[{"data":1,"prerenderedAt":715},["ShallowReactive",2],{"method-pointlio2023":3},{"method":4,"reference":58,"equipment":82,"figures":151,"results":152},{"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":33,"platform":36,"estimator":43,"association":44,"timeModel":45,"deskew":46,"loopClosure":47,"globalOptimization":47,"mapRepresentation":48,"prior":49,"outputGeometry":50,"compute":51,"codeUrl":52,"codeLicense":53,"relatedVersions":54},"pointlio2023","He et al., 2023a","Point-LIO","Point‐LIO: Robust High‐Bandwidth Light Detection and Ranging Inertial Odometry",2023,"recent","C05","odometry_with_local_mapping","Point-LIO 在每一個 LiDAR 點或 IMU 取樣到達時，就以不迭代的流形擴展卡爾曼濾波（on-manifold EKF）進行傳播與更新，里程計輸出可達 4 至 8 kHz，並從架構上避免掃描內的運動畸變。作者把角速度與線加速度擴增為一階積分隨機過程狀態，將 IMU 量測視為系統輸出，飽和的 IMU 通道直接略過，因此旋轉超出 IMU 量程時仍能估計位姿。每個點以 ikd-Tree 中五個最近鄰擬合平面，計算一維點到平面殘差。","Point-LIO fuses every LiDAR point and IMU sample at its own timestamp in a non-iterated on-manifold EKF, treating IMU readings as outputs of a stochastic-process-augmented kinematic model so saturated channels can be skipped; one-dimensional point-to-plane residuals against an ikd-Tree map give 4 to 8 kHz odometry without frame-level deskewing.","full_text_reviewed","peer_reviewed_published","background","未在施工現場或施工資料集上測試；實驗涵蓋機器人車於公園、廣場與走廊、實驗室旋轉台與擺錘、無人機，以及 utbm、ulhk、liosam、lili 公開序列，只提供軌跡、漂移與執行時間層級的證據，未評估點雲幾何精度。",[20,21],"controlled_experiment","public_benchmark",[23,24,25,26],"4 to 8 kHz odometry (6955 Hz average on Odo) and bandwidth above 150 Hz versus 100 Hz for FAST-LIO2 (Table 2)","Survives IMU saturation after the initial stage: Satu-1 rotation and translation RMSE 4.60 deg and 0.233 m, where FAST-LIO2 and Point-LIO-input diverge once the IMU saturates; Satu-2 4.42 deg and 0.0990 m, with consistently lower errors than FAST-LIO2 and Point-LIO-input (Sec. 5.5)","Park drift 0.080 m versus 1.242 m for FAST-LIO2 (Table 1)","Best RMSE on 4 of 5 and lowest drift on 5 of 7 public sequences with one parameter set (Tables 5 and 6)",[28,29,30,31,32],"README requires LiDAR-IMU synchronization and per-point timestamps, and IMU saturation values must be configured (README, Important notes A-C)","Fails when the motion already exceeds the IMU range at start (37.68 rad\u002Fs start failed, Table 3); rotation RMSE grows with initial angular velocity","Worse than FAST-LIO2 and LILI-OM on the long lili_8 sequence, attributed to untuned shared parameters (Sec. 6.1.2)","Point-wise processing is emulated after sorting packaged scans because drivers do not stream single points (Sec. 5.1)","Odometry only: no loop closure or global map optimization module is described; Sec. 8 presents Point-LIO as an odometry (inference from system scope)",[34,35],"Livox Avia solid-state LiDAR with built-in BMI088 IMU (own experiments)","public benchmarks with Livox Horizon, Velodyne HDL-32E and VLP-16 plus their IMUs",[37,38,39,40,41,42],"robot car (DJI RoboMaster 2019 AI)","step-motor rotating platform","pendulum","racing quadrotor UAV","self-rotating UAV","public vehicle and campus sequences (utbm, ulhk, liosam, lili)","Tightly coupled on-manifold EKF (IKFoM toolbox), deliberately not iterated; 24-dimensional state on SO(3) x R21 with