PE-SLAM: A Modified Simultaneous Localization and Mapping System Based on Particle Swarm Optimization and Epipolar Constraints

Abstract

Due to various typical unstructured factors in the environment of photovoltaic power stations, such as high feature similarity, weak textures, and simple structures, the motion model of the ORB-SLAM2 algorithm performs poorly, leading to a decline in tracking accuracy. To address this issue, we propose PE-SLAM, which improves the ORB-SLAM2 algorithm’s motion model by incorporating the particle swarm optimization algorithm combined with epipolar constraint to eliminate mismatches. First, a new mutation strategy is proposed to introduce perturbations to the pbest (personal best value) during the late convergence stage of the PSO algorithm, thereby preventing the PSO algorithm from falling into local optima. Then, the improved PSO algorithm is used to solve the fundamental matrix between two images based on the feature matching relationships obtained from the motion model. Finally, the epipolar constraint is applied using the computed fundamental matrix to eliminate incorrect matches produced by the motion model, thereby enhancing the tracking accuracy and robustness of the ORB-SLAM2 algorithm in unstructured photovoltaic power station scenarios. In feature matching experiments, compared to the ORB algorithm and the ORB+HAMMING algorithm, the ORB+PE-match algorithm achieved an average accuracy improvement of 19.5%, 14.0%, and 6.0% in unstructured environments, respectively, with better recall rates. In the trajectory experiments of the TUM dataset, PE-SLAM reduced the average absolute trajectory error compared to ORB-SLAM2 by 29.1% and the average relative pose error by 27.0%. In the photovoltaic power station scene mapping experiment, the dense point cloud map constructed has less overlap and is complete, reflecting that PE-SLAM has basically overcome the unstructured factors of the photovoltaic power station scene and is suitable for applications in this scene.