[{"data":1,"prerenderedAt":666},["ShallowReactive",2],{"method-glim2024":3},{"method":4,"reference":64,"equipment":86,"figures":164,"results":165},{"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":36,"platform":41,"estimator":44,"association":45,"timeModel":46,"deskew":47,"loopClosure":48,"globalOptimization":49,"mapRepresentation":50,"prior":51,"outputGeometry":52,"compute":53,"codeUrl":54,"codeLicense":55,"relatedVersions":56},"glim2024","Koide et al., 2024","GLIM","GLIM: 3D range-inertial localization and mapping with GPU-accelerated scan matching factors",2024,"recent","C05","full_slam_with_global_correction","GLIM 以 GPU 加速的體素化 GICP 配準誤差因子（matching cost factor）取代傳統的掃描對模型配準與以高斯近似的相對位姿約束。里程計以固定延遲平滑（fixed-lag smoothing）在約數秒視窗內持續修正過去狀態，並以關鍵影格作為配準目標，使短暫幾何退化仍可藉由後續觀測回推修正。全域最佳化直接最小化各子地圖之間的配準誤差並緊耦合 IMU，可約束重疊很小的子地圖，但運算量高而需 GPU。","Range-inertial SLAM that uses GPU-accelerated voxelized-GICP registration-error factors both in fixed-lag-smoothing odometry and in global submap optimization, replacing pose-graph relative constraints.","full_text_reviewed","peer_reviewed_published","main_body","not_reported（驗證包括模擬走廊、面對平坦牆面的實測退化序列、以 FARO Focus 建立參考點雲的室內跨感測器實驗、Newer College 校園與 NTU VIRAL 空拍資料；牆面退化情境與施工中室內場景相似屬推論，論文未於工地驗證）",[20,21,22],"public_benchmark","controlled_experiment","simulation",[24,25,26,27,28],"Tolerates a few seconds of completely degenerate range data where filter-based frame-to-model methods drift or diverge (abstract; Sec. VI-A)","Real flat-wall degeneration test: ATE 0.017 to 0.389 m versus 0.297 to 15.551 m for FAST-LIO2 and 0.146 to 0.950 m for VoxelMap (Table II)","Works with LiDARs and depth or stereo cameras under one parameter set: ATE 0.007 to 0.042 m for LiDARs and ToF camera, 0.139 and 0.206 m for stereo cameras (Sec. VI-B; Table III)","Best ATE on the indoor stairs sequence (0.106 m odometry, 0.046 m with loop closure, versus 1.320 m for FAST-LIO2) (Table V)","Global registration-error minimization constrains submaps with small overlap (Sec. I)",[30,31,32,33,34,35],"Long-term degeneration beyond the bounded optimization window remains challenging (Sec. VII)","Requires much more computation than conventional methods and relies on a GPU for real time (abstract; Sec. I)","Stereo-based cameras (Realsense D455, ZED2i) give distorted maps and larger errors due to distorted input clouds (Sec. VI-B; Table III)","Multi-resolution voxelmap slightly lowers accuracy in small indoor scenes (stairs ATE 0.073 m without it versus 0.106 m with it) (Sec. VI-C; Table VIII)","City- or nation-scale mapping would need a more scalable optimizer (Sec. VII)","In the real degeneration test VoxelMap was better on Seq. 02 (0.146 versus 0.299 m) (Table II)",[37,38,39,40],"3D LiDAR (spinning and non-repetitive)","depth cameras (ToF, active stereo, stereo)","IMU","optional multi-camera",[42,43],"handheld","UAV","fixed-lag smoothing (iSAM2 in GTSAM) with GPU voxelized-GICP matching-cost factors, IMU preintegration and keyframes; global factor graph minimizing registration errors between submaps with IMU constraints","voxelized GICP (distribution-to-distribution) with surface-orientation-based correspondence validation and multi-resolution voxelmaps","discrete poses (authors judge continuous-time unnecessary when IMU prediction cancels distortion, Sec. II)","IMU-based motion prediction transforms points to the IMU frame before covariance estimation (Sec. IV-B)","implicit: global matching-cost factors between overlapping submaps rather than explicit place recognition","global multi-scan registration-error minimization over submaps with tightly coupled IMU constraints and submap endpoints","submaps of points with covariances and GPU voxelmaps","none","globally optimized