A semantic visual SLAM towards object selection and tracking optimization.

Simultaneous localization and mapping (SLAM) technology has garnered considerable attention as a pivotal component for the autonomous navigation of intelligent mobile vehicles. Integrating target detection and target tracking technology into SLAM enhances scene perception, resulting in a more resilient SLAM system. Consequently, this article presents a pose optimization algorithm based on image segmentation, coupled with object detection technology, to achieve superior multi-frame association feature matching. Subsequently, this paper proposes a method for selecting the most stable targets to better conduct pose optimization. Finally, experimental validation was conducted on five sequences from the TUM dataset. We conducted tracking performance experiments to demonstrate the necessity of selecting stable targets for pose optimization. Afterwards, we carried out a comprehensive comparison with the current state-of-the-art SLAM implementations in terms of accuracy and robustness. The average absolute trajectory error of our method in the dynamic benchmark datasets is ∼ 94.14% lower than that of ORB-SLAM2, ∼ 61.90% lower than that of RS-SLAM, and ∼ 80.89% lower than that of DS-SLAM. At the end of the experiment, the process performance of the proposed method is demonstrated. The experiments collectively showcase the system's capability to deliver outstanding results. [ABSTRACT FROM AUTHOR]