Acoustic-VINS: Tightly Coupled Acoustic-Visual-Inertial Navigation System for Autonomous Underwater Vehicles

In this work, we present an acoustic-visual-inertial navigation system (Acoustic-VINS) for underwater robot localization. Specifically, we address the problem of the global position of the underwater visual-inertial navigation system being inappreciable by tightly coupling the long baseline (LBL) system into an optimization-based visual-inertial SLAM. In our proposed Acoustic-VINS, the reprojection error, IMU preintegration error, and raw LBL measurement error are jointly minimized within a sliding window factor graph framework. Furthermore, we propose an acoustic-aided initialization method to exhibit an accurate initial state for successful state estimation. Additionally, for wider application, we extend the sensor data of the real-world AQUALOC dataset to obtain the LBL-AQUALOC dataset. Experimental results on the ten sequences of the LBL-AQUALOC dataset in challenging underwater scenes show that our proposed approach outperforms state-of-the-art visual-inertial SLAM.