submap point clouds and trajectory; export format not_reported in sections read","Real time on a single consumer-grade GPU; park-sequence timing on an Intel Core i7 8700K with an NVIDIA 'RTX 1660 Ti' (as written): preprocessing 15.1 ms and odometry 29.3 ms per frame, local-map optimization 295.0 ms per submap (about every 2 s), global optimization 55.8 ms per submap with a maximum of about 250 ms; linear solving on the CPU is about 5% of global optimization time (Sec. VI-C; Table VII)","https:\u002F\u002Fgithub.com\u002Fkoide3\u002Fglim","MIT (LICENSE file checked); authors state the majority of the code is released",[57,61],{"relation":58,"title":59,"doi_or_url":60},"preprint","GLIM (arXiv v1)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2407.10344",{"relation":62,"title":63,"doi_or_url":54},"code_release","koide3\u002Fglim",{"id":5,"kind":65,"shortName":7,"title":8,"authors":66,"year":9,"venue":71,"venueType":72,"publisher":73,"volumeIssuePages":74,"doi":75,"arxivId":76,"url":77,"firstPublicDate":78,"publicationStatus":16,"metadataStatus":79,"fulltextStatus":15,"era":10,"classicReason":80,"codeUrl":54,"cluster":11,"topics":81,"mdpi":82,"verification":83,"label":6,"fulltextRoute":84,"versionRead":85,"addedByCensus":82},"method",[67,68,69,70],"Kenji Koide","Masashi Yokozuka","Shuji Oishi","Atsuhiko Banno","Robotics and Autonomous Systems","journal","Elsevier","179:104750","10.1016\u002Fj.robot.2024.104750","2407.10344","https:\u002F\u002Fapi.crossref.org\u002Fworks\u002F10.1016\u002Fj.robot.2024.104750","2024-07-09","metadata_verified","not_applicable",[11],false,"corrected","arXiv","arXiv v1 (2407.10344v1, submitted 2024-07-14, arXiv comment 'Robotics and Autonomous Systems'); HTML read in full and PDF text used to check table layout; Elsevier version of record not opened",[87,94,98,104,108,110,114,118,123,126,131,137,140,145,148,152,155,161],{"category":88,"model":89,"canonical":89,"role":90,"dataset":91,"specs":92,"locator":93},"lidar","Velodyne VLP-16","method input",null,"LiDAR model used to generate simulated point clouds; observation range limited to 15 m in a 40 m wide environment","Sec. VI-A; Fig. 8",{"category":88,"model":95,"canonical":95,"role":90,"dataset":91,"specs":96,"locator":97},"Livox Avia","non-repetitive scan LiDAR; used for the eight flat-wall degeneration sequences and the cross-sensor test","Sec. VI-A; Sec. VI-B; Table III",{"category":99,"model":100,"canonical":100,"role":101,"dataset":91,"specs":102,"locator":103},"camera","OMRON SENTECH STC-MBS202POE","reference or ground truth","images recorded with LiDAR-IMU data; synchronized via IEEE 1588 PTP; with wall AprilTags gives ground-truth trajectories","Sec. VI-A",{"category":88,"model":105,"canonical":105,"role":90,"dataset":91,"specs":106,"locator":107},"Ouster OS0-32","spinning LiDAR","Sec. VI-B; Table III",{"category":88,"model":109,"canonical":109,"role":90,"dataset":91,"specs":106,"locator":107},"Ouster OS0-64",{"category":111,"model":112,"canonical":112,"role":90,"dataset":91,"specs":113,"locator":107},"rgbd","Microsoft Azure Kinect","time-of-flight depth camera",{"category":88,"model":115,"canonical":116,"role":90,"dataset":91,"specs":117,"locator":107},"Intel Realsense L515","Intel RealSense L515","described as solid-state LiDAR",{"category":119,"model":120,"canonical":121,"role":90,"dataset":91,"specs":122,"locator":107},"stereo_camera","Intel Realsense D455","Intel RealSense D455","active stereo camera",{"category":119,"model":124,"canonical":124,"role":90,"dataset":91,"specs":125,"locator":107},"Stereolabs ZED2i","passive stereo camera",{"category":127,"model":128,"canonical":128,"role":101,"dataset":91,"specs":129,"locator":130},"tls_scanner","FARO Focus","survey-grade