Semantic SLAM for Dynamic Environment

ABSTRACT Most of the current simultaneous localization and mapping (SLAM) algorithms are realized based on the assumption of a static environment. However, most environments are dynamic in the real world. Therefore, we propose a semantic SLAM algorithm for a dynamic environment. Specifically, we obtain the semantic information through deep learning, combine it with the position information of point cloud to filter out dynamic objects and alleviate the accuracy degradation caused by dynamic obstacles. Due to factors such as noise and occlusion, there are some outliers present within the point cloud data, and these data points can adversely impact the performance of the algorithm. To solve this, we utilize semantic information consistency to adjust the weighting of the error function, thereby mitigating the impact of outliers and enhancing the robustness of the front‐end odometry. On this basis, we further propose a multi‐frame verification method based on descriptors to optimize the back‐end loop closure detection algorithm. The experimental results show that our method enhances accuracy and robustness compared with the benchmark.