Under‐Ice Odometry Fusing Acoustic and Visual Information for Underwater Vehicles

ABSTRACT Ice cover hinders the deployment of acoustic arrays or satellites, making it challenging to locate underwater vehicles in the polar regions. Odometry based on acoustic or optical sensors has the potential to provide precise navigation results for vehicles. However, the robustness of single‐sensor‐based odometry is significantly compromised by the smooth and featureless nature of the ice‐covered bottom, which lacks distinct points, lines, or surfaces. This paper presents a filter‐based multi‐sensor fusion underwater ice odometry system, integrating measurements from a stereo camera, a forward‐looking sonar (FLS), a low‐cost inertial measurement unit (IMU), and a depth gauge (DG). To balance real‐time performance and accuracy, the Multi‐State Constraint Kalman Filter (MSCKF) algorithm is employed to fuse inertial and stereo measurements for vehicle state estimation. Additionally, FLS images and relative depth measurements from the DG are utilized to enhance the accuracy and robustness of the state estimation. Specifically, a Fourier‐based algorithm is proposed to estimate linear speed using global information from FLS images. A compensation factor is introduced to address the impact of FLS elevation angle on estimation accuracy. Furthermore, a residual‐based adaptive method and forgetting factor are implemented to adjust the measurement noise covariance of linear speed and relative depth, improving dynamic state estimation precision. The proposed system was evaluated through tank and field experiments using a remotely operated vehicle (ROV). Results show it provides more accurate and robust under‐ice navigation compared to state‐of‐the‐art algorithms.