[{"data":1,"prerenderedAt":690},["ShallowReactive",2],{"method-aloam_software":3},{"method":4,"reference":45,"equipment":60,"figures":80,"results":81},{"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":20,"limitations":22,"sensors":25,"platform":27,"estimator":28,"association":29,"timeModel":30,"deskew":31,"loopClosure":32,"globalOptimization":33,"mapRepresentation":34,"prior":35,"outputGeometry":36,"compute":37,"codeUrl":38,"codeLicense":39,"relatedVersions":40},"aloam_software","Qin & Cao, n.d.","A-LOAM","A-LOAM: Advanced implementation of LOAM (GitHub repository HKUST-Aerial-Robotics\u002FA-LOAM)",null,"recent","C04","odometry_with_local_mapping","A-LOAM 是 HKUST 空中機器人組對 LOAM 的重新實作，README 說明以 Eigen 與 Ceres Solver 簡化程式結構並移除繁複推導，定位為學習用的精簡版本。程式分為特徵擷取、掃描對掃描里程計與掃描對地圖精修三個節點，只使用 3D LiDAR，不讀取 IMU，也沒有迴圈閉合。依原始碼判讀，預設設定關閉了單幀內的運動畸變校正（DISTORTION 設為 0），地圖以 50 m 立方格保存邊緣與平面特徵點。在本群集已核對的論文中，F-LOAM 的授權檔說明其程式碼由 A-LOAM 修改而來，Loam_livox 與 LiLi-OM 以 A-LOAM 作為 LOAM 基準；Feng 等人的施工現場評估也提及 A-LOAM 並指出其缺少迴圈閉合。（推論）後續比較中的「LOAM」可能多指 A-LOAM；由於 A-LOAM 預設關閉單幀內畸變校正，其結果不宜直接當成原始 LOAM 論文所述方法的表現。原始 LOAM 程式碼本次未核對，兩者是否等價仍屬未驗證。","A-LOAM is a clean Eigen\u002FCeres re-implementation of LOAM; within this cluster F-LOAM is built on its code and Loam_livox and LiLi-OM use it as their LOAM baseline.","full_text_reviewed","software_or_dataset_record","supplementary","not_reported",[],[21],"Simplified, readable code intended as learning material (README)",[23,24],"Lacks loop closure detection (feng2025_construction_lidar_eval, Sec. 4.1.1)","No accompanying peer-reviewed paper or version-specific evaluation",[26],"3D spinning LiDAR only: launch files for Velodyne VLP-16, HDL-32 and HDL-64 (README examples name 'Velodyne VLP-16' and 'Velodyne HDL-64'; 'HDL-64E' is not written); no IMU subscription in the code",[18],"LOAM-style two-stage estimation re-implemented with Eigen and Ceres Solver: scan-to-scan odometry minimising point-to-edge and point-to-plane residuals (2 Ceres solves, Huber loss 0.1) followed by scan-to-map refinement (2 outer iterations, max 4 solver iterations) (README; laserOdometry.cpp; laserMapping.cpp; lidarFactor.hpp)","curvature-based features per scan-line segment: up to 2 sharp and 20 less-sharp edge points (curvature > 0.1) and 4 flat points (curvature \u003C 0.1), remaining less-flat points downsampled at 0.2 m; scan-to-scan correspondences by KD-tree nearest neighbours on adjacent scan lines (distance threshold 5 m); scan-to-map uses 5 nearest map points, accepts a line when the largest eigenvalue exceeds 3 times the second and fits a plane otherwise; only 16, 32 or 64 scan lines are supported","discrete scan poses with a fixed scan period of 0.1 s; scanRegistration.cpp stores scan-line ID plus relative in-sweep time in each point's intensity field, but laserOdometry.cpp uses it only when DISTORTION is non-zero (default 0)","not applied in the default code: '#define DISTORTION 0' makes TransformToStart use the full scan-to-scan relative pose for every point (s = 1.0), and the TransformToEnd re-projection block is wrapped in 'if (0)'; no IMU input is used","none: no loop-closure or place-recognition code in scanRegistration.cpp, laserOdometry.cpp or laserMapping.cpp (code inspection), consistent with feng2025_construction_lidar_eval Sec. 4.1.1","none (same source)","global edge and planar