attitude, position, velocity, gyroscope and accelerometer biases, gravity, and angular velocity and linear acceleration modelled as first-order integrator processes; each LiDAR point or IMU sample is propagated and fused at its own timestamp, and saturated IMU channels are skipped","Each point is projected with the propagated pose; its five nearest map points within 5 m in the ikd-Tree are fitted to a plane; if any neighbour lies more than 0.1 m from the plane the point is added to the map without an update, otherwise a one-dimensional point-to-plane residual updates the state","Discrete-time state propagated and updated at every measurement time (each LiDAR point or IMU sample), with the kinematic model discretized over each inter-measurement interval; because drivers deliver packaged scans, points and IMU samples in a package are sorted by timestamp and processed one by one","No explicit deskewing step: each point is fused at its own sampling time so frame-level motion distortion does not arise; wall-thickness views show thinner walls than FAST-LIO2 (qualitative, Figs. 5 to 7)","not_reported","ikd-Tree incremental k-d tree point map from FAST-LIO2; local map size 2000 m, spatial downsampling 0.25 m, rebalancing thresholds 0.6 and 0.5, parallel rebuild threshold 1500 points; public benchmarks use FAST-LIO2 default mapping parameters with 1:4 temporal downsampling of raw points","none","Point map accumulated in the ikd-Tree (points inserted at the updated pose) and odometry at 4 to 8 kHz; no export format or map accuracy evaluation reported","DJI Manifold 2-C7 (Intel i7-8550U 1.8 GHz, 8 GB RAM): average 20.27 ms per scan on 12 public sequences versus 20.19 ms for FAST-LIO2 (Table 7), about 9 microseconds per point; nearest-neighbour search is sequential while FAST-LIO2 uses four threads; Khadas VIM3 Pro ARM board (Cortex-A73 2.2 GHz, 4 GB) onboard the self-rotating UAV: 14.63 ms per 50 Hz package","https:\u002F\u002Fgithub.com\u002Fhku-mars\u002FPoint-LIO","LICENSE file contains LOAM\u002FLivox-derived BSD-3-Clause-style text (checked); overall project licensing not further verified",[55],{"relation":56,"title":57,"doi_or_url":52},"code_release","hku-mars\u002FPoint-LIO",{"id":5,"kind":59,"shortName":7,"title":8,"authors":60,"year":9,"venue":67,"venueType":68,"publisher":69,"volumeIssuePages":70,"doi":71,"arxivId":72,"url":73,"firstPublicDate":74,"publicationStatus":16,"metadataStatus":75,"fulltextStatus":15,"era":10,"classicReason":76,"codeUrl":52,"cluster":11,"topics":77,"mdpi":78,"verification":79,"label":6,"fulltextRoute":80,"versionRead":81,"addedByCensus":78},"method",[61,62,63,64,65,66],"Dongjiao He","Wei Xu","Nan Chen","Fanze Kong","Chongjian Yuan","Fu Zhang","Advanced Intelligent Systems","journal","Wiley","5(7):2200459","10.1002\u002Faisy.202200459",null,"https:\u002F\u002Fapi.crossref.org\u002Fworks\u002F10.1002\u002Faisy.202200459","2023-04-07","metadata_verified","not_applicable",[11],false,"confirmed","NTU institutional (Chrome)","Version of record, Advanced Intelligent Systems 5(7):2200459 (Wiley HTML, open access)",[83,89,94,101,107,112,116,120,124,128,134,138,143,147],{"category":84,"model":85,"canonical":85,"role":86,"dataset":72,"specs":87,"locator":88},"lidar","Livox