[{"data":1,"prerenderedAt":1111},["ShallowReactive",2],{"method-suma2018":3},{"method":4,"reference":51,"equipment":72,"figures":97,"results":98},{"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":25,"sensors":31,"platform":33,"estimator":35,"association":36,"timeModel":37,"deskew":38,"loopClosure":39,"globalOptimization":40,"mapRepresentation":41,"prior":42,"outputGeometry":43,"compute":44,"codeUrl":45,"codeLicense":46,"relatedVersions":47},"suma2018","Behley & Stachniss, 2018","SuMa","Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments",2018,"recent","C04","full_slam_with_global_correction","SuMa 以面元（surfel，帶法向量與半徑的小圓盤）地圖表示環境，將掃描投影成球面頂點圖與法向量圖，並從面元地圖繪製（render）同視角的模型圖，以投影式資料關聯（projective data association）執行密集的點到面 frame-to-model ICP，避免最近鄰搜尋。面元以穩定度對數勝算比過濾動態物體與雜訊；迴圈則在非活動地圖中搜尋候選，並以「合成虛擬視圖」檢驗一致性、連續多幀驗證後才加入位姿圖。由於面元綁定建立時的位姿，位姿圖最佳化後可直接更新地圖。","SuMa performs dense frame-to-model ICP by projective association against views rendered from a GPU surfel map, verifies loop closures with composed virtual views, and updates the pose-anchored surfel map after pose-graph optimization.","full_text_reviewed","peer_reviewed_published","main_body","not_reported（僅在 KITTI 城市、鄉間與高速公路資料測試；（推論）工地常見的長廊與開闊樓板等結構稀少場景可能類似其報告的退化情境，需實測）。",[20],"public_benchmark",[22,23,24],"KITTI training set average 0.3 deg\u002F100 m and 0.8% relative translational error with loop closure (Table II)","Full pipeline including loop closure at about 20 Hz on average on KITTI 00 (Sec. IV, Fig. 7)","Loop detection works with small overlap between scans via the virtual-view criterion (Sec. III-E, Fig. 5)",[26,27,28,29,30],"Test set: 1.4% translational error vs 0.7% reported for LOAM (Sec. IV)","Fails to estimate motion in sequences with very few structures such as highways (Sec. IV)","Consistently moving objects (e.g., cars in a jam) can be integrated as static surfels and corrupt the map (Sec. IV)","Loop-closure gains are hard to assess with KITTI's GPS\u002FINS ground truth, which has height inconsistencies (Sec. IV)","Requires a GPU (OpenGL) implementation (Sec. III-F)",[32],"3D LiDAR (Velodyne HDL-64E S2 via KITTI)",[34],"vehicle (KITTI)","frame-to-model point-to-plane ICP with projective data association, Gauss-Newton with Huber weights; pose graph optimized with gtsam (Levenberg-Marquardt) in a separate thread (Sec. III-C, III-E, III-F)","dense projective data association between the current vertex\u002Fnormal maps (spherical projection) and vertex\u002Fnormal maps rendered from the surfel map (Sec. III-A, III-C)","discrete scan poses","not_reported","single candidate within a radius searched in the inactive map; ICP with multiple initializations; accepted only if a composed virtual map view is consistent with the scan, then verified over subsequent scans (Sec. III-E)","pose graph of odometry and verified loop closures (gtsam); surfels are anchored to creation poses so the map is updated without re-integration (Sec. III-B, III-E)","surfel map (position, normal, radius, creation\u002Fupdate timestamps, stability log-odds); active\u002Finactive partition; GPU rolling-grid submaps (Sec. III-B, III-F)","none","globally consistent surfel map \u002F registered point cloud (Fig. 1, Fig. 4)","OpenGL 4.0 GPU implementation with a 2D rolling-grid of submaps offloaded between GPU and main memory; test machine Intel i7-6700 @3.4 GHz with 16 GB RAM and Nvidia GeForce GTX 960 with 4 GB RAM; odometry and map update 31 ms average (max 71 ms), up to 189 ms with loop closure