[{"data":1,"prerenderedAt":171},["ShallowReactive",2],{"method-slamtoolbox2021":3},{"method":4,"reference":61,"equipment":83,"figures":96,"results":129},{"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":27,"sensors":32,"platform":35,"estimator":37,"association":38,"timeModel":39,"deskew":40,"loopClosure":41,"globalOptimization":42,"mapRepresentation":43,"prior":44,"outputGeometry":45,"compute":46,"codeUrl":47,"codeLicense":48,"relatedVersions":49},"slamtoolbox2021","Macenski & Jambrecic, 2021","SLAM Toolbox","SLAM Toolbox: SLAM for the dynamic world",2021,"recent","C01","full_slam_with_global_correction","SLAM Toolbox 是以 SRI 的 Open Karto 為基礎的 ROS 2D 雷射位姿圖 SLAM 套件，提供同步建圖、非同步建圖與純定位三種模式，也支援多次作業（multi-session）建圖。它把完整的原始掃描與位姿圖一起序列化，因此可以日後載入並繼續精修或擴充地圖，也能手動調整位姿圖節點、協助困難的迴圈閉合，或以運動學方式合併多張地圖。純定位模式把目前作業的量測以滾動緩衝加入原位姿圖，舊量測過期後即移除，作者稱為彈性位姿圖變形。作者並把原本的 SBA 最佳化介面改為 Ceres 外掛，重寫量測匹配以取得約十倍加速。","ROS and ROS 2 2D laser pose-graph SLAM built on Open Karto with Ceres optimization, serialization of raw scans and pose graph for multi-session mapping, manual graph editing, map merging and rolling-buffer pure localization.","full_text_reviewed","peer_reviewed_published","supplementary","未在營建場域驗證，論文也沒有定量精度評估。作者以零售賣場、倉庫與大型辦公建築為目標，並報告即時繪製達 24,000 m2 的空間（Summary）；多次作業建圖與手動修正位姿圖的功能，與大型建築室內分區、分次掃描後整合平面地圖的需求相關（推論）。GitHub README 另稱曾以同步模式繪製 200,000 平方英尺的建築，這屬軟體說明，並非同儕審查的結果。",[20],"completed_building",[22,23,24,25,26],"Mapped spaces as large as 24,000 m2 (250,000 ft2) in real time by non-expert technicians (Summary).","Serializing the complete raw data and pose graph enables multi-session refinement, manual pose-graph manipulation and kinematic map merging (Features; Figs. 2-3).","Selected as the default SLAM vendor in ROS 2, replacing GMapping, and integrated into Navigation2 (Summary).","Measurement matching restructured for a 10x speed-up with multi-threading; optimizer exposed as a plugin (Features).","The authors argue GMapping fails to close loops accurately at industrial scale and Hector SLAM lacks loop closure, motivating a graph-based approach (Related Work).",[28,29,30,31],"No quantitative accuracy or runtime evaluation in the paper (whole paper) (inference)","Pure localization mode cannot persist environment changes; buffered measurements expire (Features)","Asynchronous mode may omit valid measurements when processing lags (Features)","Planar 2D mapping only (repository README; the paper does not say 2D explicitly)",[33,34],"laser scanner (paper); planar 2D scans per the repository README, which the paper itself does not state","wheel or other odometry supplied as the odom-to-base transform (repository README; not stated in the paper)",[36],"wheeled mobile robots (deployment list only; no controlled experiment is reported)","graph-based SLAM derived from Open Karto; the provided Sparse Bundle Adjustment optimization interface was replaced by Google Ceres behind a run-time dynamically loaded optimizer plugin interface (Features)","Open Karto measurement matching, restructured for about a 10x speed-up and multi-threading; new processing modes and K-D tree search for localization and multi-session mapping (Features); the matching algorithm itself is not described","discrete poses","not_reported","loop closures inside the pose graph, including between separate mapping sessions (Fig. 3); detection method not described in the paper","pose-graph optimization with Ceres through an optimizer plugin (Features)","pose graph with the complete raw scan data serialized (not submaps as in Cartographer); 2D maps rendered for navigation (Figs. 1, 3)","optional prior session(s): a deserialized pose graph and scans for multi-session mapping or pure localization","2D map for navigation and localization; serialized pose graph and raw scans","real time on mobile Intel CPUs typical of mobile robots; spaces well in excess of 100,000 ft2 and up to 24,000 m2 (250,000 ft2) mapped in real time (Summary; Features)","https:\u002F\u002Fgithub.com\u002FSteveMacenski\u002Fslam_toolbox","LGPL-2.1 (stated in the paper Summary; LICENSE file checked)",[50,53,57],{"relation":51,"title":52,"doi_or_url":47},"code_release","SteveMacenski\u002Fslam_toolbox (LGPL-2.1)",{"relation":54,"title":55,"doi_or_url":56},"talk","Macenski (2019) On use of SLAM Toolbox, ROSCon 2019 (source credited in the figure captions)","https:\u002F\u002Fdoi.org\u002F10.36288\u002Froscon2019-900903",{"relation":58,"title":59,"doi_or_url":60},"builds_on","Open Karto \u002F Karto SLAM (karto_spa2010)","https:\u002F\u002Fdoi.org\u002F10.1109\u002FIROS.2010.5649043",{"id":5,"kind":62,"shortName":7,"title":8,"authors":63,"year":9,"venue":66,"venueType":67,"publisher":68,"volumeIssuePages":69,"doi":70,"arxivId":71,"url":72,"firstPublicDate":73,"publicationStatus":16,"metadataStatus":74,"fulltextStatus":15,"era":10,"classicReason":75,"codeUrl":47,"cluster":11,"topics":76,"mdpi":78,"verification":79,"label":6,"fulltextRoute":80,"versionRead":81,"addedByCensus":82},"method",[64,65],"Steve Macenski","Ivona Jambrecic","Journal of Open Source Software","journal","Open Journals (JOSS)","6(61):2783","10.21105\u002Fjoss.02783",null,"https:\u002F\u002Fdoi.org\u002F10.21105\u002Fjoss.02783","2021-05-13","metadata_verified","not_applicable",[11,77],"C04",false,"corrected","publisher OA","JOSS version of record (published 2021-05-13), CC BY 4.0",true,[84,90],{"category":85,"model":86,"canonical":86,"role":87,"dataset":71,"specs":88,"locator":89},"compute","mobile Intel CPUs typically found on robots (models not reported)","compute for runtime","real-time mapping of spaces well over 100,000 ft2","Features",{"category":91,"model":92,"canonical":92,"role":93,"dataset":71,"specs":94,"locator":95},"lidar","laser scanner (model not reported)","method input","laser scans are the mapping input (scan matching, pose-graph nodes, manual scan-to-map matching in Fig. 2); 2D per the repository README, not stated in the paper","Summary; Features; Fig. 2",[97,110,120],{"refId":5,"refLabel":6,"fig":98,"whatZh":99,"license":100,"licenseUrl":101,"sourceUrl":102,"src":103,"width":104,"height":105,"thumb":106,"thumbWidth":107,"thumbHeight":108,"modified":109},"Fig. 1","以 SLAM Toolbox 建立的零售賣場二維地圖（圖源為作者 2019 年 ROSCon 報告）","CC BY 4.0","http:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby\u002F4.0\u002F","https:\u002F\u002Fraw.githubusercontent.com\u002Fopenjournals\u002Fjoss-papers\u002Fmaster\u002Fjoss.02783\u002Fmedia\u002Fstore_map.png","\u002Ffigure-files\u002Fslamtoolbox2021\u002Ffig-1.webp",1400,809,"\u002Ffigure-files\u002Fslamtoolbox2021\u002Ffig-1.thumb.webp",480,277,"resized to at most 1400 px wide and converted to WebP",{"refId":5,"refLabel":6,"fig":111,"whatZh":112,"license":100,"licenseUrl":101,"sourceUrl":113,"src":114,"width":115,"height":116,"thumb":117,"thumbWidth":107,"thumbHeight":118,"modified":119},"Fig. 2","手動調整位姿圖節點、使該節點雷射掃描對齊地圖的操作畫面","https:\u002F\u002Fraw.githubusercontent.com\u002Fopenjournals\u002Fjoss-papers\u002Fmaster\u002Fjoss.02783\u002Fmedia\u002Futils.png","\u002Ffigure-files\u002Fslamtoolbox2021\u002Ffig-2.webp",600,439,"\u002Ffigure-files\u002Fslamtoolbox2021\u002Ffig-2.thumb.webp",351,"converted to WebP",{"refId":5,"refLabel":6,"fig":121,"whatZh":122,"license":100,"licenseUrl":101,"sourceUrl":123,"src":124,"width":125,"height":126,"thumb":127,"thumbWidth":107,"thumbHeight":128,"modified":119},"Fig. 3","分兩次作業建立並合併的大型辦公大樓地圖，兩批資料之間有多個迴圈閉合","https:\u002F\u002Fraw.githubusercontent.com\u002Fopenjournals\u002Fjoss-papers\u002Fmaster\u002Fjoss.02783\u002Fmedia\u002Fcircuit_launch.png","\u002Ffigure-files\u002Fslamtoolbox2021\u002Ffig-3.webp",1393,1613,"\u002Ffigure-files\u002Fslamtoolbox2021\u002Ffig-3.thumb.webp",556,{"totalRows":130,"groupCount":131,"groups":132,"others":170},2,1,[133],{"slug":134,"group":135,"sourceId":5,"sourceLabel":6,"table":136,"selfRows":130,"metrics":137,"seqs":145,"entrants":151,"cells":155,"outcomes":162,"locators":163,"hardware":165,"wordings":167,"notes":168},"slamtoolbox2021-text-summary-and-features","slamtoolbox2021:Text Summary and Features","Text Summary and Features",[138,142],{"label":139,"unit":140,"statistic":141,"alignment":75},"area mapped in real time by non-expert technicians","m2","max",{"label":143,"unit":144,"statistic":40,"alignment":75},"speed-up of restructured measurement matching relative to Open Karto","x (factor)",[146,150],{"dataset":147,"sequence":148,"environment":149},"not_applicable (deployments)","largest mapped space","retail and warehouse type indoor spaces",{"dataset":75,"sequence":75,"environment":75},[152,153],{"name":7,"methodId":5,"linkable":82,"proposed":82,"self":82},{"name":154,"methodId":5,"linkable":82,"proposed":82,"self":82},"SLAM Toolbox vs Open Karto",[156,160],[157,157,157,158,159,157,157,159,157],0,24000,-1,[131,131,131,161,159,131,157,159,157],10,[],[164,89],"Summary",[166],"not reported",[],[169],"Scale and speed statements without a controlled benchmark",[],1790510661941]