[{"data":1,"prerenderedAt":927},["ShallowReactive",2],{"method-legoloam2018":3},{"method":4,"reference":57,"equipment":76,"figures":109,"results":110},{"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":26,"sensors":31,"platform":34,"estimator":37,"association":38,"timeModel":39,"deskew":40,"loopClosure":41,"globalOptimization":42,"mapRepresentation":43,"prior":44,"outputGeometry":45,"compute":46,"codeUrl":47,"codeLicense":48,"relatedVersions":49},"legoloam2018","Shan & Englot, 2018","LeGO-LOAM","LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain",2018,"recent","C04","full_slam_with_global_correction","LeGO-LOAM 針對地面載具，先把點雲投影為距離影像（range image），分離地面點，並以影像式分割剔除少於 30 點的小群集（如樹葉）；邊緣特徵只取自非地面點，以避開草地造成的不穩定特徵，再依 LOAM 的粗糙度指標擷取邊緣與平面特徵。其兩步驟 Levenberg-Marquardt 最佳化先以地面平面特徵求 [tz, roll, pitch]，再以邊緣特徵求 [tx, ty, yaw]，以降低計算量。地圖改為儲存每次掃描的特徵集合與對應位姿，並可選擇性地接上以 ICP 建立迴圈約束、iSAM2 最佳化的位姿圖（pose graph）。","LeGO-LOAM adds ground segmentation and cluster filtering to LOAM, solves the pose in two ground-then-edge LM steps for embedded real-time use, and optionally closes loops with ICP constraints in an iSAM2 pose graph.","full_text_reviewed","peer_reviewed_published","main_body","原論文僅在校園與森林步道等室外地形測試。Feng 等人 [feng2025_construction_lidar_eval] 在施工中醫院門診大樓與模擬工地以預設參數評估 LeGO-LOAM；作者在結論中報告其因地面點特徵與迴圈閉合而在 LiDAR-only 方法中表現最佳，實際工地 APE RMSE 為 7.97 m（Table 4）；但在模擬工地中 HDL-Graph-SLAM 的 RMSE（12.12 m）略低於 LeGO-LOAM（12.33 m）（Table 3）。論文未說明實際工地 APE 所用參考軌跡的來源（全文僅描述 Gazebo 模擬的真實軌跡外掛），故實際工地 APE 只能視為作者報告值，不能當作已驗證的幾何精度。",[20,21],"public_benchmark","cross_site",[23,24,25],"Feature extraction and odometry runtime reduced by about an order of magnitude and mapping runtime by at least 60% relative to LOAM on the same hardware (Sec. IV-C, Table IV)","Two-step LM reduces odometry runtime by 34-48% with similar accuracy (Sec. IV-C, Table III)","On a forested trail (about 35 min, 19 m elevation change) end-to-start translation error 13.93 m (Jetson) vs 69.40 m for LOAM (Sec. IV-B3, Table V)",[27,28,29,30],"Ground-optimized steps assume a ground plane is visible; UAV use would require segmentation without ground extraction (Sec. V)","Accuracy evaluated only by end-to-start pose difference in campus\u002Fforest runs, not by an external trajectory or map reference (Sec. IV-B)","In KITTI loop-closure test, HDL-64E scans were reduced to a 16-ring range image (75% of points omitted) to run in real time on the Jetson (Sec. IV-D)","Follow-up work states its IMU use is the same loosely coupled scheme as LOAM (liosam2020, Sec. II)",[32,33],"3D LiDAR (Velodyne VLP-16; HDL-64E via KITTI)","IMU (low-cost CH Robotics UM6, used only for initial guess)",[35,36],"wheeled UGV (Clearpath Jackal)","vehicle (KITTI)","two-step Levenberg-Marquardt: ground planar features estimate [tz, roll, pitch], then edge features estimate [tx, ty, yaw]; optional pose graph optimized with iSAM2 (Sec. III-D, III-E, IV-D)","range-image ground separation and image-based segmentation (clusters \u003C 30 points discarded); LOAM-style roughness-based edge\u002Fplanar features; label-consistent point-to-edge \u002F point-to-plane matching (Sec. III-B to III-D)","discrete scan poses (per-scan transformation) (Sec. III)","not_reported in the