detection and verification, 48 ms average overall (about 20 Hz) on KITTI 00; pose graph optimized with gtsam 4.0 Levenberg-Marquardt in a separate thread (Sec. III-F, IV, Fig. 7)","https:\u002F\u002Fgithub.com\u002Fjbehley\u002FSuMa","MIT-style permission notice (LICENSE file, Copyright 2016-2018 Jens Behley)",[48],{"relation":49,"title":50,"doi_or_url":45},"code_release","jbehley\u002FSuMa",{"id":5,"kind":52,"shortName":7,"title":8,"authors":53,"year":9,"venue":56,"venueType":57,"publisher":58,"volumeIssuePages":59,"doi":60,"arxivId":61,"url":62,"firstPublicDate":63,"publicationStatus":16,"metadataStatus":64,"fulltextStatus":15,"era":10,"classicReason":65,"codeUrl":45,"cluster":11,"topics":66,"mdpi":68,"verification":69,"label":6,"fulltextRoute":70,"versionRead":71,"addedByCensus":68},"method",[54,55],"Jens Behley","Cyrill Stachniss","Robotics: Science and Systems XIV (RSS 2018)","conference","RSS Foundation","RSS XIV, paper p16 (proceedings URL rss14\u002Fp16.pdf)","10.15607\u002Frss.2018.xiv.016",null,"https:\u002F\u002Fwww.roboticsproceedings.org\u002Frss14\u002Fp16.pdf","2018-06-26","metadata_verified","not_applicable",[11,67],"C12",false,"confirmed","publisher OA","version of record: RSS 2018 proceedings PDF (Robotics: Science and Systems XIV, p16)",[73,81,87,93],{"category":74,"model":75,"canonical":76,"role":77,"dataset":78,"specs":79,"locator":80},"lidar","Velodyne HDL-64E S2","Velodyne HDL-64E","dataset sensor","KITTI odometry","provided KITTI point clouds recorded at 10 Hz; parameters assume vertical FoV fup 3.0 deg and fdown 25 deg, vertex map 900 x 64 (Table I)","Sec. IV, Table I",{"category":82,"model":83,"canonical":83,"role":84,"dataset":78,"specs":85,"locator":86},"gnss","KITTI GPS-based inertial navigation system ground truth (model not stated)","reference or ground truth","authors observed height inconsistencies in the training ground truth","Sec. IV, Fig. 6",{"category":88,"model":89,"canonical":89,"role":90,"dataset":61,"specs":91,"locator":92},"compute","Intel i7-6700","compute for runtime","3.4 GHz, 16 GB RAM","Sec. IV",{"category":88,"model":94,"canonical":94,"role":90,"dataset":61,"specs":95,"locator":96},"Nvidia GeForce GTX 960","4 GB RAM; OpenGL 4.0 implementation","Sec. III-F, IV",[],{"totalRows":99,"groupCount":100,"groups":101,"others":894},387,46,[102,331,521,715],{"slug":103,"group":104,"sourceId":5,"sourceLabel":6,"table":105,"selfRows":106,"metrics":107,"seqs":115,"entrants":142,"cells":157,"outcomes":325,"locators":326,"hardware":327,"wordings":328,"notes":329},"suma2018-table-ii","suma2018:Table II","Table II",72,[108,112],{"label":109,"unit":110,"statistic":111,"alignment":38},"relative rotational error (deg per 100 m)","deg\u002F100m","mean",{"label":113,"unit":114,"statistic":111,"alignment":38},"relative translational error (%)","%",[116,120,122,124,126,128,130,132,134,136,138,140],{"dataset":117,"sequence":118,"environment":119},"KITTI odometry (training)","00*","street environments (KITTI)",{"dataset":117,"sequence":121,"environment":119},"01",{"dataset":117,"sequence":123,"environment":119},"02*",{"dataset":117,"sequence":125,"environment":119},"03",{"dataset":117,"sequence":127,"environment":119},"04",{"dataset":117,"sequence":129,"environment":119},"05*",{"dataset":117,"sequence":131,"environment":119},"06*",{"dataset":117,"sequence":133,"environment":119},"07*",{"dataset":117,"sequence":135,"environment":119},"08*",{"dataset":117,"sequence":137,"environment":119},"09*",{"dataset":117,"sequence":139,"environment":119},"10",{"dataset":117,"sequence":141,"environment":119},"Average",[143,146,148,150,153,155],{"name":144,"methodId":5,"linkable":145,"proposed":145,"self":145},"Frame-to-Frame",true,{"name":147,"methodId":5,"linkable":145,"proposed":145,"self":145},"Frame-to-Model",{"name":149,"methodId":5,"linkable":145,"proposed":145,"self":145},"Frame-to-Model