DFE-SLAM: a visual slam augmented by feature extraction in dynamic environments

In intelligent mobile robot technology, SLAM (simultaneous localization and map construction) technology is the key component, which enables the robot to achieve autonomous navigation and understanding of the surrounding environment in the unknown environment. Although traditional SLAM methods usually assume that the environment is static or slowly changing, the dynamic nature of the environment, especially the presence of dynamic objects, poses significant challenges to SLAM algorithms in practical application scenarios such as drone navigation, autonomous driving, and robot exploration.To address this challenge, We put forward a sturdy semantic visual SLAM algorithm designated as DFE-SLAM. The innovation of DFE-SLAM is that it combines semantic segmentation network and optical flow pyramid technology to effectively reduce the influence of dynamic targets on positioning accuracy, thus significantly improving the accuracy of positioning in dynamic environments. In addition, we introduced Nerf mapping technology to make our SALM building maps clearer. To verify the performance of DFE-SLAM, we conducted extensive experiments on the TUM RGB-D dataset as well as in real-world environments. The experimental results show that the absolute ballistic accuracy of DFE-SLAM is significantly improved compared with that of ORB-SLAM3. This result not only validates the effectiveness of DFE-SLAM in dynamic environments, but also provides strong support for future research and application.