paper (feature and matching details deferred to LOAM [20]); IMU provides the initial guess (Sec. IV-A)","optional: ICP between current and earlier feature sets adds pose-graph constraints, optimized by iSAM2; used only in the KITTI seq. 00 test (Sec. III-E, IV-D)","optional iSAM2 pose graph (Sec. III-E, IV-D)","per-scan edge\u002Fplanar feature sets stored with sensor poses; local map assembled from sets within 100 m or the k most recent sets (Sec. III-E)","none (assumes presence of ground for ground-optimized steps)","feature-set point cloud map and 6-DoF poses (Sec. III-E); export of full-resolution map not described in paper","CPU only on an Nvidia Jetson TX2 (ARM Cortex-A57) and a 2.5 GHz i7-4710MQ laptop; LeGO-LOAM per scan on the Jetson: segmentation 29.3 to 36.8 ms, feature extraction 6.1 to 9.9 ms, odometry 18.1 to 19.3 ms, mapping 253.3 to 278.2 ms; on the i7: 16.7 to 20.0, 2.3 to 4.4, 6.1 to 6.8 and 101.7 to 116.7 ms; LOAM extraction plus odometry exceeded 100 ms on the Jetson so scans were skipped","https:\u002F\u002Fgithub.com\u002FRobustFieldAutonomyLab\u002FLeGO-LOAM","BSD 3-Clause (LICENSE file)",[50,54],{"relation":51,"title":52,"doi_or_url":53},"preprint","Author preprint PDF hosted in the official repository (IEEE copyright notice on page 1)","https:\u002F\u002Fraw.githubusercontent.com\u002FRobustFieldAutonomyLab\u002FLeGO-LOAM\u002Fmaster\u002FShan_Englot_IROS_2018_Preprint.pdf",{"relation":55,"title":56,"doi_or_url":47},"code_release","RobustFieldAutonomyLab\u002FLeGO-LOAM",{"id":5,"kind":58,"shortName":7,"title":8,"authors":59,"year":9,"venue":62,"venueType":63,"publisher":64,"volumeIssuePages":65,"doi":66,"arxivId":67,"url":53,"firstPublicDate":68,"publicationStatus":16,"metadataStatus":69,"fulltextStatus":15,"era":10,"classicReason":70,"codeUrl":47,"cluster":11,"topics":71,"mdpi":72,"verification":73,"label":6,"fulltextRoute":74,"versionRead":75,"addedByCensus":72},"method",[60,61],"Tixiao Shan","Brendan Englot","2018 IEEE\u002FRSJ International Conference on Intelligent Robots and Systems (IROS)","conference","IEEE","pp. 4758-4765","10.1109\u002Firos.2018.8594299",null,"2018-10","metadata_verified","not_applicable",[11],false,"corrected","author copy","author preprint of the IROS 2018 paper hosted in the official repository (8 pages, IEEE copyright notice on page 1); not compared with the IEEE Xplore version of record",[77,83,89,94,99,106],{"category":78,"model":79,"canonical":79,"role":80,"dataset":67,"specs":81,"locator":82},"lidar","Velodyne VLP-16","method input","16 channels; range up to 100 m, accuracy +\u002F-3 cm; vertical FOV 30 deg (+\u002F-15 deg), 2 deg vertical resolution; 360 deg horizontal FOV, 0.1 to 0.4 deg horizontal resolution; scan rate set to 10 Hz (0.2 deg); projected to a 1800 x 16 range image","Sec. II; Sec. III-B",{"category":78,"model":84,"canonical":84,"role":85,"dataset":86,"specs":87,"locator":88},"Velodyne HDL-64E","dataset sensor","KITTI odometry (sequence 00)","360 deg horizontal FOV, 48 more channels than the VLP-16, vertical FOV 26.9 deg; downsampled to the VLP-16 range image (75% of points omitted) for real time on the Jetson","Sec. II; Sec. IV-D",{"category":90,"model":91,"canonical":91,"role":80,"dataset":67,"specs":92,"locator":93},"imu","CH