Robust Pose Graph Optimization Against Outliers Using Consistency Credibility Factor

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. The results demonstrate that the proposed algorithm surpasses several existing studies in terms of the weighted summation of squared measurement errors (i.e., denoted by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$E_{G}^{2}$</tex>), highlighting its superior performance. Overall, the proposed algorithm presents a promising solution to mitigate the adverse effects of outliers in PGO, thereby enhancing the accuracy and reliability of the SLAM process.