[{"data":1,"prerenderedAt":100},["ShallowReactive",2],{"method-gtsam_software":3},{"method":4,"reference":43,"equipment":61,"figures":62,"results":63},{"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":25,"sensors":30,"platform":31,"estimator":32,"association":33,"timeModel":34,"deskew":33,"loopClosure":33,"globalOptimization":33,"mapRepresentation":33,"prior":33,"outputGeometry":33,"compute":35,"codeUrl":36,"codeLicense":37,"relatedVersions":38},"gtsam_software","Dellaert & GTSAM Contributors, 2026","GTSAM","GTSAM 4.3.0",2026,"classic","C03","estimation_framework_or_library","GTSAM 是以因子圖與 Bayes network 為運算範式（而非直接操作稀疏矩陣）的 C++ 平滑與建圖程式庫，提供 MATLAB 與 Python 包裝，實作批次最佳化、iSAM2 與固定延遲平滑器。README 說明其預積分 IMU 因子以 Lupton 與 Sukkarieh 的概念為基礎、依 Forster 等人的流形積分改良，而 GTSAM 4 預設改用在 NavState 切空間積分的實作（可用 GTSAM_TANGENT_PREINTEGRATION 切回 RSS 2015 版本）。4.3.0 版發行說明另列出連續時間高斯過程與 Lie 群樣條、GNSS 因子、riSAM 與 GNC 等穩健估計、平行多前沿求解器與實驗性 CUDA 加速；IMU 因子文件並說明可把重力當作最佳化變數，以用於未與重力對齊的 LiDAR 里程計地圖座標系。4.3.0 標籤內 README 的引用區塊仍指向較早的 Zenodo DOI 10.5281\u002Fzenodo.5794541，develop 分支才改為 4.3.0 版記錄。","GTSAM is the Georgia Tech factor-graph smoothing and mapping library (batch optimization, iSAM2, fixed-lag smoothing, preintegrated IMU factors); release 4.3.0 (Zenodo 10.5281\u002Fzenodo.22866773, 2026-09-18) adds continuous-time GP and spline factors, GNSS factors, robust riSAM and GNC, and experimental CUDA solvers.","full_text_reviewed","software_or_dataset_record","background","not_reported",[],[21,22,23,24],"Factor graphs and Bayes networks as the computing paradigm (README)","Includes a preintegrated IMU factor based on Lupton and Sukkarieh and Forster et al. (README)","Release 4.3.0 reports faster elimination, iSAM2 and IMU-integration code paths, GNSS factors and continuous-time trajectory support (release notes)","IMU factor documentation allows gravity to be optimized for navigation frames that are not gravity-aligned, such as LiDAR-odometry map frames (doc\u002FImuFactor.pdf)",[26,27,28,29],"README states that the default GTSAM 4 IMU factor uses a newer tangent-space implementation than the RSS 2015 paper version, so implementation and paper can differ (README, Preintegrated IMU Factor)","CUDA acceleration in 4.3 is described as experimental (release notes)","The citation block of the README inside the 4.3.0 tag points to the older DOI 10.5281\u002Fzenodo.5794541, so the version-specific DOI must be taken from Zenodo or the develop README","The development branch may include API changes; production use should pin a stable release (README)",[],[],"factor-graph smoothing and mapping library: batch nonlinear optimization (Levenberg-Marquardt named in the 4.3.0 notes), incremental iSAM2, IncrementalFixedLagSmoother (moved to the stable library in 4.3), EKF and invariant-EKF infrastructure, robust GNC and riSAM, constrained (LP, QP, QCQP) and certifiable solvers (README; 4.3.0 release notes)","not_applicable","discrete states by default; 4.3.0 adds continuous-time Gaussian-process (white-noise-on-acceleration) factors and Lie-group-aware differentiable splines (4.3.0 release notes)","C++17 library with MATLAB and Python wrappers; optional Boost, TBB-parallel multifrontal elimination, optional CHOLMOD and experimental CUDA solvers (README; 4.3.0 release notes)","https:\u002F\u002Fgithub.com\u002Fborglab\u002Fgtsam","BSD: LICENSE file calls it the 'simplified BSD license' but LICENSE.BSD contains 3-clause BSD text (Copyright 2010 Georgia Tech Research Corporation); README says 'BSD license'; DataCite lists BSD-3-Clause-Clear; bundled third-party code (e.g., METIS Apache-2.0, Eigen MPL2) keeps its own licenses",[39],{"relation":40,"title":41,"doi_or_url":42},"concept_record","Zenodo concept DOI for all GTSAM versions (IsVersionOf in DataCite)","10.5281\u002Fzenodo.22866772",{"id":5,"kind":44,"shortName":7,"title":8,"authors":45,"year":9,"venue":48,"venueType":44,"publisher":48,"volumeIssuePages":49,"doi":50,"arxivId":51,"url":52,"firstPublicDate":53,"publicationStatus":16,"metadataStatus":54,"fulltextStatus":15,"era":10,"classicReason":55,"codeUrl":36,"cluster":11,"topics":56,"mdpi":57,"verification":58,"label":6,"fulltextRoute":59,"versionRead":60,"addedByCensus":57},"software",[46,47],"Frank Dellaert","GTSAM Contributors","Zenodo","version 4.3.0","10.5281\u002Fzenodo.22866773",null,"https:\u002F\u002Fapi.datacite.org\u002Fdois\u002F10.5281\u002Fzenodo.22866773","not_verified","metadata_verified","reproducible baseline: the cited 4.3.0 record is dated 2026-09-18, but the library predates the recent window (the iSAM2 author draft dated 2011-04-06 states iSAM2 source code is available as part of the gtsam library); it implements iSAM2 and preintegrated IMU factors used by many systems.",[11],false,"corrected","other","GTSAM 4.3.0 git tag (commit 71a25ca36c): README.md, LICENSE, LICENSE.BSD and doc\u002FImuFactor.pdf (dated 2026-08-01); GitHub release notes for 4.3.0 (published 2026-09-18, narrative sections; the list of merged PRs was not read); Zenodo API and DataCite metadata for 10.5281\u002Fzenodo.22866773; develop-branch README compared for the citation block",[],[],{"totalRows":64,"groupCount":64,"groups":65,"others":99},1,[66],{"slug":67,"group":68,"sourceId":69,"sourceLabel":70,"table":71,"selfRows":64,"metrics":72,"seqs":77,"entrants":82,"cells":86,"outcomes":91,"locators":93,"hardware":95,"wordings":96,"notes":97},"dellaert2012gtsamtr-text-sec-5-3","dellaert2012gtsamtr:Text Sec.5.3","dellaert2012gtsamtr","Dellaert, 2012","Text Sec.5.3",[73],{"label":74,"unit":75,"statistic":18,"alignment":76},"optimization time","ms","none",[78],{"dataset":79,"sequence":80,"environment":81},"GTSAM example data (PlanarSLAMExample_graph)","119 variables, 517 factors","example graph file distributed with GTSAM (PlanarSLAMExample_graph); origin of the data not stated",[83],{"name":84,"methodId":5,"linkable":85,"proposed":57,"self":85},"GTSAM nonlinear optimization",true,[87],[88,88,88,89,88,88,90,90,88],0,10,-1,[92],"reported as less than 10 ms (upper bound)",[94],"Sec. 5.3; Fig. 12",[],[],[98],"Larger planar SLAM example read from a .graph file (about 100 poses and about 30 landmarks)",[],1790510654255]