Year: 2025
Type: article
Abstract: Traditional visual SLAM systems are predominantly designed for static environments, where they encounter challenges in dynamic scenes, leading to increased system errors and redundancy. This paper introduces a dynamic feature detection and filtering algorithm. Through a feature point selection and optimization strategy within quadtree nodes, high-response feature points are prioritized. Semantic information is leveraged to remove features on prior dynamic objects, and geometric constraints are applied to filter truly dynamic features. For unmatched features, an extension method is used, and high-confidence points are weighted to obtain feature point status information. Compared with the standard ORB-SLAM2 algorithm, our improved algorithm achieves over a 90% performance increase in highly dynamic environments, with absolute trajectory error performance improvements up to 96.84% in low-dynamic settings. Overall, our algorithm demonstrates superior adaptability and robustness in dynamic environments.
Source: Scientific Reports
Authors: Yanli Liu, Yuting Wang, Heng Zhang, Qi Li
Institution: Shanghai Dianji University
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
Award: 基于粒子滤波的深度学习同步定位与地图构建研究
OpenAlex ID: W4413828153