A real-time visual SLAM based on semantic information and geometric information in dynamic environment

Simultaneous Localization and Mapping (SLAM) is the core technology enabling mobile robots to autonomously explore and perceive the environment. However, dynamic objects in the scene significantly impact the accuracy and robustness of visual SLAM systems, limiting its applicability in real-world scenarios. Hence, we propose a real-time RGB-D visual SLAM algorithm designed for indoor dynamic scenes. Our approach includes a parallel lightweight object detection thread, which leverages the YOLOv7-tiny network to detect potential moving objects and generate 2D semantic information. Subsequently, a novel dynamic feature removal strategy is introduced in the tracking thread. This strategy integrates semantic information, geometric constraints, and feature point depth-based RANSAC to effectively mitigate the influence of dynamic features. To evaluate the effectiveness of the proposed algorithms, we conducted comparative experiments using other state-of-the-art algorithms on the TUM RGB-D dataset and Bonn RGB-D dataset, as well as in real-world dynamic scenes. The results demonstrate that the algorithm maintains excellent accuracy and robustness in dynamic environments, while also exhibiting impressive real-time performance.