WS-SLAM: a monocular visual SLAM algorithm considering dynamic invalid features of water surface for USV

The autonomous navigation capability of USVs in complex water environments relies on Simultaneous Localization and Mapping (SLAM) technology. However, water surface perturbations and reflections generate numerous dynamic invalid feature points, causing low localization accuracy and poor robustness in VSLAM. To this end, we propose a monocular VSLAM algorithm considering dynamic invalid features of the water surface for USV, named WS-SLAM. Firstly, the ORBSLAM3 framework is improved by adding the PP-LiteSeg semantic segmentation thread to distinguish between dynamic invalid feature points of the water surface and feature points on shore. Secondly, semantic information is used to generate a mask to cull dynamic invalid feature points of the water surface, reducing the impact of water current perturbation on visual localization. Then, further culling of potentially residual dynamic feature points on shore is performed in conjunction with the epipolar geometric constraint. Finally, the camera position is estimated using only static feature points. The experimental results based on the public USVInland dataset show that WSSLAM improves the positioning accuracy by an average of 43.47% in straight line sailing compared with ORBSLAM3. It can effectively inhibit the position drift caused by the dynamic invalid features of the water surface and has higher positioning accuracy and stronger robustness.