Periodical inspections are a fundamental operation to monitor the status of underwater structures and to assess their need for proper maintenance or repair interventions. Autonomous Underwater Vehicles (AUVs) could represent a viable option to carry out underwater inspection tasks, potentially bringing benefits in terms of safety for human operators and quality of the collected data. Aiming at developing a fully autonomous vision-based inspection strategy, this paper proposes a comparative analysis between monocular and stereo vision approaches for estimating the lateral velocity of an AUV and its orientation with respect to a target surface. The proposed analysis is performed by exploiting a dataset of real underwater images, collected during at-sea experiments in which the Zeno AUV was remotely driven to carry out a pier inspection. Specifically, the performance of the two solutions in terms of estimation of the robot lateral velocity is assessed by considering doppler velocity log measurements as benchmark. Instead, the accuracy of the estimation of the vehicle orientation with respect to the target is evaluated by taking into account both geographical information of the pier and AUV attitude observations. The comparison suggests that stereo vision provides better performance for estimating the relative orientation between the AUV and the target; on the contrary, the monocular approach produces more reliable lateral velocity estimates. The results obtained prove the suitability of the two vision-based strategies for inspection applications in a real underwater scenario, thus suggesting a possible implementation onboard the reference vehicle.

"File: https://ieeexplore.ieee.org/abstract/document/10244261"