Avia","method input","solid-state, 70.4 deg x 77.2 deg circular FoV, non-repetitive scanning, 230,000 points\u002Fs, packages at 10 to 100 Hz","Sec. 5.2; Fig. 3a",{"category":90,"model":91,"canonical":91,"role":86,"dataset":72,"specs":92,"locator":93},"imu","BMI088 (built-in IMU of Livox Avia)","200 Hz; measuring range 35 rad\u002Fs and about 30 m\u002Fs2 (Sec. 5.5.1); range set to 17.5 rad\u002Fs on the self-rotating UAV (Sec. 7.2)","Sec. 5.2; Sec. 5.5.1; Sec. 7.2",{"category":95,"model":96,"canonical":96,"role":97,"dataset":98,"specs":99,"locator":100},"camera","first-person-view (FPV) camera","dataset sensor","Point-LIO own sequences (Livox Avia sensor suite)","aligned with LiDAR FoV; used only for visual illustration","Sec. 5.2; Fig. 3a; Sec. 7.1",{"category":102,"model":103,"canonical":103,"role":104,"dataset":98,"specs":105,"locator":106},"other","Vicon Tracker (five Vicon markers on the sensor suite)","reference or ground truth","recorded at 300 Hz for bandwidth analysis","Sec. 5.2; Sec. 5.4",{"category":108,"model":109,"canonical":109,"role":86,"dataset":98,"specs":110,"locator":111},"platform","RoboMaster 2019 AI robot car","sensor suite mounted on chassis without vibration absorber","Sec. 5.2; Sec. 5.3; Fig. 3b",{"category":108,"model":113,"canonical":113,"role":86,"dataset":98,"specs":114,"locator":115},"rotating platform driven by Nimotion STM4260A step motor","peak yaw rate 75 rad\u002Fs in Satu-1","Sec. 5.2; Sec. 5.5.1; Fig. 3c",{"category":108,"model":117,"canonical":117,"role":86,"dataset":98,"specs":118,"locator":119},"pendulum (sensor suite on a rope)","circling motion, acceleration up to 40 m\u002Fs2","Sec. 5.2; Sec. 5.5.2; Fig. 3d",{"category":108,"model":121,"canonical":121,"role":86,"dataset":72,"specs":122,"locator":123},"racing quadrotor drone","thrust-to-weight ratio up to 5.4; carries Livox Avia and FPV camera; angular velocity up to 59.37 rad\u002Fs","Sec. 7.1; Fig. 18a",{"category":108,"model":125,"canonical":125,"role":86,"dataset":72,"specs":126,"locator":127},"self-rotating single-actuated UAV","average yaw rate about 25 rad\u002Fs; Livox Avia facing front","Sec. 7.2; Fig. 18b",{"category":129,"model":130,"canonical":130,"role":131,"dataset":72,"specs":132,"locator":133},"compute","DJI Manifold 2-C7","compute for runtime","1.8 GHz quad-core Intel i7-8550U, 8 GB RAM","Sec. 5.6; Sec. 6",{"category":129,"model":135,"canonical":135,"role":131,"dataset":72,"specs":136,"locator":137},"Khadas VIM3 Pro","2.2 GHz quad-core Cortex-A73, 4 GB RAM; runs Point-LIO onboard in real time","Sec. 7.2",{"category":84,"model":139,"canonical":139,"role":97,"dataset":140,"specs":141,"locator":142},"Livox Horizon","lili (LILI-OM dataset)","solid-state 3D LiDAR","Sec. 6",{"category":84,"model":144,"canonical":144,"role":97,"dataset":145,"specs":146,"locator":142},"Velodyne HDL-32E","utbm and ulhk","spinning LiDAR",{"category":84,"model":148,"canonical":149,"role":97,"dataset":150,"specs":146,"locator":142},"VLP-16","Velodyne VLP-16","liosam (LIO-SAM