feature point clouds held in a 21 x 21 x 11 array of 50 m cubes (4851 cells) re-centred around the sensor; voxel-grid downsampling with defaults 0.4 m (line) and 0.8 m (plane), 0.2 m and 0.4 m in the VLP-16 launch file","none","odometry streams (\u002Flaser_odom_to_init at scan rate, \u002Faft_mapped_to_init after scan-to-map refinement, \u002Faft_mapped_to_init_high_frec), registered full-resolution cloud (\u002Fvelodyne_cloud_registered), local surround map every 5 frames and full feature map every 20 frames","no hardware or runtime figures published; the code only prints per-module timing; scan-to-scan step runs 2 Ceres solves (DENSE_QR, Huber loss 0.1), scan-to-map step 2 outer iterations with at most 4 solver iterations each; launch files set mapping_skip_frame 1 (comment: mapping at 10 Hz)","https:\u002F\u002Fgithub.com\u002FHKUST-Aerial-Robotics\u002FA-LOAM","BSD-style 3-clause text retaining LOAM copyright (Copyright 2013 Ji Zhang, CMU; 2016 Southwest Research Institute) (LICENSE file)",[41],{"relation":42,"title":43,"doi_or_url":44},"code_release","Re-implementation of LOAM (RSS 2014) per README and LICENSE","10.15607\u002FRSS.2014.X.007",{"id":5,"kind":46,"shortName":7,"title":8,"authors":47,"year":9,"venue":50,"venueType":46,"publisher":51,"volumeIssuePages":52,"doi":9,"arxivId":9,"url":38,"firstPublicDate":53,"publicationStatus":16,"metadataStatus":54,"fulltextStatus":15,"era":10,"classicReason":52,"codeUrl":38,"cluster":11,"topics":55,"mdpi":56,"verification":57,"label":6,"fulltextRoute":58,"versionRead":59,"addedByCensus":56},"software",[48,49],"Tong Qin","Shaozu Cao","GitHub repository (HKUST Aerial Robotics Group)","GitHub (HKUST-Aerial-Robotics)","not_applicable","not_verified (repository activity confirmed by commits dated 2019-03-08 to 2019-03-28 in the devel-branch Atom feed; earliest commit not established)","metadata_partial",[11],false,"corrected","other","GitHub repository HKUST-Aerial-Robotics\u002FA-LOAM, branch devel, files fetched 2026-09-25 via raw.githubusercontent.com (commit hash not recorded); no accompanying paper exists",[61,68,74],{"category":62,"model":63,"canonical":63,"role":64,"dataset":65,"specs":66,"locator":67},"lidar","Velodyne VLP-16","dataset sensor","NSH indoor outdoor (example rosbag linked in README)","16 scan lines (scan_line 16), minimum_range 0.3 m in launch file","README Sec. 3; launch\u002Faloam_velodyne_VLP_16.launch",{"category":62,"model":69,"canonical":70,"role":71,"dataset":9,"specs":72,"locator":73},"Velodyne HDL-32","Velodyne HDL-32E","method input","32 scan lines (scan_line 32); launch configuration only, no example data","launch\u002Faloam_velodyne_HDL_32.launch",{"category":62,"model":75,"canonical":76,"role":64,"dataset":77,"specs":78,"locator":79},"Velodyne HDL-64","Velodyne HDL-64E","KITTI Odometry","64 scan lines (scan_line 64), minimum_range 5 m in launch file","README Sec. 4; launch\u002Faloam_velodyne_HDL_64.launch",[],{"totalRows":82,"groupCount":83,"groups":84,"others":534},269,31,[85,221,339,433],{"slug":86,"group":87,"sourceId":88,"sourceLabel":89,"table":90,"selfRows":91,"metrics":92,"seqs":135,"entrants":144,"cells":152,"outcomes":215,"locators":216,"hardware":217,"wordings":218,"notes":219},"in2laama2021-table-i","in2laama2021:Table I","in2laama2021","Le Gentil et al., 2021","Table