002Fgithub.com\u002Fi2Nav-WHU\u002FPA-LVIO","GPL-3.0 LICENSE file present and README restricts use to academic purposes; however, as of 2026-09-25 the repository contains only README.md, LICENSE and paper\u002F (README news 2026-03-19: 'The codes will be released soon'), although the arXiv abstract states the code is open-sourced",[51,54],{"relation":16,"title":52,"doi_or_url":53},"PA-LVIO arXiv v1 2026-03-17, v2 2026-03-24","https:\u002F\u002Farxiv.org\u002Fabs\u002F2603.16228",{"relation":55,"title":56,"doi_or_url":48},"code_release","i2Nav-WHU\u002FPA-LVIO",{"id":5,"kind":58,"shortName":7,"title":8,"authors":59,"year":9,"venue":66,"venueType":16,"publisher":66,"volumeIssuePages":67,"doi":68,"arxivId":69,"url":53,"firstPublicDate":70,"publicationStatus":16,"metadataStatus":71,"fulltextStatus":15,"era":10,"classicReason":72,"codeUrl":48,"cluster":11,"topics":73,"mdpi":74,"verification":75,"label":6,"fulltextRoute":66,"versionRead":76,"addedByCensus":74},"method",[60,61,62,63,64,65],"Hailiang Tang","Tisheng Zhang","Liqiang Wang","Xin Ding","Man Yuan","Xiaoji Niu","arXiv","not_reported",null,"2603.16228","2026-03-17","metadata_verified","not_applicable",[11],false,"corrected","arXiv 2603.16228 v2 (2026-03-24), all 14 pages; no journal version found",[78,86,91,96,100,104,108,110,114,116,119,120,124,129,131,134,135,141,144],{"category":79,"model":80,"canonical":81,"role":82,"dataset":83,"specs":84,"locator":85},"lidar","Livox AVIA","Livox Avia","method input","HandNav (private)","10 Hz (Table I)","Table I; Fig. 4",{"category":87,"model":88,"canonical":88,"role":82,"dataset":83,"specs":89,"locator":90},"camera","RGB camera (model not reported)","1800x1200, 10 Hz; resized to 900x600 on the ARM computer (Table I; Sec. IV-D)","Table I; Sec. IV-D",{"category":92,"model":93,"canonical":93,"role":82,"dataset":83,"specs":94,"locator":95},"imu","BMI088","200 Hz (Table I)","Table I",{"category":97,"model":98,"canonical":98,"role":82,"dataset":83,"specs":99,"locator":85},"platform","HandNav handheld device","3 sequences, 1.5 km (Table I)",{"category":79,"model":101,"canonical":101,"role":102,"dataset":103,"specs":84,"locator":95},"Hesai AT128","dataset sensor","i2Nav-Robot",{"category":87,"model":105,"canonical":105,"role":102,"dataset":103,"specs":106,"locator":107},"camera of i2Nav-Robot (model not reported)","1600x1200, 10 Hz; pixel size 5.86 um, focal length 6 mm (Table I; Sec. IV-C1)","Table I; Sec. IV-C1",{"category":92,"model":109,"canonical":109,"role":102,"dataset":103,"specs":94,"locator":95},"ADIS16465",{"category":97,"model":111,"canonical":111,"role":102,"dataset":103,"specs":112,"locator":113},"low-speed wheeled robot (model not reported)","10 sequences, 17.1 km","Sec. IV-A; Table I",{"category":79,"model":80,"canonical":81,"role":102,"dataset":115,"specs":84,"locator":95},"MARS-LVIG",{"category":87,"model":117,"canonical":117,"role":102,"dataset":115,"specs":118,"locator":95},"camera of MARS-LVIG (model not reported)","2448x2048, 10 Hz (Table I)",{"category":92,"model":93,"canonical":93,"role":102,"dataset":115,"specs":94,"locator":95},{"category":97,"model":121,"canonical":121,"role":102,"dataset":115,"specs":122,"locator":123},"UAV (model not reported)","3 to 12 m\u002Fs, altitude 80 to 130 m; 9 sequences, 32.6 km","Sec. IV-B2; Table I",{"category":125,"model":126,"canonical":126,"role":127,"dataset":115,"specs":67,"locator":128},"gnss","RTK (low-rate, provided with MARS-LVIG) post-processed with RTK\u002FINS software into high-rate ground truth","reference or ground truth","Sec. IV-B2",{"category":79,"model":80,"canonical":81,"role":102,"dataset":130,"specs":84,"locator":95},"R3LIVE dataset",{"category":87,"model":132,"canonical":132,"role":102,"dataset":130,"specs":133,"locator":95},"camera of the R3LIVE dataset (model not reported)","1280x1024, 15 Hz (Table I)",{"category":92,"model":93,"canonical":93,"role":102,"dataset":130,"specs":94,"locator":95},{"category":136,"model":137,"canonical":137,"role":138,"dataset":68,"specs":139,"locator":140},"compute","AMD Ryzen 9 9950X CPU","compute for runtime","desktop PC","Sec. IV-A; Sec. IV-D",{"category":136,"model":142,"canonical":142,"role":138,"dataset":68,"specs":143,"locator":140},"NVIDIA RTX 5090 GPU (Sec. IV-A); RTX 5080 GPU (Sec. IV-D)","CUDA-accelerated OpenCV for visual processing",{"category":136,"model":145,"canonical":145,"role":138,"dataset":83,"specs":146,"locator":140},"NVIDIA Orin NX","6-core CPU, 8 GB RAM",[],{"totalRows":149,"groupCount":150,"groups":151,"others":678},39,5,[152,491,601,646],{"slug":153,"group":154,"sourceId":5,"sourceLabel":6,"table":155,"selfRows":156,"metrics":157,"seqs":162,"entrants":206,"cells":223,"outcomes":484,"locators":486,"hardware":487,"wordings":488,"notes":489},"palvio2026-table-ii","palvio2026:Table II","Table II",21,[158],{"label":159,"unit":160,"statistic":161,"alignment":67},"absolute translation error (RMSE, meters)","m","RMSE",[163,166,168,170,172,174,176,178,180,182,184,186,189,191,193,195,197,199,201,203,205],{"dataset":103,"sequence":164,"environment":165},"building00","low-speed wheeled robot; building, parking, playground and street sequences (indoor-outdoor)",{"dataset":103,"sequence":167,"environment":165},"building01",{"dataset":103,"sequence":169,"environment":165},"building02",{"dataset":103,"sequence":171,"environment":165},"parking00",{"dataset":103,"sequence":173,"environment":165},"parking01",{"dataset":103,"sequence":175,"environment":165},"parking02",{"dataset":103,"sequence":177,"environment":165},"playground00",{"dataset":103,"sequence":179,"environment":165},"street00",{"dataset":103,"sequence":181,"environment":165},"street01",{"dataset":103,"sequence":183,"environment":165},"street02",{"dataset":103,"sequence":185,"environment":165},"Average",{"dataset":115,"sequence":187,"environment":188},"AMtown01","UAV at 3 to 12 m\u002Fs, cruising altitude 80 m (AMtown, HKairport) to 130 m (AMvalley)",{"dataset":115,"sequence":190,"environment":188},"AMtown02",{"dataset":115,"sequence":192,"environment":188},"AMtown03",{"dataset":115,"sequence":194,"environment":188},"AMvalley01",{"dataset":115,"sequence":196,"environment":188},"AMvalley02",{"dataset":115,"sequence":198,"environment":188},"AMvalley03",{"dataset":115,"sequence":200,"environment":188},"HKairport01",{"dataset":115,"sequence":202,"environment":188},"HKairport02",{"dataset":115,"sequence":204,"environment":188},"HKairport03",{"dataset":115,"sequence":185,"environment":188},[207,209,213,215,218,221],{"name":208,"methodId":68,"linkable":74,"proposed":74,"self":74},"FF-LINS",{"name":210,"methodId":211,"linkable":212,"proposed":74,"self":74},"FAST-LIO2","fastlio2_2022",true,{"name":214,"methodId":68,"linkable":74,"proposed":74,"self":74},"LE-VINS",{"name":216,"methodId":217,"linkable":212,"proposed":74,"self":74},"R3LIVE","r3live2022",{"name":219,"methodId":220,"linkable":212,"proposed":74,"self":74},"FAST-LIVO2","fastlivo2_2025",{"name":222,"methodId":5,"linkable":212,"proposed":212,"self":212},"Ours (PA-LVIO)",[224,228,231,234,236,239,241,243,245,247,248,250,252,254,256,258,260,262,264,266,268,270,272,274,276,278,280,282,284,285,287,289,290,292,294,296,297,300,302,304,306,308,310,313,315,317,319,321,322,325,327,328,330,332,334,337,339,341,343,345,347,350,352,354,356,357,359,362,364,366,368,370,372,375,376,378,380,382,384,387,389,391,393,395,397,400,402,404,406,408,410,413,415,417,419,421,423,426,428,430,432,434,436,439,441,443,445,447,448,451,453,455,457,459,461,464,466,468,470,471,472,475,477,479,481,483],[225,225,225,226,227,225,227,227,225],0,2.32,-1,[229,225,225,230,227,225,227,227,225],1,0.68,[232,225,225,233,227,225,227,227,225],2,1.17,[235,225,225,68,225,225,227,227,225],3,[237,225,225,238,227,225,227,227,225],4,1.1,[150,225,225,240,227,225,227,227,225],0.34,[225,225,229,242,227,225,227,227,225],1.16,[229,225,229,244,227,225,227,227,225],0.38,[232,225,229,246,227,225,227,227,225],1.01,[235,225,229,68,225,225,227,227,225],[237,225,229,249,227,225,227,227,225],0.19,[150,225,229,251,227,225,227,227,225],0.17,[225,225,232,253,227,225,227,227,225],6.73,[229,225,232,255,227,225,227,227,225],1.46,[232,225,232,257,227,225,227,227,225],2.35,[235,225,232,259,227,225,227,227,225],4.49,[237,225,232,261,227,225,227,227,225],1.65,[150,225,232,263,227,225,227,227,225],0.47,[225,225,235,265,227,225,227,227,225],1.94,[229,225,235,267,227,225,227,227,225],0.25,[232,225,235,269,227,225,227,227,225],0.98,[235,225,235,271,227,225,227,227,225],0.59,[237,225,235,273,227,225,227,227,225],0.26,[150,225,235,275,227,225,227,227,225],0.11,[225,225,237,277,227,225,227,227,225],1.06,[229,225,237,279,227,225,227,227,225],0.44,[232,225,237,281,227,225,227,227,225],0.69,[235,225,237,283,227,225,227,227,225],0.39,[237,225,237,251,227,225,227,227,225],[150,225,237,286,227,225,227,227,225],0.14,[225,225,150,288,227,225,227,227,225],1.31,[229,225,150,255,227,225,227,227,225],[232,225,150,291,227,225,227,227,225],0.79,[235,225,150,293,227,225,227,227,225],0.66,[237,225,150,295,227,225,227,227,225],0.18,[150,225,150,275,227,225,227,227,225],[225,225,298,299,227,225,227,227,225],6,1.71,[229,225,298,301,227,225,227,227,225],0.31,[232,225,298,303,227,225,227,227,225],1.34,[235,225,298,305,227,225,227,227,225],0.62,[237,225,298,307,227,225,227,227,225],0.53,[150,225,298,309,227,225,227,227,225],0.3,[225,225,311,312,227,225,227,227,225],7,3.32,[229,