dataset)",[],{"totalRows":153,"groupCount":154,"groups":155,"others":669},61,12,[156,456,502,597],{"slug":157,"group":158,"sourceId":159,"sourceLabel":160,"table":161,"selfRows":162,"metrics":163,"seqs":168,"entrants":215,"cells":240,"outcomes":448,"locators":450,"hardware":452,"wordings":453,"notes":454},"voxelslam2026-table-2-odometry-without-lc","voxelslam2026:Table 2 (odometry without LC)","voxelslam2026","Liu et al., 2026","Table 2 (odometry without LC)",13,[164],{"label":165,"unit":166,"statistic":167,"alignment":47},"absolute trajectory error (RMSE, centimeters)","cm","RMSE",[169,173,176,179,183,187,191,195,199,203,206,209,212],{"dataset":170,"sequence":171,"environment":172},"Hilti handheld sequence exp01-construction (name per Table C1)","hilti01","construction environment (sequence named construction)",{"dataset":174,"sequence":175,"environment":172},"Hilti handheld sequence exp02-construction (name per Table C1)","hilti02",{"dataset":177,"sequence":178,"environment":172},"Hilti handheld sequence exp03-construction (name per Table C1)","hilti03",{"dataset":180,"sequence":181,"environment":182},"Hilti handheld sequence exp07-long-corridor (name per Table C1)","hilti04","long corridor",{"dataset":184,"sequence":185,"environment":186},"Hilti handheld sequence exp09-cupola (name per Table C1)","hilti05","cupola",{"dataset":188,"sequence":189,"environment":190},"Hilti handheld sequence exp11-lower-gallery (name per Table C1)","hilti06","lower gallery",{"dataset":192,"sequence":193,"environment":194},"Hilti handheld sequence exp15-upper-gallery (name per Table C1)","hilti07","upper gallery",{"dataset":196,"sequence":197,"environment":198},"Hilti handheld sequence exp21-outside (name per Table C1)","hilti08","outside",{"dataset":200,"sequence":201,"environment":202},"Hilti handheld sequence site1-handheld-1 (name per Table C1)","hilti09","construction site (same site for hilti09 to hilti13, Sec. 10.3.1)",{"dataset":204,"sequence":205,"environment":202},"Hilti handheld sequence site1-handheld-2 (name per Table C1)","hilti10",{"dataset":207,"sequence":208,"environment":202},"Hilti handheld sequence site1-handheld-3 (name per Table C1)","hilti11",{"dataset":210,"sequence":211,"environment":202},"Hilti handheld sequence site1-handheld-4 (name per Table C1)","hilti12",{"dataset":213,"sequence":214,"environment":202},"Hilti handheld sequence site1-handheld-5 (name per Table C1)","hilti13",[216,220,223,226,229,232,235,236,238],{"name":217,"methodId":218,"linkable":219,"proposed":78,"self":78},"LeGO-LOAM","legoloam2018",true,{"name":221,"methodId":222,"linkable":219,"proposed":78,"self":78},"LiLi-OM","liliom2021",{"name":224,"methodId":225,"linkable":219,"proposed":78,"self":78},"LINS","lins2020",{"name":227,"methodId":228,"linkable":219,"proposed":78,"self":78},"LIO-SAM","liosam2020",{"name":230,"methodId":231,"linkable":219,"proposed":78,"self":78},"FAST-LIO2","fastlio2_2022",{"name":233,"methodId":234,"linkable":219,"proposed":78,"self":78},"Faster-LIO","fasterlio2022",{"name":7,"methodId":5,"linkable":219,"proposed":78,"self":219},{"name":237,"methodId":159,"linkable":219,"proposed":219,"self":78},"Our (Odom)",{"name":239,"methodId":159,"linkable":219,"proposed":219,"self":78},"Our (Odom+LM)",[241,245,248,250,253,255,258,260,262,265,268,271,274,275,277,279,280,282,283,285,286,288,290,292,294,295,296,298,300,301,303,304,306,307,309,311,313,315,317,318,320,322,323,325,326,328,329,331,333,335,337,339,341,343,345,347,349,351,353,355,357,358,360,362,364,366,368,370,372,373,375,377,378,379,381,383,385,387,389,390,391,393,395,397,399,401,402,404,406,408,409,411,412,414,416,417,418,419,421,422,423,424,425,426,428,430,431,432,434,436,437,439,441,443,444,445,446],[242,242,242,243,244,242,244,244,242],0,9.1,-1,[242,242,246,247,244,242,244,244,242],1,47,[242,242,249,72,242,242,244,244,242],2,[242,242,251,252,244,242,244,244,242],3,25.3,[242,242,254,72,242,242,244,244,242],4,[242,242,256,257,244,242,244,244,242],5,67,[242,242,259,72,242,242,244,244,242],6,[242,242,261,252,244,242,244,244,242],7,[242,242,263,264,244,242,244,244,242],8,12.7,[242,242,266,267,244,242,244,244,242],9,14.3,[242,242,269,270,244,242,244,244,242],10,27.1,[242,242,272,273,244,242,244,244,242],11,19.7,[242,242,154,72,242,242,244,244,242],[246,242,242,276,244,242,244,244,242],6.2,[246,242,246,278,244,242,244,244,242],22.2,[246,242,249,72,242,242,244,244,242],[246,242,251,281,244,242,244,244,242],31,[246,242,254,72,242,242,244,244,242],[246,242,256,284,244,242,244,244,242],28.9,[246,242,259,72,242,242,244,244,242],[246,242,261,287,244,242,244,244,242],20.3,[246,242,263,289,244,242,244,244,242],6.9,[246,242,266,291,244,242,244,244,242],8.5,[246,242,269,293,244,242,244,244,242],19.9,[246,242,272,252,244,242,244,244,242],[246,242,154,72,242,242,244,244,242],[249,242,242,297,244,242,244,244,242],6.5,[249,242,246,299,244,242,244,244,242],18.8,[249,242,249,72,242,242,244,244,242],[249,242,251,302,244,242,244,244,242],20.7,[249,242,254,72,242,242,244,244,242],[249,242,256,305,244,242,244,244,242],23.1,[249,242,259,72,242,242,244,244,242],[249,242,261,308,244,242,244,244,242],17.8,[249,242,263,310,244,242,244,244,242],7.5,[249,242,266,312,244,242,244,244,242],9.9,[249,242,269,314,244,242,244,244,242],28.1,[249,242,272,316,244,242,244,244,242],20,[249,242,154,72,242,242,244,244,242],[251,242,242,319,244,242,244,244,242],7.4,[251,242,246,321,244,242,244,244,242],15.2,[251,242,249,72,242,242,244,244,242],[251,242,251,324,244,242,244,244,242],23.4,[251,242,254,72,242,242,244,244,242],[251,242,256,327,244,242,244,244,242],17.4,[251,242,259,72,242,242,244,244,242],[251,242,261,330,244,242,244,244,242],22.4,[251,242,263,332,244,242,244,244,242],6.6,[251,242,266,334,244,242,244,244,242],6.8,[251,242,269,336,244,242,244,244,242],17.6,[251,242,272,338,244,242,244,244,242],16.8,[251,242,154,340,244,242,244,244,242],74,[254,242,242,342,244,242,244,244,242],1.3,[254,242,246,344,244,242,244,244,242],2.8,[254,242,249,346,244,242,244,244,242],32,[254,242,251,348,244,242,244,244,242],6.7,[254,242,254,350,244,242,244,244,242],55,[254,242,256,352,244,242,244,244,242],2.4,[254,242,259,354,244,242,244,244,242],72,[254,242,261,356,244,242,244,244,242],1.7,[254,242,263,352,244,242,244,244,242],[254,242,266,359,244,242,244,244,242],1.8,[254,242,269,361,244,242,244,244,242],4.2,[254,242,272,363,244,242,244,244,242],3.5,[254,242,154,365,244,242,244,244,242],16,[256,242,242,367,244,242,244,244,242],1.1,[256,242,246,369,244,242,244,244,242],2.1,[256,242,249,371