Integration of Sonar and Visual–Inertial Systems for SLAM in Underwater Environments

Abstract

Underwater SLAM encounters challenges in complex environments due to suspended particles, underwater blur, and light and color attenuation. These factors make underwater features less distinct than those in surface images. Moreover, underwater visual features are often unstructured, together with visual failures and low visibility. To address these challenges, a multi-sensor system is a promising solution. In this paper, we introduce a multi-sensor fusion underwater SLAM method, integrating stereo vision, multi-beam imaging sonar, and IMU data. Our system comprises a visual-inertial subsystem and an acoustic-inertial subsystem. These subsystems collaborate when common features are detected. If one subsystem fails, the other can function independently. The visual-inertial subsystem uses depth information from imaging sonar to optimize error correction during feature tracking. Furthermore, we have optimized the initialization process by matching visual and sonar images and introduced a novel method for depth estimation from sonar images. This dual-sensor strategy improves the system’s robustness and adaptability to diverse challenging underwater conditions. Through experiments, we have demonstrated the excellent performance of our algorithm.