Visual Simultaneous Localization and Mapping for Highly Dynamic Environments

This paper presents a visual simultaneous localization and mapping (SLAM) system designed for highly dynamic environments, capable of eliminating dynamic objects using only visual information. The proposed system integrates learning-based and geometry-based methods to address the challenges posed by moving objects. The learning-based approach leverages image segmentation to remove previously trained objects, whereas the geometry-based approach utilises point correlation to eliminate unseen objects. By complementing each other, these methods enhance the robustness of the SLAM system in dynamic scenarios. Experimental results demonstrate that the proposed method effectively removes dynamic objects. Comparative studies with state-of-the-art algorithms further show that the proposed method achieves superior accuracy and robustness.