[{"data":1,"prerenderedAt":586},["ShallowReactive",2],{"method-kaess2008isam":3},{"method":4,"reference":52,"equipment":72,"figures":103,"results":104},{"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":34,"platform":37,"estimator":39,"association":40,"timeModel":41,"deskew":42,"loopClosure":43,"globalOptimization":44,"mapRepresentation":45,"prior":46,"outputGeometry":47,"compute":48,"codeUrl":49,"codeLicense":50,"relatedVersions":51},"kaess2008isam","Kaess et al., 2008","iSAM","iSAM: Incremental Smoothing and Mapping",2008,"classic","C03","estimation_framework_or_library","iSAM 將 SLAM 表述為平滑（smoothing）問題並保留整條軌跡，使資訊矩陣維持自然稀疏；相對地，濾波在邊際化位姿時會使資訊矩陣變稠密。方法以增量更新平方根資訊矩陣（QR 分解）的方式，只重算受新量測影響的項目。遇到迴圈造成填充（fill-in）時，採週期性變數重排序並重新分解；另提供由分解因子有效取得邊際協方差的演算法，以支援即時資料關聯。","iSAM performs incremental updates of the square-root information matrix of the smoothing problem, with periodic reordering and relinearization, and efficient covariance recovery for data association.","full_text_reviewed","peer_reviewed_published","background","未在營建場域驗證。真實資料為公園（Victoria Park）、建物內（Intel）與 MIT Killian Court 的雷射資料集，只評估計算時間與平方根因子稀疏度；正規化 χ2 只用於模擬的 Manhattan 資料集，並未評估點雲或地圖幾何精度（Sec. VI）。結論僅提及未來可用於建物或城市尺度的即時建圖（Sec. VIII）。",[20,21,22],"simulation","public_benchmark","completed_building",[24,25,26,27,28],"Exact yet efficient solution of the linearized smoothing problem with access to marginal covariances (abstract; Sec. VIII)","Constant number of Givens rotations per step in pure exploration (Sec. III-C, Fig. 4)","Periodic reordering keeps the factor sparse on trajectories with many loops; one to three orders of magnitude faster than purely incremental or batch solutions in the 8-loop simulation (Sec. IV-A, Fig. 5)","Victoria Park with unknown data association solved in 464 s versus 26 min of recording, over 3 times faster than real time; final factor has 9.79 entries per column (Sec. VI-A)","On Manhattan the normalized chi-square after one extra relinearization equals the full nonlinear optimum (1.0375) (Sec. VI-B)",[30,31,32,33],"Requires periodic batch steps for variable reordering and relinearization, as stated by the follow-up iSAM2 paper (kaess2012isam2, Conclusion)","Authors list incremental variable ordering and incremental relinearization as open improvements (Sec. VIII)","Exact marginal covariance recovery becomes expensive in large environments and cannot be run every step; conservative estimates are used online (Sec. V-E)","Fill-in grows markedly on the Intel dataset (coarse run then detailed exploration with many redundant constraints), requiring a shorter reordering interval (Sec. VI-C, Fig. 12)",[35,36],"laser range data (Victoria Park: tree landmarks from a simple tree detector; Intel: pose constraints from scan matching; MIT Killian Court: data preprocessed into pose constraints)","vehicle odometry (Victoria Park)",[38,20],"vehicle","incremental QR update of the square-root information matrix by Givens rotations (new rows eliminated, new variables appended); periodic block COLAMD variable reordering followed by full refactorization every 100 steps (every 20 steps for the Intel dataset); relinearization performed only at these reordering steps; OCaml implementation with automatic differentiation","maximum likelihood data association: Mahalanobis-distance cost matrix solved as a minimum-cost assignment by the Jonker-Volgenant-Castanon