BE-SLAM: BEV-Enhanced Dynamic Semantic SLAM with Static Object Reconstruction

The quality of a robot’s environmental perception determines whether it can achieve more intelligent applications, such as semantic interaction with humans. SLAM, on the other hand, is one of the crucial capabilities for a robot to perceive its environment. However, when only a monocular image is provided, dynamic objects in the environment significantly impact the accuracy of map construction by the robot, leading to erroneous perception results. To address this issue, we propose a Visual SLAM framework based on BEV perception results, named BE-SLAM. With this framework, we can handle dynamic objects, occlusions, and incompletely observed objects. It can construct a stable static map by strengthening trust in static objects. Considering that object-level semantic maps can enhance a robot’s perception abilities, we also reconstruct static objects in the map and use them to optimize the pose. Through experiments on existing publicly available dataset, we compare BE-SLAM with several existing methods that have shown good performance. The experimental results demonstrate that BE-SLAM performs exceptionally well on high-dynamic sequences and achieves comparable results on static or low-dynamic sequences.