Robotics UM6 Orientation Sensor","low-cost IMU on the Jackal; supplies the identical initial translational and rotational guess to LOAM and LeGO-LOAM","Sec. II; Sec. IV-A",{"category":95,"model":96,"canonical":96,"role":80,"dataset":67,"specs":97,"locator":98},"platform","Clearpath Jackal","UGV, 270 Wh lithium battery, maximum speed 2.0 m\u002Fs, maximum payload 20 kg","Sec. II; Fig. 1a",{"category":100,"model":101,"canonical":102,"role":103,"dataset":67,"specs":104,"locator":105},"compute","Nvidia Jetson TX2","NVIDIA Jetson TX2","compute for runtime","embedded device with ARM Cortex-A57 CPU; CPU only","Sec. II",{"category":100,"model":107,"canonical":107,"role":103,"dataset":67,"specs":108,"locator":105},"laptop with Intel i7-4710MQ","2.5 GHz i7-4710MQ CPU, chosen to match the LOAM papers' hardware; CPU only",[],{"totalRows":111,"groupCount":112,"groups":113,"others":713},300,43,[114,252,418,502],{"slug":115,"group":116,"sourceId":5,"sourceLabel":6,"table":117,"selfRows":118,"metrics":119,"seqs":131,"entrants":142,"cells":148,"outcomes":243,"locators":245,"hardware":246,"wordings":249,"notes":250},"legoloam2018-table-iv","legoloam2018:Table IV","Table IV",24,[120,125,127,129],{"label":121,"unit":122,"statistic":123,"alignment":124},"runtime of segmentation module per scan","ms","mean","not_reported",{"label":126,"unit":122,"statistic":123,"alignment":124},"runtime of feature extraction module per scan",{"label":128,"unit":122,"statistic":123,"alignment":124},"runtime of lidar odometry module per scan",{"label":130,"unit":122,"statistic":123,"alignment":124},"runtime of lidar mapping module per scan",[132,136,139],{"dataset":133,"sequence":134,"environment":135},"Own Jackal UGV datasets","Experiment 1","Stevens campus, smooth roads, 1.09 km, 11 m elevation change",{"dataset":133,"sequence":137,"environment":138},"Experiment 2","Stevens campus incl. sidewalk bordered by grass and trees, 1.24 km, 11 m elevation change",{"dataset":133,"sequence":140,"environment":141},"Experiment 3","forested hiking trail (dirt, asphalt, grass), 2.71 km, 19 m elevation change",[143,147],{"name":144,"methodId":145,"linkable":146,"proposed":72,"self":72},"LOAM","loam2014",true,{"name":7,"methodId":5,"linkable":146,"proposed":146,"self":146},[149,152,155,157,159,162,164,167,169,170,172,174,176,178,180,182,184,185,187,189,191,193,195,197,199,200,202,204,206,208,210,212,214,215,217,219,221,223,225,227,229,230,232,234,236,238,239,241],[150,150,150,67,150,150,150,151,150],0,-1,[153,150,150,154,151,150,150,151,150],1,29.3,[150,153,150,156,151,150,150,151,150],105.1,[153,153,150,158,151,150,150,151,150],9.1,[150,160,150,161,151,150,150,151,150],2,133.4,[153,160,150,163,151,150,150,151,150],19.3,[150,165,150,166,151,150,150,151,150],3,702.3,[153,165,150,168,151,150,150,151,150],266.7,[150,150,153,67,150,150,150,151,150],[153,150,153,171,151,150,150,151,150],29.9,[150,153,153,173,151,150,150,151,150],106.7,[153,153,153,175,151,150,150,151,150],9.9,[150,160,153,177,151,150,150,151,150],124.5,[153,160,153,179,151,150,150,151,150],18.6,[150,165,153,181,151,150,150,151,150],793.6,[153,165,153,183,151,150,150,151,150],278.2,[150