algorithm, using marginal covariances recovered from the square-root factor either exactly (dynamic programming over non-zeros of R) or conservatively (initial landmark uncertainty); nearest neighbour evaluated for comparison; pose-only experiments assume known correspondences","discrete poses","not_applicable","handles loops in the trajectory; fill-in controlled by periodic reordering","full trajectory and map smoothing (exact solution of the linearized problem)","landmarks or pose-only graph","none","full trajectory and landmark map with access to marginal covariances (Victoria Park map has 140 distinct landmarks); for Intel and Killian Court the figures show the final trajectory with an evidence grid map (Figs. 10b, 11b)","real-time on 2 GHz Pentium M laptop in OCaml implementation (Sec. VI)",null,"not_verified",[],{"id":5,"kind":53,"shortName":7,"title":8,"authors":54,"year":9,"venue":58,"venueType":59,"publisher":60,"volumeIssuePages":61,"doi":62,"arxivId":49,"url":63,"firstPublicDate":64,"publicationStatus":16,"metadataStatus":65,"fulltextStatus":15,"era":10,"classicReason":66,"codeUrl":49,"cluster":11,"topics":67,"mdpi":68,"verification":69,"label":6,"fulltextRoute":70,"versionRead":71,"addedByCensus":68},"method",[55,56,57],"Michael Kaess","Ananth Ranganathan","Frank Dellaert","IEEE Transactions on Robotics","journal","IEEE","24(6):1365-1378","10.1109\u002Ftro.2008.2006706","https:\u002F\u002Fapi.crossref.org\u002Fworks\u002F10.1109\u002FTRO.2008.2006706","2008-12","metadata_verified","necessary technical node: first incremental square-root smoothing method in the sqrt-SAM to iSAM to iSAM2 lineage that modern factor-graph LiDAR and VIO back-ends build on.",[11],false,"corrected","author copy","Authors' manuscript 'IEEE Transactions on Robotics, manuscript September 7, 2008' (Kaess08tro.pdf, 14 pages, accepted as a regular paper), byte-identical (MD5 6bdb3dfc57536d6616ec8797d061e1a6) to the Georgia Tech repository post-print (hdl 1853\u002F38262); the typeset IEEE version of record was not opened",[73,79,86,90,94,97],{"category":74,"model":75,"canonical":75,"role":76,"dataset":49,"specs":77,"locator":78},"compute","Pentium M","compute for runtime","2 GHz; laptop computer; timings of the OCaml implementation","Sec. VI",{"category":80,"model":81,"canonical":81,"role":82,"dataset":83,"specs":84,"locator":85},"lidar","laser range sensor (model not stated)","dataset sensor","Sydney Victoria Park","laser-range data; 3640 tree landmark measurements extracted","Sec. VI-A",{"category":87,"model":88,"canonical":88,"role":82,"dataset":83,"specs":89,"locator":85},"wheel_or_leg_odometry","vehicle odometry (sensor not stated)","not_reported",{"category":80,"model":81,"canonical":81,"role":82,"dataset":91,"specs":92,"locator":93},"Intel dataset","laser range data preprocessed by scan matching into 910 poses and 4453 constraints","Sec. VI-B",{"category":80,"model":81,"canonical":81,"role":82,"dataset":95,"specs":96,"locator":93},"MIT Killian Court","laser range data preprocessed into 1941 poses and 2190 pose constraints",{"category":98,"model":99,"canonical":99,"role":100,"dataset":83,"specs":101,"locator":102},"gnss","Differential GPS (receiver not stated)","reference or ground truth","shown in Fig. 8 only for visual comparison; not used to obtain the results; unavailable in many places","Fig. 8 