225,311,314,227,225,227,227,225],1.86,[232,225,311,316,227,225,227,227,225],1.18,[235,225,311,318,227,225,227,227,225],1.61,[237,225,311,320,227,225,227,227,225],1.12,[150,225,311,263,227,225,227,227,225],[225,225,323,324,227,225,227,227,225],8,4.2,[229,225,323,326,227,225,227,227,225],1.28,[232,225,323,261,227,225,227,227,225],[235,225,323,329,227,225,227,227,225],2.36,[237,225,323,331,227,225,227,227,225],2.45,[150,225,323,333,227,225,227,227,225],0.91,[225,225,335,336,227,225,227,227,225],9,3.78,[229,225,335,338,227,225,227,227,225],1.49,[232,225,335,340,227,225,227,227,225],2.16,[235,225,335,342,227,225,227,227,225],1.4,[237,225,335,344,227,225,227,227,225],2.92,[150,225,335,346,227,225,227,227,225],1.15,[225,225,348,349,227,225,227,227,225],10,2.75,[229,225,348,351,227,225,227,227,225],0.96,[232,225,348,353,227,225,227,227,225],1.33,[235,225,348,355,227,225,227,227,225],1.51,[237,225,348,277,227,225,227,227,225],[150,225,348,358,227,225,227,227,225],0.54,[225,225,360,361,227,225,227,227,225],11,3.35,[229,225,360,363,227,225,227,227,225],2.99,[232,225,360,365,227,225,227,227,225],13.72,[235,225,360,367,227,225,227,227,225],1.67,[237,225,360,369,227,225,227,227,225],2.57,[150,225,360,371,227,225,227,227,225],3.05,[225,225,373,374,227,225,227,227,225],12,4.67,[229,225,373,361,227,225,227,227,225],[232,225,373,377,227,225,227,227,225],11.32,[235,225,373,379,227,225,227,227,225],2.24,[237,225,373,381,227,225,227,227,225],2.82,[150,225,373,383,227,225,227,227,225],2.67,[225,225,385,386,227,225,227,227,225],13,2.53,[229,225,385,388,227,225,227,227,225],3.54,[232,225,385,390,227,225,227,227,225],31.23,[235,225,385,392,227,225,227,227,225],3.7,[237,225,385,394,227,225,227,227,225],3.25,[150,225,385,396,227,225,227,227,225],1.56,[225,225,398,399,227,225,227,227,225],14,1.77,[229,225,398,401,227,225,227,227,225],6.72,[232,225,398,403,227,225,227,227,225],12.92,[235,225,398,405,227,225,227,227,225],3.91,[237,225,398,407,227,225,227,227,225],7.78,[150,225,398,409,227,225,227,227,225],2.05,[225,225,411,412,227,225,227,227,225],15,2.15,[229,225,411,414,227,225,227,227,225],7.75,[232,225,411,416,227,225,227,227,225],12.74,[235,225,411,418,227,225,227,227,225],3.98,[237,225,411,420,227,225,227,227,225],3.55,[150,225,411,422,227,225,227,227,225],2.01,[225,225,424,425,227,225,227,227,225],16,3.62,[229,225,424,427,227,225,227,227,225],11.98,[232,225,424,429,227,225,227,227,225],18.88,[235,225,424,431,227,225,227,227,225],3.95,[237,225,424,433,227,225,227,227,225],1.43,[150,225,424,435,227,225,227,227,225],2.77,[225,225,437,438,227,225,227,227,225],17,0.5,[229,225,437,440,227,225,227,227,225],0.42,[232,225,437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translation error (RMSE, m), all systems in real-time mode on the desktop PC; 'x' = system totally failed. Ablation columns (Ours VIO, LIO, w\u002Fo F2M, Marg. F2M, w\u002Fo calib.) omitted here. MARS-LVIG ground truth re-derived by the authors with post-processed RTK\u002FINS; i2Nav-Robot ground-truth source not described in this paper. Average rows printed by the