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Robust Pose Graph Optimization Against Outliers Using Consistency Credibility Factor
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作者 Jie Cai Guoliang Wei +1 位作者 Wangyan Li Yaolei Wang 《IEEE/CAA Journal of Automatica Sinica》 2025年第5期1044-1046,共3页
Dear Editor,Pose graph optimization(PGO)is a popular optimization approach that plays a crucial role in the simultaneous localization and mapping(SLAM)back-end.However,when incorrect loop closure constraints(referred ... Dear Editor,Pose graph optimization(PGO)is a popular optimization approach that plays a crucial role in the simultaneous localization and mapping(SLAM)back-end.However,when incorrect loop closure constraints(referred to as outliers)are present in the SLAM front-end,the standard PGO algorithm fails catastrophically and can not return an accurate map.To address this issue,this letter proposes a novel algorithm that leverages classical optimization methods to effectively handle outliers.The proposed algorithm introduces a new formulation that incorporates a credibility factor model,which improves the robustness of the optimization process.Additionally,an innovative consistency classification algorithm is developed to detect outliers.Extensive experiments are conducted on multiple benchmark datasets to evaluate the consistency and accuracy of the proposed algorithm. 展开更多
关键词 graph optimization pgo pose graph optimization outliers consistency classification robustness optimization approach credibility factor classical optimization methods
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