caption",[],{"totalRows":105,"groupCount":106,"groups":107,"others":569},70,7,[108,419,476,528],{"slug":109,"group":110,"sourceId":111,"sourceLabel":112,"table":113,"selfRows":114,"metrics":115,"seqs":129,"entrants":150,"cells":160,"outcomes":404,"locators":405,"hardware":406,"wordings":408,"notes":409},"kaess2012isam2-table-1","kaess2012isam2:Table 1","kaess2012isam2","Kaess et al., 2012","Table 1",36,[116,120,123,126],{"label":117,"unit":118,"statistic":119,"alignment":42},"average time per step","ms","mean",{"label":121,"unit":118,"statistic":122,"alignment":42},"standard deviation of time per step","std",{"label":124,"unit":118,"statistic":125,"alignment":42},"maximum time per step","max",{"label":127,"unit":128,"statistic":89,"alignment":42},"overall time","s",[130,133,135,137,140,142,144,146,148],{"dataset":131,"sequence":132,"environment":20},"City20000","full sequence",{"dataset":134,"sequence":132,"environment":20},"W10000",{"dataset":136,"sequence":132,"environment":20},"Manhattan",{"dataset":138,"sequence":132,"environment":139},"Intel","real laser range data",{"dataset":141,"sequence":132,"environment":139},"Killian Court",{"dataset":143,"sequence":132,"environment":139},"Victoria Park",{"dataset":145,"sequence":132,"environment":20},"Trees10000",{"dataset":147,"sequence":132,"environment":20},"Sphere2500",{"dataset":149,"sequence":132,"environment":20},"Torus10000",[151,154,156,158],{"name":152,"methodId":111,"linkable":153,"proposed":153,"self":68},"iSAM2",true,{"name":155,"methodId":5,"linkable":153,"proposed":68,"self":153},"iSAM1",{"name":157,"methodId":49,"linkable":68,"proposed":68,"self":68},"HOG-Man",{"name":159,"methodId":49,"linkable":68,"proposed":68,"self":68},"SPA",[161,165,168,171,174,176,178,180,182,184,186,188,190,192,194,196,198,200,202,204,206,208,210,212,214,216,218,220,222,224,226,228,230,232,234,236,238,240,242,244,246,247,249,251,253,255,257,259,261,263,265,267,269,271,273,275,277,279,281,283,285,287,289,290,292,295,297,298,300,302,304,306,308,309,311,312,314,316,318,320,322,325,327,329,331,333,335,337,338,341,343,345,347,349,351,353,355,357,359,361,363,365,367,369,371,373,375,377,379,382,384,386,388,390,392,394,396,398,400,402],[162,162,162,163,164,162,162,164,162],0,16.1,-1,[162,166,162,167,164,162,162,164,162],1,65.6,[162,169,162,170,164,162,162,164,162],2,1125,[162,172,162,173,164,162,162,164,162],3,323,[166,162,162,175,164,162,162,164,162],7.05,[166,166,162,177,164,162,162,164,162],14.5,[166,169,162,179,164,162,162,164,162],308,[166,172,162,181,164,162,162,164,162],141,[169,162,162,183,164,162,162,164,162],27.4,[169,166,162,185,164,162,162,164,162],27.8,[169,169,162,187,164,162,162,164,162],146,[169,172,162,189,164,162,162,164,162],548,[172,162,162,191,164,162,162,164,162],48.7,[172,166,162,193,164,162,162,164,162],32.6,[172,169,162,195,164,162,162,164,162],140,[172,172,162,197,164,162,162,164,162],977,[162,162,166,199,164,162,162,164,166],22.4,[162,166,166,201,164,162,162,164,166],64.6,[162,169,166,203,164,162,162,164,166],901,[162,172,166,205,164,162,162,164,166],224,[166,162,166,207,164,162,162,164,166],35.7,[166,166,166,209,164,162,162,164,166],58.8,[166,169,166,211,164,162,162,164,166],683,[166,172,166,213,164,162,162,164,166],357,[169,162,166,215,164,162,162,164,166],16.4,[169,166,166,217,164,162,162,164,166],14.9,[169,169,166,219,164,162,162,164,166],147,[169,172,166,221,164,162,162,164,166],164