I",21,[93,96,100,103,105,107,109,111,113,115,117,119,121,123,125,127,129,131,133],{"label":94,"unit":95,"statistic":18,"alignment":18},"Num. fails; as printed: 0","count",{"label":97,"unit":98,"statistic":99,"alignment":18},"Final pos. error (m); as printed: 5.67 ± 2.63","m","mean",{"label":101,"unit":102,"statistic":99,"alignment":18},"Final rot. error (deg); as printed: 27.9 ± 13.6","deg",{"label":104,"unit":98,"statistic":99,"alignment":18},"Relative pos. error (m), frame-to-frame; as printed: 0.47 ± 0.14",{"label":106,"unit":102,"statistic":99,"alignment":18},"Relative rot. error (deg), frame-to-frame; as printed: 1.46 ± 0.51",{"label":108,"unit":98,"statistic":99,"alignment":18},"RMSE pos. error (m); as printed: 5.62 ± 1.72",{"label":110,"unit":102,"statistic":99,"alignment":18},"RMSE rot. error (deg); as printed: 29.2 ± 8.98",{"label":112,"unit":98,"statistic":99,"alignment":18},"Final pos. error (m); as printed: 7.03 ± 3.41",{"label":114,"unit":102,"statistic":99,"alignment":18},"Final rot. error (deg); as printed: 57.2 ± 26.2",{"label":116,"unit":98,"statistic":99,"alignment":18},"Relative pos. error (m), frame-to-frame; as printed: 0.48 ± 0.17",{"label":118,"unit":102,"statistic":99,"alignment":18},"Relative rot. error (deg), frame-to-frame; as printed: 4.85 ± 1.84",{"label":120,"unit":98,"statistic":99,"alignment":18},"RMSE pos. error (m); as printed: 6.29 ± 2.22",{"label":122,"unit":102,"statistic":99,"alignment":18},"RMSE rot. error (deg); as printed: 56.1 ± 17.9",{"label":124,"unit":98,"statistic":99,"alignment":18},"Final pos. error (m); as printed: 16.2 ± 5.60",{"label":126,"unit":102,"statistic":99,"alignment":18},"Final rot. error (deg); as printed: 119 ± 38.2",{"label":128,"unit":98,"statistic":99,"alignment":18},"Relative pos. error (m), frame-to-frame; as printed: 0.53 ± 0.12",{"label":130,"unit":102,"statistic":99,"alignment":18},"Relative rot. error (deg), frame-to-frame; as printed: 13.0 ± 3.45",{"label":132,"unit":98,"statistic":99,"alignment":18},"RMSE pos. error (m); as printed: 11.1 ± 2.71",{"label":134,"unit":102,"statistic":99,"alignment":18},"RMSE rot. error (deg); as printed: 90.7 ± 20.6",[136,140,142],{"dataset":137,"sequence":138,"environment":139},"IN2LAAMA simulation (virtual room with 7 planes, VLP-16 and MTi-3 models)","Slow (avg 14.7, max 22.1 deg\u002Fs)","simulated room",{"dataset":137,"sequence":141,"environment":139},"Moderate (avg 49.0, max 78.2 deg\u002Fs)",{"dataset":137,"sequence":143,"environment":139},"Fast (avg 125, max 198 deg\u002Fs)",[145,148,150],{"name":146,"methodId":5,"linkable":147,"proposed":56,"self":147},"[10] (A-LOAM implementation of LOAM)",true,{"name":149,"methodId":9,"linkable":56,"proposed":56,"self":56},"[5] IN2LAMA (no IMU factors)",{"name":151,"methodId":88,"linkable":147,"proposed":147,"self":56},"IN2LAAMA",[153,156,159,162,165,168,171,174,175,176,177,180,183,186,189,192,195,196,197,200,203,206,208,211,214],[154,154,154,154,155,154,155,155,154],0,-1,[154,157,154,158,155,154,155,155,154],1,5.67,[154,160,154,161,155,154,155,155,154],2,27.9,[154,163,154,164,155,154,155,155,154],3,0.47,[154,166,154,167,155,154,155,155,154],4,1.46,[154,169,154,170,155,154,155,155,154],5,5.62,[154,172,154,173,155,154,155,155,154],6,29.2,[157,154,154,154,155,154,155,155,154],[160,154,154,154,155,154,155,155,154],[154,154,157,