YG-SLAM: GPU-Accelerated RGBD-SLAM Using YOLOv5 in a Dynamic Environment

Traditional simultaneous localization and mapping (SLAM) performs well in a static environment; however, with the abrupt increase of dynamic points in dynamic environments, the algorithm is influenced by a lot of meaningless information, leading to low precision and poor robustness in pose estimation. To tackle this problem, a new visual SLAM algorithm of dynamic scenes named YG-SLAM is proposed, which creates an independent dynamic-object-detection thread and adds a dynamic-feature-point elimination step in the tracking thread. The YOLOv5 algorithm is introduced in the dynamic-object-detection thread for target recognition and deployed on the GPU to speed up image frame identification. The optic-flow approach employs an optic flow to monitor feature points and helps to remove the dynamic points in different dynamic objects based on the varying speeds of pixel movement. While combined with the antecedent information of object detection, the system can eliminate dynamic feature points under various conditions. Validation is conducted in both TUM and KITTI datasets, and the results illustrate that YG-SLAM can achieve a higher accuracy in dynamic indoor environments, with the maximum accuracy augmented from 0.277 m to 0.014 m. Meanwhile, YG-SLAM requires less processing time than other dynamic-scene SLAM algorithms, indicating its positioning priority in dynamic situations.