,244,242,244,244,242],37,[256,242,251,256,244,242,244,244,242],[256,242,254,374,244,242,244,244,242],73,[256,242,256,376,244,242,244,244,242],1.4,[256,242,259,153,244,242,244,244,242],[256,242,261,352,244,242,244,244,242],[256,242,263,380,244,242,244,244,242],1.9,[256,242,266,382,244,242,244,244,242],2.3,[256,242,269,384,244,242,244,244,242],2.7,[256,242,272,386,244,242,244,244,242],2.6,[256,242,154,388,244,242,244,244,242],11.4,[259,242,242,367,244,242,244,244,242],[259,242,246,251,244,242,244,244,242],[259,242,249,392,244,242,244,244,242],23,[259,242,251,394,244,242,244,244,242],3.7,[259,242,254,396,244,242,244,244,242],44,[259,242,256,398,244,242,244,244,242],0.9,[259,242,259,400,244,242,244,244,242],45,[259,242,261,386,244,242,244,244,242],[259,242,263,403,244,242,244,244,242],3.2,[259,242,266,405,244,242,244,244,242],1.6,[259,242,269,407,244,242,244,244,242],3.6,[259,242,272,254,244,242,244,244,242],[259,242,154,410,244,242,244,244,242],9.2,[261,242,242,342,244,242,244,244,242],[261,242,246,413,244,242,244,244,242],2.5,[261,242,249,415,244,242,244,244,242],9.3,[261,242,251,361,244,242,244,244,242],[261,242,254,392,244,242,244,244,242],[261,242,256,405,244,242,244,244,242],[261,242,259,420,244,242,244,244,242],15.7,[261,242,261,359,244,242,244,244,242],[261,242,263,405,244,242,244,244,242],[261,242,266,249,244,242,244,244,242],[261,242,269,344,244,242,244,244,242],[261,242,272,352,244,242,244,244,242],[261,242,154,427,244,242,244,244,242],4.3,[263,242,242,429,244,242,244,244,242],0.8,[263,242,246,359,244,242,244,244,242],[263,242,249,251,244,242,244,244,242],[263,242,251,433,244,242,244,244,242],3.4,[263,242,254,435,244,242,244,244,242],15.9,[263,242,256,398,244,242,244,244,242],[263,242,259,438,244,242,244,244,242],9.8,[263,242,261,440,244,242,244,244,242],1.2,[263,242,263,442,244,242,244,244,242],1.25,[263,242,266,376,244,242,244,244,242],[263,242,269,352,244,242,244,244,242],[263,242,272,376,244,242,244,244,242],[263,242,154,447,244,242,244,244,242],1.26,[449],"failed (dash; text states LeGO-LOAM, LiLi-OM, LINS and LIO-SAM failed in these sequences)",[451],"Table 2",[],[],[455],"Hilti handheld sequences (Hesai XT-32, BMI085 400 Hz); ATE exported from the Hilti evaluation website; odometry without loop closure; all methods with default parameters",{"slug":457,"group":458,"sourceId":5,"sourceLabel":6,"table":459,"selfRows":261,"metrics":460,"seqs":464,"entrants":480,"cells":482,"outcomes":495,"locators":497,"hardware":498,"wordings":499,"notes":500},"pointlio2023-table-3","pointlio2023:Table 3","Table 3",[461],{"label":462,"unit":463,"statistic":167,"alignment":47},"RMSE of rotation (deg)","deg",[465,468,470,472,474,476,478],{"dataset":98,"sequence":466,"environment":467},"start 6.28 rad\u002Fs","cluttered laboratory, rotating platform",{"dataset":98,"sequence":469,"environment":467},"start 12.56 rad\u002Fs",{"dataset":98,"sequence":471,"environment":467},"start 18.84 rad\u002Fs",{"dataset":98,"sequence":473,"environment":467},"start 25.12 rad\u002Fs",{"dataset":98,"sequence":475,"environment":467},"start 31.40 rad\u002Fs",{"dataset":98,"sequence":477,"environment":467},"start 34.85 rad\u002Fs",{"dataset":98,"sequence":479,"environment":467},"start 