154,155,154,155,155,154],[154,178,157,179,155,154,155,155,154],7,7.03,[154,181,157,182,155,154,155,155,154],8,57.2,[154,184,157,185,155,154,155,155,154],9,0.48,[154,187,157,188,155,154,155,155,154],10,4.85,[154,190,157,191,155,154,155,155,154],11,6.29,[154,193,157,194,155,154,155,155,154],12,56.1,[160,154,157,154,155,154,155,155,154],[154,154,160,154,155,154,155,155,154],[154,198,160,199,155,154,155,155,154],13,16.2,[154,201,160,202,155,154,155,155,154],14,119,[154,204,160,205,155,154,155,155,154],15,0.53,[154,207,160,198,155,154,155,155,154],16,[154,209,160,210,155,154,155,155,154],17,11.1,[154,212,160,213,155,154,155,155,154],18,90.7,[160,154,160,154,155,154,155,155,154],[],[90],[],[],[220],"Simulated odometry set-up, 50-run Monte Carlo, loop closure off; trajectories average 288.7 m at 4.85 m\u002Fs (max 7.35 m\u002Fs); errors on successful runs only (favours [5] in Fast); values are mean with plus-minus spread",{"slug":222,"group":223,"sourceId":224,"sourceLabel":225,"table":226,"selfRows":227,"metrics":228,"seqs":235,"entrants":242,"cells":263,"outcomes":333,"locators":334,"hardware":335,"wordings":336,"notes":337},"rloam2021-table-ii","rloam2021:Table II","rloam2021","Oelsch et al., 2021","Table II",20,[229,233],{"label":230,"unit":231,"statistic":232,"alignment":18},"APE in cm, median","cm","median",{"label":234,"unit":102,"statistic":232,"alignment":18},"RE (rotational error) in deg, median",[236,240],{"dataset":237,"sequence":238,"environment":239},"R-LOAM Gazebo simulated datasets","Dataset 1: VLP-16, 15111 scans, 0.35 m\u002Fs, 514 m, manual flight","Scenario 1 (B737 in hangar, airplane as reference)",{"dataset":237,"sequence":241,"environment":239},"Dataset 2: OS1-128, 9905 scans, 0.48 m\u002Fs, 474 m",[243,245,247,249,251,253,255,257,259,261],{"name":244,"methodId":5,"linkable":147,"proposed":56,"self":147},"LOAM [1], #Iter 2 (def)",{"name":246,"methodId":5,"linkable":147,"proposed":56,"self":147},"LOAM [1], #Iter 5",{"name":248,"methodId":5,"linkable":147,"proposed":56,"self":147},"LOAM [1], #Iter 15",{"name":250,"methodId":5,"linkable":147,"proposed":56,"self":147},"LOAM [1], #Iter 25",{"name":252,"methodId":5,"linkable":147,"proposed":56,"self":147},"LOAM [1], #Iter 35",{"name":254,"methodId":224,"linkable":147,"proposed":147,"self":56},"R-LOAM, #Iter 2 (def)",{"name":256,"methodId":224,"linkable":147,"proposed":147,"self":56},"R-LOAM, #Iter 5",{"name":258,"methodId":224,"linkable":147,"proposed":147,"self":56},"R-LOAM, #Iter 15",{"name":260,"methodId":224,"linkable":147,"proposed":147,"self":56},"R-LOAM, #Iter 25",{"name":262,"methodId":224,"linkable":147,"proposed":147,"self":56},"R-LOAM, #Iter 35",[264,266,268,270,272,274,276,278,280,282,284,286,288,290,292,294,296,298,300,301,303,305,306,308,310,311,312,313,314,315,316,318,320,322,323,325,326,328,330,332],[154,154,154,265,155,154,155,155,154],50.5,[154,157,154,267,155,154,155,155,154],2.2,[157,154,154,269,155,154,155,155,154],57.7,[157,157,154,271,155,154,155,155,154],2.56,[160,154,154,273,155,154,155,155,154],51.1,[160,157,154,275,155,154,155,155,154],2.12,[163,154,154,277,155,154,155,155,154],30.9,[163,157,154,279,155,154,155,155,154],1.59,[166,154,154,281,155,154,155,155,154],31.9,[166,157,154,283,155,154,155,155,154],1.61,[169,154,154,285,155,154,155,155,154],10.5,[169,157,154,287,155,154,155