[{"data":1,"prerenderedAt":1092},["ShallowReactive",2],{"method-vinsfusion2019":3},{"method":4,"reference":51,"equipment":68,"figures":101,"results":102},{"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":22,"limitations":24,"sensors":29,"platform":33,"estimator":36,"association":37,"timeModel":38,"deskew":39,"loopClosure":40,"globalOptimization":41,"mapRepresentation":42,"prior":43,"outputGeometry":44,"compute":18,"codeUrl":45,"codeLicense":46,"relatedVersions":47},"vinsfusion2019","Qin et al., 2019","VINS-Fusion (local odometry framework)","A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors",2019,"recent","C08","odometry_with_local_mapping","此預印本提出以最佳化為核心的通用區域里程計框架，把每種感測器量測視為一個因子（factor），共享狀態變數的因子相加組成最佳化問題。論文以雙目、單眼加 IMU、雙目加 IMU 三種組合示範。其目標為區域精度，全域感測器如 GPS 被列為後續工作。","This preprint generalises optimisation-based local odometry by treating each sensor measurement as a factor, demonstrated with stereo, mono-inertial and stereo-inertial configurations.","full_text_reviewed","preprint","supplementary","not_reported",[20,21],"public_benchmark","controlled_experiment",[23],"keep; add: configurations with IMU tracked all EuRoC sequences and the framework outperformed OKVIS in most sequences (Table I); outdoor RMSE fell from 1.85 to 2.59 m (stereo) to 0.43 to 0.75 m with an IMU over about 224 to 232 m walks (Table II)",[25,26,27,28],"keep","add: stereo-only configuration failed on EuRoC V1_03 and V2_03 and drifted most (Table I, Sec. 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