37.68 rad\u002Fs",[481],{"name":7,"methodId":5,"linkable":219,"proposed":219,"self":219},[483,484,486,488,490,492,494],[242,242,242,289,244,242,244,244,242],[242,242,246,485,244,242,244,244,242],9.7,[242,242,249,487,244,242,244,244,242],13.6,[242,242,251,489,244,242,244,244,242],14.6,[242,242,254,491,244,242,244,244,242],16.4,[242,242,256,493,244,242,244,244,242],18.7,[242,242,259,72,242,242,244,244,242],[496],"failed",[459],[],[],[501],"Spinning experiment started at different initial yaw rates; IMU range 35 rad\u002Fs; rotation RMSE against Vicon",{"slug":503,"group":504,"sourceId":5,"sourceLabel":6,"table":505,"selfRows":261,"metrics":506,"seqs":510,"entrants":529,"cells":536,"outcomes":589,"locators":592,"hardware":593,"wordings":594,"notes":595},"pointlio2023-table-6","pointlio2023:Table 6","Table 6",[507],{"label":508,"unit":509,"statistic":47,"alignment":49},"drift (meters)","m",[511,515,517,519,522,524,527],{"dataset":512,"sequence":513,"environment":514},"lili","lili_6","public LiDAR datasets (mixed urban and campus)",{"dataset":512,"sequence":516,"environment":514},"lili_7",{"dataset":512,"sequence":518,"environment":514},"lili_8",{"dataset":520,"sequence":521,"environment":514},"ulhk","ulhk_5",{"dataset":520,"sequence":523,"environment":514},"ulhk_6",{"dataset":525,"sequence":526,"environment":514},"liosam","liosam_2",{"dataset":525,"sequence":528,"environment":514},"liosam_3",[530,531,532,534,535],{"name":7,"methodId":5,"linkable":219,"proposed":219,"self":219},{"name":230,"methodId":231,"linkable":219,"proposed":78,"self":78},{"name":533,"methodId":222,"linkable":219,"proposed":78,"self":78},"LILI-OM",{"name":227,"methodId":228,"linkable":219,"proposed":78,"self":78},{"name":224,"methodId":225,"linkable":219,"proposed":78,"self":78},[537,538,539,541,542,543,544,546,547,549,551,553,554,555,557,558,560,562,564,566,568,570,571,572,573,575,577,578,580,581,582,583,584,586,587],[242,242,242,72,242,242,244,244,242],[242,242,246,72,242,242,244,244,242],[242,242,249,540,244,242,244,244,242],28.64,[242,242,251,72,242,242,244,244,242],[242,242,254,382,244,242,244,244,242],[242,242,256,72,242,242,244,244,242],[242,242,259,545,244,242,244,244,242],7.85,[246,242,242,72,242,242,244,244,242],[246,242,246,548,244,242,244,244,242],1.63,[246,242,249,550,244,242,244,244,242],17.39,[246,242,251,552,244,242,244,244,242],0.39,[246,242,254,72,242,242,244,244,242],[246,242,256,72,242,242,244,244,242],[246,242,259,556,244,242,244,244,242],9.5,[249,242,242,429,244,242,244,244,242],[249,242,246,559,244,242,244,244,242],4.13,[249,242,249,561,244,242,244,244,242],15.6,[249,242,251,563,244,242,244,244,242],1.84,[249,242,254,565,244,242,244,244,242],7.89,[249,242,256,567,244,242,244,244,242],1.95,[249,242,259,569,244,242,244,244,242],13.79,[251,242,242,72,246,242,244,244,242],[251,242,246,72,246,242,244,244,242],[251,242,249,72,246,242,244,244,242],[251,242,251,574,244,242,244,244,242],0.83,[251,242,254,576,244,242,244,244,242],2.88,[251,242,256,72,246,242,244,244,242],[251,242,259,579,244,242,244,244,242],8.61,[254,242,242,72,246,242,244,244,242],[254,242,246,72,246,242,244,244,242],[254,242,249,72,246,242,244,244,242],[254,242,251,398,244,242,244,244,242],[254,242,254,585,244,242,244,244,242],6.92,[254,242,256,72,246,242,244,244,242],[254,242,259,588,244,242,244,244,242],29.9,[590,591],"reported