,155,154],0.4,[172,154,154,289,155,154,155,155,154],6.6,[172,157,154,291,155,154,155,155,154],0.31,[178,154,154,293,155,154,155,155,154],3.1,[178,157,154,295,155,154,155,155,154],0.25,[181,154,154,297,155,154,155,155,154],2.8,[181,157,154,299,155,154,155,155,154],0.11,[184,154,154,160,155,154,155,155,154],[184,157,154,302,155,154,155,155,154],0.09,[154,154,157,304,155,154,155,155,154],13.2,[154,157,157,302,155,154,155,155,154],[157,154,157,307,155,154,155,155,154],13.5,[157,157,157,309,155,154,155,155,154],0.08,[160,154,157,307,155,154,155,155,154],[160,157,157,309,155,154,155,155,154],[163,154,157,307,155,154,155,155,154],[163,157,157,309,155,154,155,155,154],[166,154,157,307,155,154,155,155,154],[166,157,157,309,155,154,155,155,154],[169,154,157,317,155,154,155,155,154],2.6,[169,157,157,319,155,154,155,155,154],0.13,[172,154,157,321,155,154,155,155,154],1.9,[172,157,157,302,155,154,155,155,154],[178,154,157,324,155,154,155,155,154],1.4,[178,157,157,309,155,154,155,155,154],[181,154,157,327,155,154,155,155,154],1.3,[181,157,157,329,155,154,155,155,154],0.07,[184,154,157,331,155,154,155,155,154],1.2,[184,157,157,329,155,154,155,155,154],[],[226],[],[],[338],"Scenario 1, airplane as reference object; APE (cm) and RE (deg) against Gazebo ground truth for 2 to 35 correspondence and optimization iterations; LOAM = A-LOAM",{"slug":340,"group":341,"sourceId":224,"sourceLabel":225,"table":342,"selfRows":227,"metrics":343,"seqs":346,"entrants":352,"cells":363,"outcomes":427,"locators":428,"hardware":429,"wordings":430,"notes":431},"rloam2021-table-iii","rloam2021:Table III","Table III",[344,345],{"label":230,"unit":231,"statistic":232,"alignment":18},{"label":234,"unit":102,"statistic":232,"alignment":18},[347,350],{"dataset":237,"sequence":348,"environment":349},"Dataset 3: VLP-16, 9718 scans, 0.49 m\u002Fs, 474 m","Scenario 2 (B737 in hangar, van as reference)",{"dataset":237,"sequence":351,"environment":349},"Dataset 4: OS1-128, 9726 scans, 0.49 m\u002Fs, 474 m",[353,354,355,356,357,358,359,360,361,362],{"name":244,"methodId":5,"linkable":147,"proposed":56,"self":147},{"name":246,"methodId":5,"linkable":147,"proposed":56,"self":147},{"name":248,"methodId":5,"linkable":147,"proposed":56,"self":147},{"name":250,"methodId":5,"linkable":147,"proposed":56,"self":147},{"name":252,"methodId":5,"linkable":147,"proposed":56,"self":147},{"name":254,"methodId":224,"linkable":147,"proposed":147,"self":56},{"name":256,"methodId":224,"linkable":147,"proposed":147,"self":56},{"name":258,"methodId":224,"linkable":147,"proposed":147,"self":56},{"name":260,"methodId":224,"linkable":147,"proposed":147,"self":56},{"name":262,"methodId":224,"linkable":147,"proposed":147,"self":56},[364,366,368,370,372,374,376,378,380,382,383,385,387,389,391,393,395,396,397,398,399,401,402,404,406,408,409,410,411,412,413,414,415,417,418,420,422,424,425,426],[154,154,154,365,155,154,155,155,154],19,[154,157,154,367,155,154,155,155,154],0.83,[157,154,154,369,155,154,155,155,154],65,[157,157,154,371,155,154,155,155,154],3.08,[160,154,154,373,155,154,155,155,154],24,[160,157,154,375,155,154,155,155,154],1.63,[163,154,154,377,155,154,155,155,154],23.8,[163,157,154,379,155,154,155,155,154],1.62,[166,154,154,381,155,154,155,155,154],23.9,[166,157,154,379,155,154,155,155,154],[169,154,154,384,155,154,155,155,154],17.