An RGB-D SLAM Algorithm Based on a Multi-Layer Refraction Model for Underwater Scenarios

The use of depth cameras in low-texture environments is crucial for ensuring the feasibility of visual simultaneous localization and mapping (SLAM) algorithms. Nevertheless, in underwater scenarios, light propagation through multi-layered media gives rise to refractive distortion. Directly utilizing distorted images acquired by depth cameras for visual SLAM computations inevitably introduces substantial errors in localization and mapping. Additionally, the waterproof glass mounted in front of the depth camera renders traditional air-based camera calibration ineffective, thereby introducing calibration inaccuracies. To mitigate these challenges, we propose a comprehensive SLAM algorithm framework for underwater multi-layered media refraction correction based on RGB-D cameras. Firstly, a multi-layer refraction calibration module is developed to calibrate the depth camera in air. Subsequently, the calibrated parameters are leveraged to construct an underwater multi-layer refraction correction module, which retrieves undistorted color images and aligned depth images. Finally, the corrected color images and depth images are fed into the front-end of the visual SLAM algorithm to generate dense point cloud maps. Both simulation and real-world experiments are conducted to validate the accuracy of the multi-layer refraction calibration results and the precision of the dense point clouds obtained via multi-layer refraction correction. Furthermore, the superiority of the proposed method is demonstrated through both qualitative and quantitative evaluations.