Real-Time Dynamic SLAM Algorithm Based on Deep Learning

In the traditional visual simultaneous localization and mapping (SLAM), the strong static assumption leads to a large degradation in the accuracy of visual SLAM in dynamic environments. For this reason, many scholars incorporate semantic segmentation networks into the visual SLAM framework to extract dynamic information in images. However, most semantic segmentation networks consume a lot of computing time due to the large model size, which leads to the algorithm’s inability to meet real-time requirements in practical applications. In this paper, a real-time visual SLAM algorithm based on deep learning is proposed. This novel algorithm is based on ORB-SLAM2, and a parallel semantic thread based on the lightweight object detection network YOLOv5s is designed, which enables us to get semantic information in the scene more quickly. In the tracking thread, an optimized homography matrix module is proposed, which utilizes semantic information to optimize and solve the homography matrix so that we can calculate a more accurate optical flow vector. In the optical flow module, the semantic information is used to narrow down the calculation range of the optical flow value to improve the real-time performance of the system, and the dynamic feature points in the image are removed by the optical flow mask to improve the accuracy of the system. Experimental results show that compared with ORB-SLAM2, DynaSLAM, and other excellent visual SLAM algorithms, this algorithm can effectively reduce the absolute trajectory error of visual SLAM in dynamic environments. Compared with other deep learning-based visual SLAM algorithms, the real-time performance of this algorithm is also significantly improved.