,150,160,67,150,150,150,151,150],[153,150,160,186,151,150,150,151,150],36.8,[150,153,160,188,151,150,150,151,150],104.6,[153,153,160,190,151,150,150,151,150],6.1,[150,160,160,192,151,150,150,151,150],122.1,[153,160,160,194,151,150,150,151,150],18.1,[150,165,160,196,151,150,150,151,150],850.9,[153,165,160,198,151,150,150,151,150],253.3,[150,150,150,67,150,150,153,151,150],[153,150,150,201,151,150,153,151,150],16.7,[150,153,150,203,151,150,153,151,150],50.4,[153,153,150,205,151,150,153,151,150],4,[150,160,150,207,151,150,153,151,150],69.8,[153,160,150,209,151,150,153,151,150],6.8,[150,165,150,211,151,150,153,151,150],289.4,[153,165,150,213,151,150,153,151,150],108.2,[150,150,153,67,150,150,153,151,150],[153,150,153,216,151,150,153,151,150],17,[150,153,153,218,151,150,153,151,150],49.3,[153,153,153,220,151,150,153,151,150],4.4,[150,160,153,222,151,150,153,151,150],66.5,[153,160,153,224,151,150,153,151,150],6.5,[150,165,153,226,151,150,153,151,150],330.5,[153,165,153,228,151,150,153,151,150],116.7,[150,150,160,67,150,150,153,151,150],[153,150,160,231,151,150,153,151,150],20,[150,153,160,233,151,150,153,151,150],48.5,[153,153,160,235,151,150,153,151,150],2.3,[150,160,160,237,151,150,153,151,150],63,[153,160,160,190,151,150,153,151,150],[150,165,160,240,151,150,153,151,150],344.9,[153,165,160,242,151,150,153,151,150],101.7,[244],"not_applicable (N\u002FA in table)",[117],[247,248],"Nvidia Jetson TX2 (ARM Cortex-A57), CPU only","laptop, 2.5 GHz Intel i7-4710MQ, CPU only",[],[251],"Runtime of each module for processing one scan, averaged over 10 real-time trials; LOAM has no segmentation module",{"slug":253,"group":254,"sourceId":255,"sourceLabel":256,"table":257,"selfRows":118,"metrics":258,"seqs":271,"entrants":281,"cells":293,"outcomes":411,"locators":413,"hardware":414,"wordings":415,"notes":416},"yarovoi2024review-table-2","yarovoi2024review:Table 2","yarovoi2024review","Yarovoi & Cho, 2024","Table 2",[259,263,266,269],{"label":260,"unit":261,"statistic":262,"alignment":124},"Translation RMSE (m)","m","RMSE",{"label":264,"unit":261,"statistic":265,"alignment":124},"Translation STD (m)","std",{"label":267,"unit":268,"statistic":262,"alignment":124},"Rotation RMSE (°)","deg",{"label":270,"unit":268,"statistic":265,"alignment":124},"Rotation STD (°)",[272,276,278],{"dataset":273,"sequence":274,"environment":275},"Hilti SLAM Challenge Dataset 2022","Exp04","construction site, Schaan (Liechtenstein), indoor floor loop",{"dataset":273,"sequence":277,"environment":275},"Exp05",{"dataset":273,"sequence":279,"environment":280},"Exp06","construction site, Schaan (Liechtenstein), indoor floor loop with fast motions",[282,284,286,289,291],{"name":283,"methodId":5,"linkable":146,"proposed":72,"self":146},"Lego-LOAM (IMU)",{"name":285,"methodId":5,"linkable":146,"proposed":72,"self":146},"Lego-LOAM (no IMU)",{"name":287,"methodId":288,"linkable":146,"proposed":72,"self":72},"LIO-SAM","liosam2020",{"name":290,"methodId":67,"linkable":72,"proposed":72,"self":72},"ART-SLAM odom",{"name":292,"methodId":67,"linkable":72,"proposed":72,"self":72},"ART-SLAM