9,[169,157,154,386,155,154,155,155,154],0.81,[172,154,154,388,155,154,155,155,154],7.3,[172,157,154,390,155,154,155,155,154],0.44,[178,154,154,392,155,154,155,155,154],6.3,[178,157,154,394,155,154,155,155,154],0.37,[181,154,154,307,155,154,155,155,154],[181,157,154,394,155,154,155,155,154],[184,154,154,307,155,154,155,155,154],[184,157,154,394,155,154,155,155,154],[154,154,157,400,155,154,155,155,154],14.8,[154,157,157,329,155,154,155,155,154],[157,154,157,403,155,154,155,155,154],15.3,[157,157,157,405,155,154,155,155,154],0.06,[160,154,157,407,155,154,155,155,154],15.4,[160,157,157,405,155,154,155,155,154],[163,154,157,407,155,154,155,155,154],[163,157,157,405,155,154,155,155,154],[166,154,157,407,155,154,155,155,154],[166,157,157,405,155,154,155,155,154],[169,154,157,392,155,154,155,155,154],[169,157,157,302,155,154,155,155,154],[172,154,157,416,155,154,155,155,154],4.9,[172,157,157,405,155,154,155,155,154],[178,154,157,419,155,154,155,155,154],4.3,[178,157,157,421,155,154,155,155,154],0.05,[181,154,157,423,155,154,155,155,154],4.2,[181,157,157,421,155,154,155,155,154],[184,154,157,423,155,154,155,155,154],[184,157,157,421,155,154,155,155,154],[],[342],[],[],[432],"Scenario 2, van as small reference object; APE (cm) and RE (deg) against Gazebo ground truth for 2 to 35 iterations; LOAM = A-LOAM",{"slug":434,"group":435,"sourceId":224,"sourceLabel":225,"table":436,"selfRows":227,"metrics":437,"seqs":440,"entrants":446,"cells":457,"outcomes":527,"locators":529,"hardware":530,"wordings":531,"notes":532},"rloam2021-table-iv","rloam2021:Table IV","Table IV",[438,439],{"label":230,"unit":231,"statistic":232,"alignment":18},{"label":234,"unit":102,"statistic":232,"alignment":18},[441,444],{"dataset":237,"sequence":442,"environment":443},"Dataset 5: VLP-16, 5674 scans, 0.51 m\u002Fs, 291 m","Scenario 3 (Eiffel Tower, tower as reference)",{"dataset":237,"sequence":445,"environment":443},"Dataset 6: OS1-128, 5762 scans, 0.51 m\u002Fs, 291 m",[447,448,449,450,451,452,453,454,455,456],{"name":244,"methodId":5,"linkable":147,"proposed":56,"self":147},{"name":246,"methodId":5,"linkable":147,"proposed":56,"self":147},{"name":248,"methodId":5,"linkable":147,"proposed":56,"self":147},{"name":250,"methodId":5,"linkable":147,"proposed":56,"self":147},{"name":252,"methodId":5,"linkable":147,"proposed":56,"self":147},{"name":254,"methodId":224,"linkable":147,"proposed":147,"self":56},{"name":256,"methodId":224,"linkable":147,"proposed":147,"self":56},{"name":258,"methodId":224,"linkable":147,"proposed":147,"self":56},{"name":260,"methodId":224,"linkable":147,"proposed":147,"self":56},{"name":262,"methodId":224,"linkable":147,"proposed":147,"self":56},[458,460,462,464,466,468,470,472,474,476,478,480,482,484,486,488,490,492,493,494,496,498,500,502,504,506,508,509,511,513,514,516,518,519,520,521,522,524,525,526],[154,154,154,459,154,154,155,155,154],499.3,[154,157,154,461,154,154,155,155,154],2.47,[157,154,154,463,154,154,155,155,154],380.2,[157,157,154,465,154,154,155,155,154],4.08,[160,154,154,467,154,154,155,155,154],1420.9,[160,157,154,469,154,154,155,155,154],17.28,[163,154,154,471,154,154,155,155,154],1326.4,[163,157,154,473,154,154,155,155,154],16.4,[166,154,154,475,154,154,155,155,154],1338.5,[166,157,154,477,154,154,155,155,154],18.21,[169,154,154,479,155,154,155,155,154],31.5,[169