Monocular Visual-Based State Estimator for Online Navigation in Complex and Unstructured Underwater Environments

This paper proposes a monocular visual-based navigation state estimator designed to operate onboard cost-effective Autonomous Underwater Vehicles (AUVs) in monitoring and inspection applications. The estimator exploits a monocular visual odometry solution, named Mono UVO (Monocular Underwater Visual Odometry), integrating acoustic range information to make the scale observable and provide an estimate of the robot linear velocity in complex and unstructured underwater scenarios. By utilizing an Extended Kalman Filter, the visual-based linear velocity is fused with robot attitude and depth measurements to retrieve the AUV navigation state. The proposed navigation framework was extensively tested offline using heterogeneous data sets of real underwater images collected during several experimental campaigns. Moreover, online validation of the navigation state estimator was performed onboard an AUV to accomplish a closed-loop autonomous survey at sea. The performance of the state estimator is evaluated by comparing the estimation output with reference signals obtained from Doppler Velocity Log measurements and GPS when available. The results demonstrate the feasibility of the proposed visual-based state estimator in providing reliable AUV navigation state in very different and challenging underwater environments. Among the contributions, the source code of the Mono UVO algorithm is made available online, together with the release of an underwater data set.