final",[294,296,298,300,302,304,306,308,310,312,314,316,318,320,322,324,325,327,329,331,333,335,337,339,341,343,345,347,349,351,353,355,356,358,360,362,364,366,368,370,372,374,376,378,380,382,384,386,388,390,392,393,395,397,399,401,403,405,407,409],[150,150,150,295,150,150,151,151,150],3.713,[150,153,150,297,150,150,151,151,150],2.664,[150,160,150,299,150,150,151,151,150],58.7,[150,165,150,301,150,150,151,151,150],49,[150,150,153,303,151,150,151,151,150],0.982,[150,153,153,305,151,150,151,151,150],0.667,[150,160,153,307,151,150,151,151,150],7.1,[150,165,153,309,151,150,151,151,150],3.3,[150,150,160,311,150,150,151,151,150],9.683,[150,153,160,313,150,150,151,151,150],5.618,[150,160,160,315,150,150,151,151,150],102.1,[150,165,160,317,150,150,151,151,150],45,[153,150,150,319,151,150,151,151,150],2.55,[153,153,150,321,151,150,151,151,150],1.211,[153,160,150,323,151,150,151,151,150],19.4,[153,165,150,307,151,150,151,151,150],[153,150,153,326,150,150,151,151,150],5.199,[153,153,153,328,150,150,151,151,150],3.404,[153,160,153,330,150,150,151,151,150],96.2,[153,165,153,332,150,150,151,151,150],59,[153,150,160,334,150,150,151,151,150],14.417,[153,153,160,336,150,150,151,151,150],6.184,[153,160,160,338,150,150,151,151,150],110.9,[153,165,160,340,150,150,151,151,150],43.7,[160,150,150,342,151,150,151,151,150],0.167,[160,153,150,344,151,150,151,151,150],0.088,[160,160,150,346,151,150,151,151,150],1.5,[160,165,150,348,151,150,151,151,150],0.4,[160,150,153,350,151,150,151,151,150],0.095,[160,153,153,352,151,150,151,151,150],0.045,[160,160,153,354,151,150,151,151,150],0.9,[160,165,153,354,151,150,151,151,150],[160,150,160,357,151,150,151,151,150],0.311,[160,153,160,359,151,150,151,151,150],0.196,[160,160,160,361,151,150,151,151,150],1.9,[160,165,160,363,151,150,151,151,150],0.8,[165,150,150,365,151,150,151,151,150],2.755,[165,153,150,367,151,150,151,151,150],1.416,[165,160,150,369,151,150,151,151,150],16,[165,165,150,371,151,150,151,151,150],7.6,[165,150,153,373,151,150,151,151,150],1.21,[165,153,153,375,151,150,151,151,150],0.673,[165,160,153,377,151,150,151,151,150],7.8,[165,165,153,379,151,150,151,151,150],3.7,[165,150,160,381,150,150,151,151,150],19.611,[165,153,160,383,150,150,151,151,150],8.375,[165,160,160,385,150,150,151,151,150],101,[165,165,160,387,150,150,151,151,150],32.5,[205,150,150,389,151,150,151,151,150],1.032,[205,153,150,391,151,150,151,151,150],0.597,[205,160,150,224,151,150,151,151,150],[205,165,150,394,151,150,151,151,150],3.2,[205,150,153,396,151,150,151,151,150],1.12,[205,153,153,398,151,150,151,151,150],0.586,[205,160,153,400,151,150,151,151,150],11.6,[205,165,153,402,151,150,151,151,150],7.2,[205,150,160,404,150,150,151,151,150],26.413,[205,153,160,406,150,150,151,151,150],11.109,[205,160,160,408,150,150,151,151,150],134.1,[205,165,160,410,150,150,151,151,150],43.8,[412],"failed (loss of tracking; value as reported)",[257],[],[],[417],"Hilti 2022 handheld construction-site sequences; errors vs motion-capture GT; translation = Euclidean distance, rotation = smallest angle; RMSE and STD; mostly default parameters; * = loss of tracking",{"slug":419,"group":420,"sourceId":421,"sourceLabel":422,"table":423,"selfRows":231,"metrics":424,"seqs":433,"entrants":448,"cells":457,"outcomes":495,"locators":496,"hardware":497,"wordings":499,"notes":500},"ndtloam2022-table-v","ndtloam2022:Table