LiDAR; environment point cloud to which sensor trajectories are aligned for ground truth","Sec. VI-B",{"category":88,"model":132,"canonical":132,"role":133,"dataset":134,"specs":135,"locator":136},"Ouster OS0-128","dataset sensor","Multi-Camera Newer College","10 Hz point clouds with 100 Hz IMU; PTP-synchronized with cameras","Sec. VI-C",{"category":99,"model":138,"canonical":138,"role":133,"dataset":134,"specs":139,"locator":136},"Sevensense Alphasense Core","4 hardware-synchronized cameras at 30 Hz",{"category":88,"model":141,"canonical":141,"role":133,"dataset":142,"specs":143,"locator":144},"Ouster OS1-16","NTU VIRAL","two units on a UAV","Sec. VI-D",{"category":99,"model":146,"canonical":146,"role":133,"dataset":142,"specs":147,"locator":144},"uEye 1221 LE","two cameras on a UAV",{"category":149,"model":150,"canonical":150,"role":133,"dataset":142,"specs":151,"locator":144},"imu","VectorNav VN100","not_reported",{"category":153,"model":154,"canonical":154,"role":133,"dataset":142,"specs":151,"locator":144},"uwb","Humatic P440",{"category":156,"model":157,"canonical":157,"role":158,"dataset":91,"specs":159,"locator":160},"compute","Intel Core i7 8700K","compute for runtime","CPU used for the processing-time measurement","Sec. VI-C; Table VII",{"category":156,"model":162,"canonical":162,"role":158,"dataset":91,"specs":163,"locator":160},"NVIDIA RTX 1660 Ti","consumer-grade GPU used for the processing-time measurement; model string printed exactly as 'NVIDIA RTX 1660 Ti' in Sec. VI-C",[],{"totalRows":166,"groupCount":167,"groups":168,"others":655},54,6,[169,416,470,591],{"slug":170,"group":171,"sourceId":5,"sourceLabel":6,"table":172,"selfRows":173,"metrics":174,"seqs":180,"entrants":202,"cells":226,"outcomes":410,"locators":411,"hardware":412,"wordings":413,"notes":414},"glim2024-table-v","glim2024:Table V","Table V",20,[175,178],{"label":176,"unit":177,"statistic":151,"alignment":151},"Absolute Trajectory Error [m] (no loop closure)","m",{"label":179,"unit":177,"statistic":151,"alignment":151},"Absolute Trajectory Error [m] (loop closure)",[181,184,186,188,190,192,194,196,198,200],{"dataset":134,"sequence":182,"environment":183},"quad-easy","handheld campus indoor and 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Newer College (Ouster OS0-128, Alphasense Core); translational ATE; unlabeled rows are the no-loop-closure variant of the method printed in the next row (checked against the PDF layout and the ablation baseline in Table VIII)",{"slug":417,"group":418,"sourceId":5,"sourceLabel":6,"table":419,"selfRows":420,"metrics":421,"seqs":426,"entrants":436,"cells":438,"outcomes":464,"locators":465,"hardware":466,"wordings":467,"notes":468},"glim2024-table-iii","glim2024:Table III","Table III",14,[422,424],{"label":423,"unit":177,"statistic":151,"alignment":151},"ATE [m] (± spread omitted)",{"label":425,"unit":177,"statistic":151,"alignment":151},"RTE [m], sub-trajectory 2 m (± spread omitted)",[427,430,431,432,433,434,435],{"dataset":428,"sequence":105,"environment":429},"authors' cross-sensor sequences","indoor experimental environment (Fig. 12)",{"dataset":428,"sequence":109,"environment":429},{"dataset":428,"sequence":95,"environment":429},{"dataset":428,"sequence":112,"environment":429},{"dataset":428,"sequence":115,"environment":429},{"dataset":428,"sequence":120,"environment":429},{"dataset":428,"sequence":124,"environment":429},[437],{"name":7,"methodId":5,"linkable":206,"proposed":206,"self":206},[439,441,442,444,446,448,449,451,452,454,456,458,460,462],[228,228,228,440,230,228,230,230,228],0.037,[228,232,228,440,230,228,230,230,228],[228,228,232,443,230,228,230,230,228],0.022,[228,232,232,445,230,228,230,230,228],0.025,[228,228,235,447,230,228,230,230,228],0.041,[228,232,235,398,230,228,230,230,228],[228,228,238,450,230,228,230,230,228],0.007,[228,232,238,450,230,228,230,230,228],[228,228,241,453,230,228,230,230,228],0.042,[228,232,241,455,230,228,230,230,228],0.045,[228,228,244,457,230,228,230,230,228],0.206,[228,232,244,459,230,228,230,230,228],0.331,[228,228,167,461,230,228,230,230,228],0.139,[228,232,167,463,230,228,230,230,228],0.194,[],[419],[],[],[469],"Cross-sensor