authors.",{"slug":492,"group":493,"sourceId":5,"sourceLabel":6,"table":494,"selfRows":311,"metrics":495,"seqs":498,"entrants":513,"cells":520,"outcomes":595,"locators":596,"hardware":597,"wordings":598,"notes":599},"palvio2026-table-iii","palvio2026:Table III","Table III",[496],{"label":497,"unit":160,"statistic":67,"alignment":44},"end-to-end error (meters)",[499,502,504,506,508,510,512],{"dataset":130,"sequence":500,"environment":501},"main-building","handheld campus, park and building sequences (Hong Kong)",{"dataset":130,"sequence":503,"environment":501},"campus00",{"dataset":130,"sequence":505,"environment":501},"campus01",{"dataset":130,"sequence":507,"environment":501},"campus02",{"dataset":130,"sequence":509,"environment":501},"park0",{"dataset":130,"sequence":511,"environment":501},"park1",{"dataset":130,"sequence":185,"environment":501},[514,515,516,517,518,519],{"name":208,"methodId":68,"linkable":74,"proposed":74,"self":74},{"name":210,"methodId":211,"linkable":212,"proposed":74,"self":74},{"name":214,"methodId":68,"linkable":74,"proposed":74,"self":74},{"name":216,"methodId":217,"linkable":212,"proposed":74,"self":74},{"name":219,"methodId":220,"linkable":212,"proposed":74,"self":74},{"name":222,"methodId":5,"linkable":212,"proposed":212,"self":212},[521,523,525,526,527,529,531,533,535,537,539,541,543,545,547,549,551,553,554,556,557,559,560,562,563,564,566,568,569,571,573,575,577,579,581,582,584,585,587,589,591,593],[225,225,225,522,227,225,227,227,225],1.2,[229,225,225,524,227,225,227,227,225],1.38,[232,225,225,351,227,225,227,227,225],[235,225,225,275,227,225,227,227,225],[237,225,225,528,227,225,227,227,225],1.27,[150,225,225,530,227,225,227,227,225],0.08,[225,225,229,532,227,225,227,227,225],2.41,[229,225,229,534,227,225,227,227,225],5.29,[232,225,229,536,227,225,227,227,225],11.09,[235,225,229,538,227,225,227,227,225],5.2,[237,225,229,540,227,225,227,227,225],5.96,[150,225,229,542,227,225,227,227,225],0.1,[225,225,232,544,227,225,227,227,225],2.51,[229,225,232,546,227,225,227,227,225],2.19,[232,225,232,548,227,225,227,227,225],2.78,[235,225,232,550,227,225,227,227,225],19.57,[237,225,232,552,227,225,227,227,225],5.68,[150,225,232,293,227,225,227,227,225],[225,225,235,555,227,225,227,227,225],4.03,[229,225,235,530,227,225,227,227,225],[232,225,235,558,227,225,227,227,225],2.38,[235,225,235,530,227,225,227,227,225],[237,225,235,561,227,225,227,227,225],0.01,[150,225,235,295,227,225,227,227,225],[225,225,237,263,227,225,227,227,225],[229,225,237,565,227,225,227,227,225],0.06,[232,225,237,567,227,225,227,227,225],1.26,[235,225,237,530,227,225,227,227,225],[237,225,237,570,227,225,227,227,225],0.04,[150,225,237,572,227,225,227,227,225],0.13,[225,225,150,574,227,225,227,227,225],1.45,[229,225,150,576,227,225,227,227,225],0.56,[232,225,150,578,227,225,227,227,225],0.89,[235,225,150,580,227,225,227,227,225],0.6,[237,225,150,358,227,225,227,227,225],[150,225,150,583,227,225,227,227,225],0.58,[225,225,298,422,227,225,227,227,225],[229,225,298,586,227,225,227,227,225],1.59,[232,225,298,588,227,225,227,227,225],3.23,[235,225,298,590,227,225,227,227,225],4.27,[237,225,298,592,227,225,227,227,225],2.25,[150,225,298,594,227,225,227,227,225],0.29,[],[494],[],[],[600],"End-to-end