V","ndtloam2022","Chen et al., 2022b","Table V",[425,427,429,431],{"label":426,"unit":122,"statistic":124,"alignment":70},"Segmentation time per scan (ms)",{"label":428,"unit":122,"statistic":124,"alignment":70},"Extraction time per scan (ms)",{"label":430,"unit":122,"statistic":124,"alignment":70},"Odometry time per scan (ms)",{"label":432,"unit":122,"statistic":124,"alignment":70},"Mapping time per scan (ms)",[434,438,440,442,444],{"dataset":435,"sequence":436,"environment":437},"KITTI odometry","#04","vehicle, road",{"dataset":435,"sequence":439,"environment":437},"#06",{"dataset":435,"sequence":441,"environment":437},"#07",{"dataset":435,"sequence":443,"environment":437},"#09",{"dataset":445,"sequence":446,"environment":447},"Kylin backpack","K1","backpack, indoor",[449,451,453,455],{"name":450,"methodId":5,"linkable":146,"proposed":72,"self":146},"LeGO-LOAM Segmentation",{"name":452,"methodId":5,"linkable":146,"proposed":72,"self":146},"LeGO-LOAM Extraction",{"name":454,"methodId":5,"linkable":146,"proposed":72,"self":146},"LeGO-LOAM Odometry",{"name":456,"methodId":5,"linkable":146,"proposed":72,"self":146},"LeGO-LOAM Mapping",[458,460,462,464,466,468,470,472,474,476,478,480,482,484,485,486,488,490,491,493],[150,150,150,459,151,150,150,151,150],21.5,[153,153,150,461,151,150,150,151,150],2.2,[160,160,150,463,151,150,150,151,150],8.2,[165,165,150,465,151,150,150,151,150],106.4,[150,150,153,467,151,150,150,151,150],22.7,[153,153,153,469,151,150,150,151,150],2.5,[160,160,153,471,151,150,150,151,150],8.9,[165,165,153,473,151,150,150,151,150],147.4,[150,150,160,475,151,150,150,151,150],21.6,[153,153,160,477,151,150,150,151,150],2.4,[160,160,160,479,151,150,150,151,150],7.4,[165,165,160,481,151,150,150,151,150],76.4,[150,150,165,483,151,150,150,151,150],55.6,[153,153,165,160,151,150,150,151,150],[160,160,165,402,151,150,150,151,150],[165,165,165,487,151,150,150,151,150],96.6,[150,150,205,489,151,150,150,151,150],6.2,[153,153,205,469,151,150,150,151,150],[160,160,205,492,151,150,150,151,150],4.7,[165,165,205,494,151,150,150,151,150],81.3,[],[423],[498],"laptop Intel i7-7700HQ 2.8 GHz, 8 GB RAM",[],[501],"Runtime of modules for processing one scan (ms) on KITTI 04, 06, 07, 09 and backpack K1",{"slug":503,"group":504,"sourceId":505,"sourceLabel":506,"table":507,"selfRows":508,"metrics":509,"seqs":513,"entrants":560,"cells":573,"outcomes":706,"locators":708,"hardware":709,"wordings":710,"notes":711},"voxelslam2026-table-2-full-slam-with-lc","voxelslam2026:Table 2 (full SLAM with LC)","voxelslam2026","Liu et al., 2026","Table 2 (full SLAM with LC)",13,[510],{"label":511,"unit":512,"statistic":262,"alignment":124},"absolute trajectory error (RMSE, centimeters)","cm",[514,518,521,524,528,532,536,540,544,548,551,554,557],{"dataset":515,"sequence":516,"environment":517},"Hilti handheld sequence exp01-construction (name per Table C1)","hilti01","construction environment (sequence named construction)",{"dataset":519,"sequence":520,"environment":517},"Hilti handheld sequence exp02-construction (name per Table C1)","hilti02",{"dataset":522,"sequence":523,"environment":517},"Hilti 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