as \u003C0.1","not reported in table (dash)",[505],[],[],[596],"End-to-end drift (m) on sequences that start and end at the same place; lili uses Livox Horizon, ulhk Velodyne HDL-32E, liosam VLP-16; LILI-OM tuned per lili sequence",{"slug":598,"group":599,"sourceId":5,"sourceLabel":6,"table":600,"selfRows":259,"metrics":601,"seqs":606,"entrants":624,"cells":629,"outcomes":661,"locators":663,"hardware":664,"wordings":666,"notes":667},"pointlio2023-table-4","pointlio2023:Table 4","Table 4",[602],{"label":603,"unit":604,"statistic":605,"alignment":76},"time consumption per scan (ms)","ms","mean",[607,610,613,616,619,621],{"dataset":98,"sequence":608,"environment":609},"Park","outdoor park",{"dataset":98,"sequence":611,"environment":612},"Square","campus square",{"dataset":98,"sequence":614,"environment":615},"Corridor","indoor corridor",{"dataset":98,"sequence":617,"environment":618},"Odo","laboratory rotating platform",{"dataset":98,"sequence":620,"environment":618},"Satu-1",{"dataset":98,"sequence":622,"environment":623},"Satu-2","laboratory pendulum",[625,626,628],{"name":230,"methodId":231,"linkable":219,"proposed":78,"self":78},{"name":627,"methodId":72,"linkable":78,"proposed":78,"self":78},"Point-LIO-input 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GHz quad-core, 8 GB RAM)",[],[668],"Average total time per scan (ms); Park, Square, Corridor at 10 Hz, Odo, Satu-1, Satu-2 at 100 Hz; dash marks sequences where the LIO fails",[670,676,681,687,692,700,705,709],{"group":671,"slug":672,"sourceLabel":673,"table":459,"selfRows":256,"datasets":674},"feng2025_construction_lidar_eval:Table 3","feng2025-construction-lidar-eval-table-3","Feng et al., 2025",[675],"Feng et al. simulated construction-site dataset (Gazebo)",{"group":677,"slug":678,"sourceLabel":673,"table":600,"selfRows":256,"datasets":679},"feng2025_construction_lidar_eval:Table 4","feng2025-construction-lidar-eval-table-4",[680],"Feng et al. real construction-site dataset (Xi'an hospital)",{"group":682,"slug":683,"sourceLabel":6,"table":684,"selfRows":256,"datasets":685},"pointlio2023:Table 5","pointlio2023-table-5","Table 5",[525,520,686],"utbm",{"group":688,"slug":689,"sourceLabel":6,"table":690,"selfRows":254,"datasets":691},"pointlio2023:Text Sec. 5.5","pointlio2023-text-sec-5-5","Text Sec. 5.5",[98],{"group":693,"slug":694,"sourceLabel":695,"table":600,"selfRows":251,"datasets":696},"lee2024lidarodom_survey:Table 4","lee2024lidarodom-survey-table-4","Lee et al., 2024b",[697,698,699],"ConSLAM","HeLiPR","NTU VIRAL",{"group":701,"slug":702,"sourceLabel":6,"table":703,"selfRows":251,"datasets":704},"pointlio2023:Table 1","pointlio2023-table-1","Table 1",[98],{"group":706,"slug":707,"sourceLabel":6,"table":451,"selfRows":249,"datasets":708},"pointlio2023:Table 2","pointlio2023-table-2",[98],{"group":710,"slug":711,"sourceLabel":6,"table":712,"selfRows":246,"datasets":713},"pointlio2023:Table 7","pointlio2023-table-7","Table 7",[714],"12 public sequences (utbm, ulhk, liosam, lili)",1790510661913]