An effective dynamic SLAM algorithm integrating YOLOv7 model

Dynamic objects are ubiquitous in real-world environments. However, traditional Simultaneous Localization and Mapping (SLAM) algorithms assume static scenes and fail on active tasks. Therefore, we need SLAM systems to achieve a precise and efficient localization and mapping in dynamic environments. Toward this goal, this paper proposes a dynamic SLAM algorithm incorporating a target detection model, YOLOv7. The general process of the algorithm is as follows: Firstly, the real-time target detector YOLOv7 segments the input image and divides it into static and dynamic parts. Secondly, feature information of the image frame is extracted. If the feature information is located in the dynamical part of the image, it will be eliminated for achieving an accurate tracking and mapping in the following steps. Next, tracking and mapping based on the image frame without dynamic objects are performed. Finally, global Bundle Adjustment (BA) optimization is performed to obtain more accurate pose and map information. The experimental results on TUM and KITTI show that the proposed algorithm in this paper has achieved the same accuracy as the DynaSLAM. In real-time, this algorithm has more advanced efficiency advantages on KITTI and TUM than the DynaSLAM algorithm and Dynamic ORB_SLAM. In particular, it has improved the speed by 33.03% on the TUM compared to Dynamic ORB_SLAM. Therefore, the proposed algorithm ensures that the SLAM system can achieve high accuracy in dynamic scenarios while maximizing speed.