,[172,162,166,223,164,162,162,164,166],108,[172,166,166,225,164,162,162,164,166],75.6,[172,169,166,227,164,162,162,164,166],287,[172,172,166,229,164,162,162,164,166],1081,[162,162,169,231,164,162,162,164,169],2.44,[162,166,169,233,164,162,162,164,169],7.71,[162,169,169,235,164,162,162,164,169],133,[162,172,169,237,164,162,162,164,169],8.54,[166,162,169,239,164,162,162,164,169],1.81,[166,166,169,241,164,162,162,164,169],3.69,[166,169,169,243,164,162,162,164,169],57.6,[166,172,169,245,164,162,162,164,169],6.35,[169,162,169,233,164,162,162,164,169],[169,166,169,248,164,162,162,164,169],6.91,[169,169,169,250,164,162,162,164,169],33.8,[169,172,169,252,164,162,162,164,169],27,[172,162,169,254,164,162,162,164,169],11.8,[172,166,169,256,164,162,162,164,169],8.46,[172,169,169,258,164,162,162,164,169],28.9,[172,172,169,260,164,162,162,164,169],41.1,[162,162,172,262,164,162,162,164,172],1.74,[162,166,172,264,164,162,162,164,172],1.76,[162,169,172,266,164,162,162,164,172],9.13,[162,172,172,268,164,162,162,164,172],1.59,[166,162,172,270,164,162,162,164,172],5.8,[166,166,172,272,164,162,162,164,172],8.03,[166,169,172,274,164,162,162,164,172],48.4,[166,172,172,276,164,162,162,164,172],5.28,[169,162,172,278,164,162,162,164,172],9.4,[169,166,172,280,164,162,162,164,172],12.5,[169,169,172,282,164,162,162,164,172],79.3,[169,172,172,284,164,162,162,164,172],8.55,[172,162,172,286,164,162,162,164,172],4.89,[172,166,172,288,164,162,162,164,172],3.77,[172,169,172,217,164,162,162,164,172],[172,172,172,291,164,162,162,164,172],4.44,[162,162,293,294,164,162,162,164,293],4,0.59,[162,166,293,296,164,162,162,164,293],0.8,[162,169,293,280,164,162,162,164,293],[162,172,293,299,164,162,162,164,293],1.15,[166,162,293,301,164,162,162,164,293],0.51,[166,166,293,303,164,162,162,164,293],1.13,[166,169,293,305,164,162,162,164,293],16.6,[166,172,293,307,164,162,162,164,293],0.99,[169,162,293,169,164,162,162,164,293],[169,166,293,310,164,162,162,164,293],2.41,[169,169,293,254,164,162,162,164,293],[169,172,293,313,164,162,162,164,293],3.88,[172,162,293,315,164,162,162,164,293],3.13,[172,166,293,317,164,162,162,164,293],1.89,[172,169,293,319,164,162,162,164,293],7.98,[172,172,293,321,164,162,162,164,293],6.07,[162,162,323,324,164,162,162,164,323],5,2.34,[162,166,323,326,164,162,162,164,323],7.75,[162,169,323,328,164,162,162,164,323],316,[162,172,323,330,164,162,162,164,323],16.3,[166,162,323,332,164,162,162,164,323],2.35,[166,166,323,334,164,162,162,164,323],4.82,[166,169,323,336,164,162,162,164,323],80.4,[166,172,323,215,164,162,162,164,323],[162,162,339,340,164,162,162,164,339],6,4.24,[162,166,339,342,164,162,162,164,339],6.52,[162,169,339,344,164,162,162,164,339],124,[162,172,339,346,164,162,162,164,339],42.4,[166,162,339,348,164,162,162,164,339],2.98,[166,166,339,350,164,162,162,164,339],6.7,[166,169,339,352,164,162,162,164,339],114,[166,172,339,354,164,162,162,164,339],29.8,[162,162,106,356,164,162,162,164,106],30.4,[162,166,106,358,164,162,162,164,106],25.5,[162,169,106,360,164,162,162,164,106],158,[162,172,106,362,164,162,162,164,106],76,[166,162,106,364,164,162,162,164,106],21.7,[166,166,106,366,164,162,162,164,106],31.3,[166,169,106,368,164,162,162,164,106],679,[166,172,106,370,164,162,162,164,106],54.3,[169,162,106,372,164,162,162,164,106],56.7,[169,166,106,374,164,162,162,164,106],40.8,[169,169,106,376,164,162,162,164,106],159,[169,172,106,378