[{"data":1,"prerenderedAt":1288},["ShallowReactive",2],{"method-segal2009gicp":3},{"method":4,"reference":50,"equipment":69,"figures":93,"results":94},{"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":26,"sensors":32,"platform":36,"estimator":38,"association":39,"timeModel":40,"deskew":41,"loopClosure":42,"globalOptimization":42,"mapRepresentation":43,"prior":44,"outputGeometry":45,"compute":46,"codeUrl":47,"codeLicense":48,"relatedVersions":49},"segal2009gicp","Segal et al., 2009","GICP","Generalized-ICP",2009,"classic","C02","registration_component","GICP 將點對點與點對平面 ICP 納入同一機率框架：兩片點雲的每個點都被視為來自高斯分布，最小化步驟以最大概似估計計算位姿。作者依局部平面假設，令每點沿表面法向量的共變異數很小、沿平面方向很大，形成「平面對平面」配準；點對點與點對平面皆可視為其特例。對應點仍以歐氏距離與 kd-tree 搜尋，因此保留 ICP 的速度與簡潔。實驗使用射線追蹤模擬的 SICK 旋轉掃描（室內走廊與建物周邊戶外，1 cm 雜訊）及車頂 Velodyne 的郊區環線記錄；Velodyne 測試掃描對彼此相距 15 至 20 m 以上，各方法的初始誤差在每軸 ±1.5 m 與 ±15° 內隨機產生。真實資料的參考位姿來自結合 GPS 與 IMU 的成對約束 SLAM，作者自承並非完美的真值。論文結果只以圖呈現，沒有數值表。","GICP casts ICP's minimization step as MLE over Gaussian point models with surface-aligned covariances from both scans (plane-to-plane), with point-to-point and point-to-plane as special cases.","full_text_reviewed","peer_reviewed_published","main_body","not_reported（模擬室內走廊與郊區道路車載資料，未涉及營建場域）",[20],"simulation",[22,23,24,25],"outperforms standard ICP and point-to-plane on simulated and real data (abstract, Sec. IV)","less sensitive to the max match distance d_max because inconsistent correspondences are down-weighted, and in the simulated scenes accuracy is not sensitive to overestimated d_max (Sec. IV, Fig. 5)","on real Velodyne data GICP with its worst-case d_max performs roughly as well as point-to-plane with its best-case d_max (Sec. IV, p. 8)","removes some local minima seen with point-to-plane in Velodyne scan pairs about 30 m apart (Sec. IV, Figs. 6 to 7)",[27,28,29,30,31],"assumes locally planar surfaces","high-frequency real data raises incorrect correspondences sharing orientation, not handled by the model (Sec. IV)","outlier terms and slack distributions left for future work (Sec. IV to V)","real-data ground truth came from pairwise constraint-based SLAM (standard ICP on scans spaced an order of magnitude closer) with GPS and IMU, which the authors call not a perfect method but a reasonable baseline (Sec. IV, p. 5)","results given only as plots, without runtime (reviewer observation)",[33,34,35],"[\"3D LiDAR (simulated SICK scanner on a rotating joint","roof-mounted Velodyne on an instrumented car)\", \"GPS and IMU (car logs","used only to build ground truth)\"]",[20,37],"vehicle","maximum-likelihood estimate over Gaussian point models; minimized with conjugate gradients in the experiments (Sec. III-IV)","Euclidean nearest neighbour via kd-tree with max match distance d_max; plane-to-plane covariance weighting (Sec. III)","not_applicable (pairwise rigid registration)","not_reported","none","point clouds with per-point covariance from PCA of 20 nearest neighbours (Sec. III-B)","initial transformation T0 required (Alg. 1)","6-DoF rigid transformation","not_reported (no runtime given); kd-tree closest-point lookup needing O(n log n) explicit point comparisons (Sec. II, p. 2); all three algorithms minimized with conjugate gradients for comparability, capped at 250 iterations for standard ICP and 50 for point-to-plane and GICP (Sec. IV); 15,000 points per scan in the tests (Figs. 5, 9)",null,"not_verified",[],{"id":5,"kind":51,"shortName":7,"title":8,"authors":52,"year":9,"venue":56,"venueType":57,"publisher":58,"volumeIssuePages":41,"doi":59,"arxivId":47,"url":60,"firstPublicDate":61,"publicationStatus":16,"metadataStatus":62,"fulltextStatus":15,"era":10,"classicReason":63,"codeUrl":47,"cluster":11,"topics":64,"mdpi":65,"verification":66,"label":6,"fulltextRoute":67,"versionRead":68,"addedByCensus":65},"method",[53,54,55],"Aleksandr