,157,154,481,155,154,155,155,154],0.29,[172,154,154,483,155,154,155,155,154],2.4,[172,157,154,485,155,154,155,155,154],0.04,[178,154,154,487,155,154,155,155,154],0.5,[178,157,154,489,155,154,155,155,154],0.02,[181,154,154,491,155,154,155,155,154],0.3,[181,157,154,489,155,154,155,155,154],[184,154,154,491,155,154,155,155,154],[184,157,154,495,155,154,155,155,154],0.01,[154,154,157,497,155,154,155,155,154],1058.2,[154,157,157,499,155,154,155,155,154],4.97,[157,154,157,501,155,154,155,155,154],105,[157,157,157,503,155,154,155,155,154],1.49,[160,154,157,505,155,154,155,155,154],9.3,[160,157,157,507,155,154,155,155,154],0.14,[163,154,157,285,155,154,155,155,154],[163,157,157,510,155,154,155,155,154],0.1,[166,154,157,512,155,154,155,155,154],11.3,[166,157,157,299,155,154,155,155,154],[169,154,157,515,155,154,155,155,154],6.4,[169,157,157,517,155,154,155,155,154],0.12,[172,154,157,483,155,154,155,155,154],[172,157,157,309,155,154,155,155,154],[178,154,157,331,155,154,155,155,154],[178,157,157,329,155,154,155,155,154],[181,154,157,523,155,154,155,155,154],1.1,[181,157,157,329,155,154,155,155,154],[184,154,157,157,155,154,155,155,154],[184,157,157,329,155,154,155,155,154],[528],"failed (authors state LOAM fails in this scenario with a VLP-16)",[436],[],[],[533],"Scenario 3, Eiffel Tower as reference object; APE (cm) and RE (deg) against Gazebo ground truth for 2 to 35 iterations; LOAM = A-LOAM; authors state LOAM fails with the VLP-16",[535,541,547,553,559,565,570,574,581,587,593,602,608,614,619,624,629,635,641,646,651,655,659,665,671,678,683],{"group":536,"slug":537,"sourceLabel":538,"table":436,"selfRows":212,"datasets":539},"viralfusion2022:Table IV","viralfusion2022-table-iv","Nguyen et al., 2022b",[540],"AirSim Building_99 (authors' simulation)",{"group":542,"slug":543,"sourceLabel":544,"table":436,"selfRows":209,"datasets":545},"jiao2022fusionportable:Table IV","jiao2022fusionportable-table-iv","Jiao et al., 2022",[546],"FusionPortable",{"group":548,"slug":549,"sourceLabel":550,"table":436,"selfRows":204,"datasets":551},"roloam2022:Table IV","roloam2022-table-iv","Oelsch et al., 2022",[552],"RO-LOAM hangar datasets (octocopter, VLP-16, Leica MS60 ground truth)",{"group":554,"slug":555,"sourceLabel":556,"table":226,"selfRows":201,"datasets":557},"locus2021:Table II","locus2021-table-ii","Palieri et al., 2021",[558],"DARPA SubT Husky datasets (CoSTAR)",{"group":560,"slug":561,"sourceLabel":562,"table":226,"selfRows":198,"datasets":563},"ruan2023slamesh:Table II","ruan2023slamesh-table-ii","Ruan et al., 2023",[564],"KITTI odometry",{"group":566,"slug":567,"sourceLabel":568,"table":90,"selfRows":193,"datasets":569},"ndtloam2022:Table I","ndtloam2022-table-i","Chen et al., 2022b",[564],{"group":571,"slug":572,"sourceLabel":568,"table":226,"selfRows":193,"datasets":573},"ndtloam2022:Table II","ndtloam2022-table-ii",[564],{"group":575,"slug":576,"sourceLabel":577,"table":578,"selfRows":193,"datasets":579},"yin2023semanticbimloc:Table 6","yin2023semanticbimloc-table-6","Yin et al., 2023","Table 6",[580],"self-collected NUS SDE4 sequences",{"group":582,"slug":583,"sourceLabel":584,"table":226,"selfRows":181,"datasets":585},"sgraphsplus2023:Table II","sgraphsplus2023-table-ii","Bavle et al., 2023",[586],"in-house construction-site dataset (VLP-16)",{"group":588,"slug":589,"sourceLabel":590,"table":90,"selfRows":178,"datasets":591},"voxelmappp2024:Table I","voxelmappp2024-table-i","Wu et al., 2024b",[592],"M2DGR",{"group":594,"slug":595,"sourceLabel":596,"table":597,"selfRows":172,"datasets":598},"liliom2021:Table 1","liliom2021-table-1","Li et al., 2021b","Table 1",[599,600,601],"UTBM (EU long-term)","UrbanLoco","UrbanNav",{"group":603,"slug":604,"sourceLabel":605,"table":578,"selfRows":172,"datasets":606},"lim2024quatropp:Table 6","lim2024quatropp-table-6","Lim et al., 2024",[607],"KITTI",{"group":609,"slug":610,"sourceLabel":538,"table":611,"selfRows":172,"datasets":612},"viralfusion2022:Table VI","viralfusion2022-table-vi","Table VI",[613],"VIRAL field flight tests (authors)",{"group":615,"slug":616,"sourceLabel":538,"table":617,"selfRows":172,"datasets":618},"viralfusion2022:Table VII","viralfusion2022-table-vii","Table VII",[613],{"group":620,"slug":621,"sourceLabel":584,"table":90,"selfRows":169,"datasets":622},"sgraphsplus2023:Table I","sgraphsplus2023-table-i",[623],"in-house simulated data (VLP-16 simulated)",{"group":625,"slug":626,"sourceLabel":89,"table":611,"selfRows":166,"datasets":627},"in2laama2021:Table VI","in2laama2021-table-vi",[628],"IN2LAAMA UTS datasets",{"group":630,"slug":631,"sourceLabel":632,"table":90,"selfRows":166,"datasets":633},"interactiveslam2021:Table I","interactiveslam2021-table-i","Koide et al., 2021a",[634],"PASCO Mobile Measurement System outdoor dataset (released via SMRT-AIST)",{"group":636,"slug":637,"sourceLabel":638,"table":90,"selfRows":166,"datasets":639},"loamlivox2020:Table I","loamlivox2020-table-i","Lin & Zhang, 2020",[640],"not_reported (data used for timing not stated)",{"group":642,"slug":643,"sourceLabel":644,"table":226,"selfRows":163,"datasets":645},"artslam2022:Table II","artslam2022-table-ii","Frosi & Matteucci, 2022",[564],{"group":647,"slug":648,"sourceLabel":644,"table":342,"selfRows":163,"datasets":649},"artslam2022:Table III","artslam2022-table-iii",[650],"KITTI raw",{"group":652,"slug":653,"sourceLabel":644,"table":436,"selfRows":163,"datasets":654},"artslam2022:Table IV","artslam2022-table-iv",[564],{"group":656,"slug":657,"sourceLabel":556,"table":342,"selfRows":163,"datasets":658},"locus2021:Table III","locus2021-table-iii",[558],{"group":660,"slug":661,"sourceLabel":89,"table":662,"selfRows":160,"datasets":663},"in2laama2021:Text Sec.VII-E-2","in2laama2021-text-sec-vii-e-2","Text Sec.VII-E-2",[664],"MC2SLAM dataset",{"group":666,"slug":667,"sourceLabel":668,"table":226,"selfRows":160,"datasets":669},"sslslam2021:Table II","sslslam2021-table-ii","Wang et al., 2021b",[670],"own rotation test",{"group":672,"slug":673,"sourceLabel":674,"table":675,"selfRows":157,"datasets":676},"ghadimzadeh2025slamnde:Table 3","ghadimzadeh2025slamnde-table-3","Ghadimzadeh Alamdari et al., 2025","Table 3",[677],"Luleå SubT tunnel dataset (Koval et al. 2022)",{"group":679,"slug":680,"sourceLabel":544,"table":681,"selfRows":157,"datasets":682},"jiao2022fusionportable:Text Sec.V","jiao2022fusionportable-text-sec-v","Text Sec.V",[546],{"group":684,"slug":685,"sourceLabel":686,"table":687,"selfRows":157,"datasets":688},"trzeciak2023conslam:Text Practical application","trzeciak2023conslam-text-practical-application","Trzeciak et al., 2023","Text Practical application",[689],"ConSLAM",1790510664621]