,164,162,162,164,106],142,[162,162,380,381,164,162,162,164,380],8,35.2,[162,166,380,383,164,162,162,164,380],45.7,[162,169,380,385,164,162,162,164,380],487,[162,172,380,387,164,162,162,164,380],352,[166,162,380,389,164,162,162,164,380],86.4,[166,166,380,391,164,162,162,164,380],119,[166,169,380,393,164,162,162,164,380],1824,[166,172,380,395,164,162,162,164,380],864,[169,162,380,397,164,162,162,164,380],99,[169,166,380,399,164,162,162,164,380],82.9,[169,169,380,401,164,162,162,164,380],404,[169,172,380,403,164,162,162,164,380],990,[],[113],[407],"laptop with Intel 1.6 GHz i7-720; iSAM2 single-threaded research C++ (alpha 0.001, beta 0.1, relinearization every 10 steps); iSAM1 v1.6 standard parameters; HOG-Man svn rev 14 with -update 1; SPA from ROS svn rev 36438",[],[410,411,412,413,414,415,416,417,418],"Runtime comparison (iSAM1 and HOG-Man set to solve in every step, SPA with standard parameters, iSAM2 relinearizing every 10 steps); per-step average, standard deviation and maximum in ms and overall time in s (P poses, M measurements, L landmarks); P 20000, M 26770, simulated 2D pose graph","Runtime comparison (iSAM1 and HOG-Man set to solve in every step, SPA with standard parameters, iSAM2 relinearizing every 10 steps); per-step average, standard deviation and maximum in ms and overall time in s (P poses, M measurements, L landmarks); P 10000, M 64311, simulated 2D pose graph","Runtime comparison (iSAM1 and HOG-Man set to solve in every step, SPA with standard parameters, iSAM2 relinearizing every 10 steps); per-step average, standard deviation and maximum in ms and overall time in s (P poses, M measurements, L landmarks); P 3500, M 5598, simulated 2D pose graph","Runtime comparison (iSAM1 and HOG-Man set to solve in every step, SPA with standard parameters, iSAM2 relinearizing every 10 steps); per-step average, standard deviation and maximum in ms and overall time in s (P poses, M measurements, L landmarks); P 910, M 4453, 2D pose graph from laser range data","Runtime comparison (iSAM1 and HOG-Man set to solve in every step, SPA with standard parameters, iSAM2 relinearizing every 10 steps); per-step average, standard deviation and maximum in ms and overall time in s (P poses, M measurements, L landmarks); P 1941, M 2190, 2D pose graph from laser range data","Runtime comparison (iSAM1 and HOG-Man set to solve in every step, SPA with standard parameters, iSAM2 relinearizing every 10 steps); per-step average, standard deviation and maximum in ms and overall time in s (P poses, M measurements, L landmarks); P 6969, M 10608, L 151, 2D landmarks from laser range data","Runtime comparison (iSAM1 and HOG-Man set to solve in every step, SPA with standard parameters, iSAM2 relinearizing every 10 steps); per-step average, standard deviation and maximum in ms and overall time in s (P poses, M measurements, L landmarks); P 10000, M 14442, L 100, simulated 2D landmarks","Runtime comparison (iSAM1 and HOG-Man set to solve in every step, SPA with standard parameters, iSAM2 relinearizing every 10 steps); per-step average, standard deviation and maximum in ms and overall time in s (P poses, M measurements, L landmarks); P 2500, M 4950, simulated 3D pose graph","Runtime comparison (iSAM1 and HOG-Man set to solve in every step, SPA with standard parameters, iSAM2 relinearizing every 10 steps); per-step average, standard deviation