with loop closure",{"name":151,"methodId":152,"linkable":145,"proposed":68,"self":68},"LOAM [35]","loam2017_auro",{"name":154,"methodId":61,"linkable":68,"proposed":68,"self":68},"S-LSD [6] (Stereo LSD-SLAM)",{"name":156,"methodId":61,"linkable":68,"proposed":68,"self":68},"SOFT-SLAM [2]",[158,162,165,167,169,172,174,177,179,181,183,185,187,190,191,193,195,197,199,201,203,205,206,208,210,212,213,215,217,219,220,221,222,223,224,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,314,315,316,317,318,319,320,321,322,323,324],[159,159,159,160,161,159,161,161,159],0,0.9,-1,[159,163,159,164,161,159,161,161,159],1,2.1,[159,159,163,166,161,159,161,161,159],1.2,[159,163,163,168,161,159,161,161,159],4,[159,159,170,171,161,159,161,161,159],2,0.8,[159,163,170,173,161,159,161,161,159],2.3,[159,159,175,176,161,159,161,161,159],3,0.7,[159,163,175,178,161,159,161,161,159],1.4,[159,159,168,180,161,159,161,161,159],1.1,[159,163,168,182,161,159,161,161,159],11.9,[159,159,184,171,161,159,161,161,159],5,[159,163,184,186,161,159,161,161,159],1.5,[159,159,188,189,161,159,161,161,159],6,0.6,[159,163,188,163,161,159,161,161,159],[159,159,192,166,161,159,161,161,159],7,[159,163,192,194,161,159,161,161,159],1.8,[159,159,196,163,161,159,161,161,159],8,[159,163,196,198,161,159,161,161,159],2.5,[159,159,200,171,161,159,161,161,159],9,[159,163,200,202,161,159,161,161,159],1.9,[159,159,204,163,161,159,161,161,159],10,[159,163,204,194,161,159,161,161,159],[159,159,207,160,161,159,161,161,159],11,[159,163,207,209,161,159,161,161,159],2.9,[163,159,159,211,161,159,161,161,159],0.3,[163,163,159,176,161,159,161,161,159],[163,159,163,214,161,159,161,161,159],0.5,[163,163,163,216,161,159,161,161,159],1.7,[163,159,170,218,161,159,161,161,159],0.4,[163,163,170,180,161,159,161,161,159],[163,159,175,214,161,159,161,161,159],[163,163,175,176,161,159,161,161,159],[163,159,168,211,161,159,161,161,159],[163,163,168,218,161,159,161,161,159],[163,159,184,225,161,159,161,161,159],0.2,[163,163,184,214,161,159,161,161,159],[163,159,188,225,161,159,161,161,159],[163,163,188,218,161,159,161,161,159],[163,159,192,211,161,159,161,161,159],[163,163,192,218,161,159,161,161,159],[163,159,196,218,161,159,161,161,159],[163,163,196,163,161,159,161,161,159],[163,159,200,211,161,159,161,161,159],[163,163,200,214,161,159,161,161,159],[163,159,204,211,161,159,161,161,159],[163,163,204,176,161,159,161,161,159],[163,159,207,211,161,159,161,161,159],[163,163,207,176,161,159,161,161,159],[170,159,159,225,161,159,161,161,159],[170,163,159,176,161,159,161,161,159],[170,159,163,214,161,159,161,161,159],[170,163,163,216,161,159,161,161,159],[170,159,170,218,161,159,161,161,159],[170,163,170,166,161,159,161,161,159],[170,159,175,214,161,159,161,161,159],[170,163,175,176,161,159,161,161,159],[170,159,168,211,161,159,161,161,159],[170,163,168,218,161,159,161,161,159],[170,159,184,225,161,159,161,161,159],[170,163,184,218,161,159,161,161,159],[170,159,188,211,161,159,161,161,159],[170,163,188,214,161,159,161,161,159],[170,159,192,189,161,159,161,161,159],[170,163,192,176,161,159,161,161,159],[170,159,196,218,161,159,161,161