test with one parameter set; reference trajectories from alignment to a FARO Focus environment point cloud; RTE sub-trajectory length 2 m; FAST-LIO2 gave no reasonable result except for Ouster and Livox",{"slug":471,"group":472,"sourceId":5,"sourceLabel":6,"table":473,"selfRows":252,"metrics":474,"seqs":477,"entrants":496,"cells":502,"outcomes":561,"locators":562,"hardware":563,"wordings":564,"notes":589},"glim2024-table-ii","glim2024:Table II","Table II",[475],{"label":476,"unit":177,"statistic":151,"alignment":151},"Absolute Trajectory Error [m], reported as value ± (± not defined in the paper)",[478,482,484,486,488,490,492,494],{"dataset":479,"sequence":480,"environment":481},"authors' flat-wall degeneration sequences","Seq. 01","indoor flat wall between pillars (real range degeneration)",{"dataset":479,"sequence":483,"environment":481},"Seq. 02",{"dataset":479,"sequence":485,"environment":481},"Seq. 03",{"dataset":479,"sequence":487,"environment":481},"Seq. 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[61]","yuan2022voxelmap",{"name":7,"methodId":5,"linkable":206,"proposed":206,"self":206},[503,505,507,509,511,513,515,517,519,521,523,526,529,532,535,537,540,543,545,548,551,553,556,559],[228,228,228,504,230,228,230,228,228],0.815,[232,228,228,506,230,228,230,232,228],0.577,[235,228,228,508,230,228,230,235,228],0.118,[228,228,232,510,230,228,230,238,228],0.822,[232,228,232,512,230,228,230,241,228],0.146,[235,228,232,514,230,228,230,244,228],0.299,[228,228,235,516,230,228,230,167,228],0.873,[232,228,235,518,230,228,230,249,228],0.95,[235,228,235,520,230,228,230,252,228],0.04,[228,228,238,522,230,228,230,255,228],1.137,[232,228,238,524,230,228,230,525,228],0.586,10,[235,228,238,527,230,228,230,528,228],0.389,11,[228,228,241,530,230,228,230,531,228],1.048,12,[232,228,241,533,230,228,230,534,228],0.786,13,[235,228,241,536,230,228,230,420,228],0.228,[228,228,244,538,230,228,230,539,228],15.551,15,[232,228,244,541,230,228,230,542,228],0.807,16,[235,228,244,404,230,228,230,544,228],17,[228,228,167,546,230,228,230,547,228],0.635,18,[232,228,167,549,230,228,230,550,228],0.366,19,[235,228,167,552,230,228,230,173,228],0.017,[228,228,249,554,230,228,230,555,228],0.297,21,[232,228,249,557,230,228,230,558,228],0.279,22,[235,228,249,512,230,228,230,560,228],23,[],[473],[],[565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588],"Absolute Trajectory Error [m], reported as value ± 0.5 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.157 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.047 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.255 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.057 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.116 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.364 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.263 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.015 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.412 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.299 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.145 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.582 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.4 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.083 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 