errors (m) on the public R3LIVE handheld dataset, computed by subtracting start positions from end positions; ablation columns omitted; Average row printed by the authors",{"slug":602,"group":603,"sourceId":5,"sourceLabel":6,"table":604,"selfRows":298,"metrics":605,"seqs":620,"entrants":623,"cells":625,"outcomes":638,"locators":639,"hardware":640,"wordings":643,"notes":644},"palvio2026-table-vi","palvio2026:Table VI","Table VI",[606,610,612,614,616,618],{"label":607,"unit":608,"statistic":609,"alignment":44},"LiDAR (ms) on PC","ms","mean",{"label":611,"unit":608,"statistic":609,"alignment":44},"LiDAR (ms) on ARM",{"label":613,"unit":608,"statistic":609,"alignment":44},"Visual (ms) on PC",{"label":615,"unit":608,"statistic":609,"alignment":44},"Visual (ms) on ARM",{"label":617,"unit":608,"statistic":609,"alignment":44},"FGO (ms) on PC",{"label":619,"unit":608,"statistic":609,"alignment":44},"FGO (ms) on ARM",[621],{"dataset":83,"sequence":185,"environment":622},"handheld, Wuhan University buildings",[624],{"name":7,"methodId":5,"linkable":212,"proposed":212,"self":212},[626,628,630,632,634,636],[225,225,225,627,227,225,225,227,225],3.27,[225,229,225,629,227,225,229,227,225],20.4,[225,232,225,631,227,225,225,227,225],7.07,[225,235,225,633,227,225,229,227,225],28.91,[225,237,225,635,227,225,225,227,225],39.06,[225,150,225,637,227,225,229,227,225],85.35,[],[604],[641,642],"desktop PC, AMD Ryzen 9 9950X CPU + NVIDIA RTX 5090 GPU (Sec. IV-A; Sec. IV-D names RTX 5080)","NVIDIA Orin NX (6-core CPU, 8 GB RAM), images resized from 1800x1200 to 900x600",[],[645],"Average processing time on the three private HandNav sequences (whu-building, whu-gateway, whu-library); LiDAR and visual include preprocessing and data association",{"slug":647,"group":648,"sourceId":5,"sourceLabel":6,"table":649,"selfRows":235,"metrics":650,"seqs":657,"entrants":660,"cells":665,"outcomes":672,"locators":673,"hardware":674,"wordings":675,"notes":676},"palvio2026-table-iv","palvio2026:Table IV","Table IV",[651,653,655],{"label":652,"unit":608,"statistic":609,"alignment":44},"FGO (ms), tightly coupled F2M variant",{"label":654,"unit":608,"statistic":609,"alignment":44},"FGO (ms), loosely coupled F2M (PA-LVIO)",{"label":656,"unit":608,"statistic":609,"alignment":44},"F2M optimization (ms)",[658],{"dataset":103,"sequence":185,"environment":659},"wheeled robot",[661,663],{"name":662,"methodId":5,"linkable":212,"proposed":212,"self":212},"PA-LVIO with tightly coupled F2M factor",{"name":664,"methodId":5,"linkable":212,"proposed":212,"self":212},"Loosely coupled (PA-LVIO)",[666,668,670],[225,225,225,667,227,225,225,227,225],33.95,[229,229,225,669,227,225,225,227,225],26.96,[229,232,225,671,227,225,225,227,225],3.19,[],[649],[641],[],[677],"Average over the 10 i2Nav-Robot sequences of per-keyframe FGO time for tightly vs loosely coupled F2M factors, and of the F2M pose optimization",[679],{"group":680,"slug":681,"sourceLabel":6,"table":682,"selfRows":232,"datasets":683},"palvio2026:Table VII","palvio2026-table-vii","Table VII",[83],1790510659347]