Improvement methods for feature matching in Basalt SLAM

Visual simultaneous localization and mapping (SLAM) is widely used to provide localization information for mobile robots, but most visual SLAM algorithms lack effective data association methods, resulting in unstable position tracking results. In this paper, relevant improvement schemes for global feature matching in the Basalt SLAM algorithm are proposed to enhance the effectiveness of its data association method. Firstly, the more efficient box average difference (BAD) descriptor is used instead of the ORB descriptor. Secondly, secondary matching is performed by narrowing the matching range with the epipolar geometric constraint to expand matching pairs. Finally, grid-based motion statistics (GMS) and histogram-based rotation consistency constraints are introduced to eliminate false matches. Experiments on the EuRoC dataset demonstrate that the proposed methods significantly enhance the effectiveness and stability of visual data association and improve the localization accuracy of the Basalt SLAM system.