and maximum in ms and overall time in s (P poses, M measurements, L landmarks); P 10000, M 22281, simulated 3D pose graph",{"slug":420,"group":421,"sourceId":5,"sourceLabel":6,"table":422,"selfRows":423,"metrics":424,"seqs":431,"entrants":435,"cells":444,"outcomes":469,"locators":470,"hardware":471,"wordings":473,"notes":474},"kaess2008isam-table-i","kaess2008isam:Table I","Table I",12,[425,427,429],{"label":426,"unit":128,"statistic":89,"alignment":42},"overall execution time",{"label":428,"unit":118,"statistic":119,"alignment":42},"average execution time per step",{"label":430,"unit":118,"statistic":125,"alignment":42},"maximum execution time per step",[432],{"dataset":433,"sequence":434,"environment":20},"simulated loop (500 poses, 240 landmarks)","full run",[436,438,440,442],{"name":437,"methodId":5,"linkable":153,"proposed":68,"self":153},"NN",{"name":439,"methodId":5,"linkable":153,"proposed":153,"self":153},"ML conservative",{"name":441,"methodId":5,"linkable":153,"proposed":153,"self":153},"ML exact, efficient",{"name":443,"methodId":5,"linkable":153,"proposed":68,"self":153},"ML exact, full",[445,447,449,451,453,455,457,459,461,463,465,467],[162,162,162,446,164,162,162,164,162],2.03,[162,166,162,448,164,162,162,164,162],4.1,[162,169,162,450,164,162,162,164,162],81,[166,162,162,452,164,162,162,164,162],2.8,[166,166,162,454,164,162,162,164,162],5.6,[166,169,162,456,164,162,162,164,162],95,[169,162,162,458,164,162,162,164,162],27.5,[169,166,162,460,164,162,162,164,162],55,[169,169,162,462,164,162,162,164,162],304,[172,162,162,464,164,162,162,164,162],429,[172,166,162,466,164,162,162,164,162],858,[172,169,162,468,164,162,162,164,162],3300,[],[422],[472],"not stated for this experiment (Sec. VI names a 2 GHz Pentium M laptop only for the Sec. VI timings); OCaml implementation",[],[475],"Simulated loop with 500 poses and 240 landmarks, significant measurement noise, 3% of measurements replaced by random ones; times include factor update, solving for all variables and the data association step, for every step",{"slug":477,"group":478,"sourceId":5,"sourceLabel":6,"table":479,"selfRows":480,"metrics":481,"seqs":487,"entrants":498,"cells":500,"outcomes":519,"locators":520,"hardware":521,"wordings":523,"notes":524},"kaess2008isam-text-sec-vi-b-timing","kaess2008isam:Text Sec.VI-B timing","Text Sec.VI-B timing",9,[482,484,485],{"label":483,"unit":128,"statistic":89,"alignment":42},"total computation time",{"label":117,"unit":118,"statistic":119,"alignment":42},{"label":486,"unit":118,"statistic":119,"alignment":42},"average time per step over the last 100 steps",[488,490,492,494,495,497],{"dataset":489,"sequence":132,"environment":20},"Manhattan world (Olson et al.)",{"dataset":489,"sequence":491,"environment":20},"final 100 steps",{"dataset":91,"sequence":132,"environment":493},"building (publicly available laser range dataset)",{"dataset":91,"sequence":491,"environment":493},{"dataset":95,"sequence":132,"environment":496},"real laser range data (public dataset)",{"dataset":95,"sequence":491,"environment":496},[499],{"name":7,"methodId":5,"linkable":153,"proposed":153,"self":153},[501,503,505,507,509,511,513,515,517],[162,162,162,502,164,162,162,164,162],140.9,[162,166,162,504,164,162,162,164,162],40,[162,169,166,506,164,162,162,164,162],48,[162,162,169,508,164,162,162,164,166],77.4,[162,166,169,510,164,162,162,164,166],85,[162,169,172,512,164,162,162,164,166],290,[162,162,293,514,164,162,162,164,169],23.7,[162,166,293,516,164,162,162,164,169],12.2,[162,169,323,518,164,162,162,164,169],31,[],[93],[522],"2 