V. Segal","Dirk Haehnel","Sebastian Thrun","Robotics: Science and Systems V","conference","RSS Foundation","10.15607\u002Frss.2009.v.021","https:\u002F\u002Fwww.roboticsproceedings.org\u002Frss05\u002Fp21.pdf","2009-06-28","metadata_verified","principle reused: plane-to-plane covariance-weighted ICP is the base of VGICP, small_gicp and GICP-based LiDAR odometry\u002Fmapping.",[11],false,"corrected","publisher OA","RSS 2009 online proceedings PDF (paper p21, 8 pages), version of record; pp. 5 to 7 have no text layer and were read from page renders",[70,77,82,87,90],{"category":71,"model":72,"canonical":72,"role":73,"dataset":74,"specs":75,"locator":76},"lidar","SICK scanner","method input","simulated indoor hallway and outdoor building scenes","simulated by ray tracing, mounted on a rotating joint; Gaussian noise added (plots titled 15,000 points, 1 cm noise)","Sec. IV (p. 4), Figs. 2, 3, 5",{"category":71,"model":78,"canonical":78,"role":73,"dataset":79,"specs":80,"locator":81},"Velodyne range finder","instrumented-car logs, suburban loop","roof-mounted on an instrumented car; scans cover a range of 70 to 100 m from the sensor","Sec. IV (pp. 4 and 8), Fig. 4",{"category":83,"model":84,"canonical":84,"role":85,"dataset":79,"specs":41,"locator":86},"gnss","GPS","reference or ground truth","Sec. IV (p. 4)",{"category":88,"model":89,"canonical":89,"role":85,"dataset":79,"specs":41,"locator":86},"imu","IMU",{"category":91,"model":92,"canonical":92,"role":73,"dataset":79,"specs":41,"locator":86},"platform","instrumented car",[],{"totalRows":95,"groupCount":96,"groups":97,"others":1197},138,19,[98,405,670,942],{"slug":99,"group":100,"sourceId":101,"sourceLabel":102,"table":103,"selfRows":104,"metrics":105,"seqs":113,"entrants":140,"cells":163,"outcomes":399,"locators":400,"hardware":401,"wordings":402,"notes":403},"vizzo2021puma-table-ii","vizzo2021puma:Table II","vizzo2021puma","Vizzo et al., 2021","Table II",26,[106,110],{"label":107,"unit":108,"statistic":109,"alignment":42},"relative translational error (%)","%","mean",{"label":111,"unit":112,"statistic":109,"alignment":42},"relative rotational error (deg per 100 m)","deg\u002F100 m",[114,118,120,122,124,126,128,130,132,134,136,138],{"dataset":115,"sequence":116,"environment":117},"KITTI Odometry","average of 00-10","outdoor 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odometry training sequences 00-10; relative errors averaged over 100-800 m segments; all methods share the range-image normals and Huber loss; Map None = frame-to-frame, Map Point cloud = frame-to-model on the last N scans; DA = data association (NN nearest neighbour, Proj. projective, RC ray casting). Per-sequence rotational errors omitted to respect the row cap; only the rotational average is kept",{"slug":406,"group":407,"sourceId":408,"sourceLabel":409,"table":410,"selfRows":411,"metrics":412,"seqs":417,"entrants":464,"cells":477,"outcomes":664,"locators":665,"hardware":666,"wordings":667,"notes":668},"mrsmap2014-table-1","mrsmap2014:Table 1","mrsmap2014","Stückler & Behnke, 2014","Table 1",22,[413],{"label":414,"unit":415,"statistic":416,"alignment":41},"median relative pose error (RPE) in mm","mm","median",[418,422,424,426,428,430,432,434,436,438,440,442,444,446,448,450,452,454,456,458,460,462],{"dataset":419,"sequence":420,"environment":421},"TUM RGB-D (Freiburg)","fr1 360","indoor