GraphTinker: Outlier Rejection and Inlier Injection for Pose Graph SLAM

In pose graph Simultaneous Localization and Mapping (SLAM) systems, incorrect loop closures can seriously hinder optimizers from converging to correct solutions, significantly degrading both localization accuracy and map consistency. Therefore, it is crucial to enhance their robustness in the presence of numerous false-positive loop closures. Existing approaches tend to fail when working with very unreliable front-end systems, where the majority of inferred loop closures are incorrect. In this paper, we propose a novel middle layer, seamlessly embedded between front and back ends, to boost the robustness of the whole SLAM system. The main contributions of this paper are two-fold: 1) the proposed middle layer offers a new mechanism to reliably detect and remove false-positive loop closures, even if they form the overwhelming majority; 2) artificial loop closures are automatically reconstructed and injected into pose graphs in the framework of an Extended Rauch-Tung-Striebel smoother, reinforcing reliable loop closures. The proposed algorithm alters the graph generated by the front-end and can then be optimized by any back-end system. Extensive experiments are conducted to demonstrate significantly improved accuracy and robustness compared with state-of-the-art methods and various back-ends, verifying the effectiveness of the proposed algorithm.