GHz Pentium M laptop; OCaml implementation",[],[525,526,527],"Pose-only iSAM with known data association, full solution after each step; 3500 poses, 5598 constraints; reordering and relinearization every 100 steps","Pose-only iSAM with known data association, full solution after each step; 910 poses, 4453 constraints after scan matching; reordering every 20 frames","Pose-only iSAM with known data association, full solution after each step; 1941 poses, 2190 pose constraints; reordering every 100 steps",{"slug":529,"group":530,"sourceId":5,"sourceLabel":6,"table":531,"selfRows":339,"metrics":532,"seqs":545,"entrants":549,"cells":551,"outcomes":562,"locators":563,"hardware":564,"wordings":565,"notes":566},"kaess2008isam-text-sec-vi-a","kaess2008isam:Text Sec.VI-A","Text Sec.VI-A",[533,535,537,539,541,543],{"label":534,"unit":128,"statistic":89,"alignment":42},"total time, unknown correspondences, solving after every frame",{"label":536,"unit":128,"statistic":89,"alignment":42},"total time, unknown correspondences, solving every 10 steps",{"label":538,"unit":128,"statistic":89,"alignment":42},"total time, known correspondences, solving after every frame",{"label":540,"unit":128,"statistic":89,"alignment":42},"total time, known correspondences, solving every 10 steps",{"label":542,"unit":118,"statistic":119,"alignment":42},"average time per step over the final 100 steps, with data association, full solution each step",{"label":544,"unit":118,"statistic":119,"alignment":42},"average time per step over the final 100 steps, known correspondences, full solution each step",[546,548],{"dataset":143,"sequence":132,"environment":547},"park with sparse tree coverage (popular SLAM test dataset)",{"dataset":143,"sequence":491,"environment":547},[550],{"name":7,"methodId":5,"linkable":153,"proposed":153,"self":153},[552,554,556,558,559,561],[162,162,162,553,164,162,162,164,162],464,[162,166,162,555,164,162,162,164,162],270,[162,169,162,557,164,162,162,164,162],351,[162,172,162,376,164,162,162,164,162],[162,293,166,560,164,162,162,164,166],120,[162,323,166,456,164,162,162,164,166],[],[85],[522],[],[567,568],"Sydney Victoria Park: 6969 of 7247 frames retained, 3640 landmark measurements from a tree detector, 4 km, 26 min recording; unknown correspondences use ML data association with conservative estimates","Sydney Victoria Park: 6969 of 7247 frames retained, 3640 landmark measurements from a tree detector, 4 km, 26 min recording; unknown correspondences use ML data association with conservative estimates; 0.22 s per step would be needed for real time",[570,575,580],{"group":571,"slug":572,"sourceLabel":6,"table":573,"selfRows":293,"datasets":574},"kaess2008isam:Text sparsity","kaess2008isam-text-sparsity","Text sparsity",[91,95,489,143],{"group":576,"slug":577,"sourceLabel":6,"table":578,"selfRows":169,"datasets":579},"kaess2008isam:Text Sec.VI-B Manhattan","kaess2008isam-text-sec-vi-b-manhattan","Text Sec.VI-B Manhattan",[489],{"group":581,"slug":582,"sourceLabel":6,"table":583,"selfRows":166,"datasets":584},"kaess2008isam:Text Sec.III-C","kaess2008isam-text-sec-iii-c","Text Sec.III-C",[585],"simulated linear exploration",1790510662858]