office and structure\u002Ftexture test scenes, RGB-D camera (carrying mode not stated in the paper)",{"dataset":419,"sequence":423,"environment":421},"fr1 desk",{"dataset":419,"sequence":425,"environment":421},"fr1 desk2",{"dataset":419,"sequence":427,"environment":421},"fr1 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odometry, trained on 00-06 (marked *) and tested on 07-10; trel = average translational RMSE (%) over 100-800 m subsequences; rows other than LOAM w\u002Fo mapping and Ours are copied from LO-Net [10]; LOAM is a full system with mapping, others are odometry only",[1198,1205,1212,1221,1227,1234,1240,1248,1253,1258,1263,1268,1272,1277,1283],{"group":1199,"slug":1200,"sourceLabel":1201,"table":1202,"selfRows":196,"datasets":1203},"razlaw2015evaluation:Table III","razlaw2015evaluation-table-iii","Razlaw et al., 2015","Table III",[1204],"Razlaw et al. MAV laser datasets",{"group":1206,"slug":1207,"sourceLabel":1208,"table":1209,"selfRows":190,"datasets":1210},"koide2024smallgicp:Text BENCHMARK.md Accuracy","koide2024smallgicp-text-benchmark-md-accuracy","Koide, 2024","Text BENCHMARK.md Accuracy",[1211],"KITTI odometry sequence 00",{"group":1213,"slug":1214,"sourceLabel":1215,"table":103,"selfRows":187,"datasets":1216},"balm2_2023:Table II","balm2-2023-table-ii","Liu et al., 2023a",[1217,1218,1219,1220],"Hilti 2021, VIRAL and UrbanLoco (19 sequences)","Hilti SLAM Challenge 2021","NTU VIRAL","UrbanLoco",{"group":1222,"slug":1223,"sourceLabel":1224,"table":103,"selfRows":184,"datasets":1225},"koide2021vgicp:Table II","koide2021vgicp-table-ii","Koide et al., 2021b",[1226],"authors' HDL-32e sequences",{"group":1228,"slug":1229,"sourceLabel":1230,"table":1231,"selfRows":184,"datasets":1232},"lim2024quatropp:Table 6","lim2024quatropp-table-6","Lim et al., 2024","Table 6",[1233],"KITTI",{"group":1235,"slug":1236,"sourceLabel":1224,"table":1237,"selfRows":178,"datasets":1238},"koide2021vgicp:Table I","koide2021vgicp-table-i","Table I",[1239],"authors' simulated LiDAR sequence",{"group":1241,"slug":1242,"sourceLabel":1243,"table":1244,"selfRows":178,"datasets":1245},"nerfloam2023:Table 3","nerfloam2023-table-3","Deng et al., 2023","Table 3",[683,1246,1247],"MaiCity","Newer College",{"group":1249,"slug":1250,"sourceLabel":1243,"table":1251,"selfRows":178,"datasets":1252},"nerfloam2023:Table 5","nerfloam2023-table-5","Table 5",[683],{"group":1254,"slug":1255,"sourceLabel":1224,"table":1256,"selfRows":175,"datasets":1257},"koide2021vgicp:Text Sec. IV-A","koide2021vgicp-text-sec-iv-a","Text Sec. IV-A",[1239],{"group":1259,"slug":1260,"sourceLabel":1261,"table":1237,"selfRows":175,"datasets":1262},"lim2025kissmatcher:Table I","lim2025kissmatcher-table-i","Lim et al., 2025",[1233],{"group":1264,"slug":1265,"sourceLabel":409,"table":1266,"selfRows":172,"datasets":1267},"mrsmap2014:Table 2","mrsmap2014-table-2","Table 2",[419],{"group":1269,"slug":1270,"sourceLabel":1215,"table":1202,"selfRows":169,"datasets":1271},"balm2_2023:Table III","balm2-2023-table-iii",[1218],{"group":1273,"slug":1274,"sourceLabel":1275,"table":1202,"selfRows":169,"datasets":1276},"genzicp2025:Table III","genzicp2025-table-iii","Lee et al., 2025a",[683],{"group":1278,"slug":1279,"sourceLabel":1280,"table":1281,"selfRows":169,"datasets":1282},"zhang2024_3dlidarslam_survey:Table 8","zhang2024-3dlidarslam-survey-table-8","Zhang et al., 2024a","Table 8",[683],{"group":1284,"slug":1285,"sourceLabel":1280,"table":1286,"selfRows":169,"datasets":1287},"zhang2024_3dlidarslam_survey:Table 9","zhang2024-3dlidarslam-survey-table-9","Table 9",[683],1790510662722]