,159],[170,163,196,166,161,159,161,161,159],[170,159,200,225,161,159,161,161,159],[170,163,200,189,161,159,161,161,159],[170,159,204,211,161,159,161,161,159],[170,163,204,176,161,159,161,161,159],[170,159,207,211,161,159,161,161,159],[170,163,207,171,161,159,161,161,159],[175,163,159,171,161,159,161,161,159],[175,163,163,178,161,159,161,161,159],[175,163,170,160,161,159,161,161,159],[175,163,175,160,161,159,161,161,159],[175,163,168,176,161,159,161,161,159],[175,163,184,189,161,159,161,161,159],[175,163,188,176,161,159,161,161,159],[175,163,192,189,161,159,161,161,159],[175,163,196,180,161,159,161,161,159],[175,163,200,171,161,159,161,161,159],[175,163,204,171,161,159,161,161,159],[175,163,207,171,161,159,161,161,159],[168,159,159,211,161,159,161,161,159],[168,163,159,189,161,159,161,161,159],[168,159,163,211,161,159,161,161,159],[168,163,163,279,161,159,161,161,159],2.4,[168,159,170,225,161,159,161,161,159],[168,163,170,171,161,159,161,161,159],[168,159,175,211,161,159,161,161,159],[168,163,175,163,161,159,161,161,159],[168,159,168,211,161,159,161,161,159],[168,163,168,218,161,159,161,161,159],[168,159,184,225,161,159,161,161,159],[168,163,184,176,161,159,161,161,159],[168,159,188,225,161,159,161,161,159],[168,163,188,176,161,159,161,161,159],[168,159,192,211,161,159,161,161,159],[168,163,192,189,161,159,161,161,159],[168,159,196,211,161,159,161,161,159],[168,163,196,180,161,159,161,161,159],[168,159,200,211,161,159,161,161,159],[168,163,200,180,161,159,161,161,159],[168,159,204,211,161,159,161,161,159],[168,163,204,176,161,159,161,161,159],[168,159,207,211,161,159,161,161,159],[168,163,207,160,161,159,161,161,159],[184,159,159,225,161,159,161,161,159],[184,163,159,176,161,159,161,161,159],[184,159,163,225,161,159,161,161,159],[184,163,163,163,161,159,161,161,159],[184,159,170,225,161,159,161,161,159],[184,163,170,178,161,159,161,161,159],[184,159,175,225,161,159,161,161,159],[184,163,175,176,161,159,161,161,159],[184,159,168,225,161,159,161,161,159],[184,163,168,214,161,159,161,161,159],[184,159,184,225,161,159,161,161,159],[184,163,184,218,161,159,161,161,159],[184,159,188,313,161,159,161,161,159],0.1,[184,163,188,218,161,159,161,161,159],[184,159,192,225,161,159,161,161,159],[184,163,192,218,161,159,161,161,159],[184,159,196,225,161,159,161,161,159],[184,163,196,171,161,159,161,161,159],[184,159,200,225,161,159,161,161,159],[184,163,200,189,161,159,161,161,159],[184,159,204,211,161,159,161,161,159],[184,163,204,176,161,159,161,161,159],[184,159,207,225,161,159,161,161,159],[184,163,207,176,161,159,161,161,159],[],[105],[],[],[330],"KITTI odometry training set; relative errors averaged over 100-800 m trajectories; values written rot [deg\u002F100m] \u002F trans [%]; * = sequence contains loop closures; LOAM, S-LSD and SOFT-SLAM values as reported by their authors",{"slug":332,"group":333,"sourceId":334,"sourceLabel":335,"table":336,"selfRows":337,"metrics":338,"seqs":344,"entrants":374,"cells":380,"outcomes":515,"locators":516,"hardware":517,"wordings":518,"notes":519},"sumapp2019-table-i","sumapp2019:Table I","sumapp2019","Chen et al., 2019","Table I",26,[339,342],{"label":340,"unit":341,"statistic":111,"alignment":42},"relative rotational error","deg\u002F100 m",{"label":343,"unit":114,"statistic":111,"alignment":42},"relative translational error",[345,349,351,353,356,358,360,362,364,366,368,370,372],{"dataset":346,"sequence":347,"environment":348},"KITTI raw (road 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