8.78 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.309 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.023 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.218 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.111 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.007 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.11 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.082 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.066 (± not defined in the paper)",[590],"Eight real sequences (path 2.2-4.8 m) with a Livox Avia moved between two pillars while facing a flat wall; ground truth from AprilTag bundle adjustment and camera-IMU batch optimization; LIO-SAM gave no decent result",{"slug":592,"group":593,"sourceId":5,"sourceLabel":6,"table":594,"selfRows":244,"metrics":595,"seqs":597,"entrants":610,"cells":615,"outcomes":638,"locators":639,"hardware":640,"wordings":641,"notes":653},"glim2024-table-i","glim2024:Table I","Table I",[596],{"label":476,"unit":177,"statistic":151,"alignment":151},[598,602,604,606,608],{"dataset":599,"sequence":600,"environment":601},"simulation (Velodyne VLP-16 model, OpenVINS IMU synthesis)","IMU noise 1.0e-3 [m\u002Fs^2] and [deg\u002Fs]","simulated corridor (complete range degeneration)",{"dataset":599,"sequence":603,"environment":601},"IMU noise 5.0e-3 [m\u002Fs^2] and [deg\u002Fs]",{"dataset":599,"sequence":605,"environment":601},"IMU noise 1.0e-2 [m\u002Fs^2] and [deg\u002Fs]",{"dataset":599,"sequence":607,"environment":601},"IMU noise 5.0e-2 [m\u002Fs^2] and [deg\u002Fs]",{"dataset":599,"sequence":609,"environment":601},"IMU noise 1.0e-1 [m\u002Fs^2] and [deg\u002Fs]",[611,613,614],{"name":612,"methodId":209,"linkable":206,"proposed":82,"self":82},"LIO-SAM [10]",{"name":213,"methodId":214,"linkable":206,"proposed":82,"self":82},{"name":7,"methodId":5,"linkable":206,"proposed":206,"self":206},[616,618,620,622,624,626,628,630,631,632,634,636],[228,228,228,617,230,228,230,228,228],1.473,[228,228,232,619,230,228,230,232,228],4.67,[228,228,235,621,230,228,230,235,228],12.888,[232,228,228,623,230,228,230,238,228],1.294,[232,228,232,625,230,228,230,241,228],1.449,[232,228,235,627,230,228,230,244,228],2.495,[232,228,238,629,230,228,230,167,228],11.735,[235,228,228,282,230,228,230,249,228],[235,228,232,392,230,228,230,252,228],[235,228,235,633,230,228,230,255,228],0.133,[235,228,238,635,230,228,230,525,228],0.384,[235,228,241,637,230,228,230,528,228],1.566,[],[594],[],[642,643,644,645,646,647,648,649,650,579,651,652],"Absolute Trajectory Error [m], reported as value ± 0.696 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 1.863 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 5.674 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.691 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.843 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 1.111 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 4.224 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.012 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.029 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.163 (± not defined in the paper)","Absolute Trajectory Error [m], reported as value ± 0.839 (± not defined in the paper)",[654],"Simulated corridor 40 m wide with LiDAR range limited to 15 m so range data fully degenerate mid-corridor; five IMU noise levels; ATE via evo; loop closure disabled for all methods",[656,661],{"group":657,"slug":658,"sourceLabel":6,"table":659,"selfRows":241,"datasets":660},"glim2024:Table VII","glim2024-table-vii","Table VII",[134],{"group":662,"slug":663,"sourceLabel":6,"table":664,"selfRows":238,"datasets":665},"glim2024:Table X","glim2024-table-x","Table X",[142],1790510654038]