A Bio-Inspired Simultaneous Surface and Underwater Risk Assessment Method Based on Stereo Vision for USVs in Nearshore Clean Waters

Abstract

Ensuring safety of unmanned surface vehicles (USVs) during nearshore field operations is challenging as they face unknown risks of collision with surface and underwater obstacles. To address this problem, we propose a bio-inspired collision risk-assessment method for ensuring safe USVs operation in nearshore clean waters. Collision risks are typically unknown due to the imprecision of underwater measurements for the water-air boundary light refraction, and disturbances caused by wave-induced USV motion. To tackle these two causes of imprecision, our collision risk-assessment method employs one single stereo camera and an IMU. We use visual information from the stereo camera to model the light rays at the water-air interface, rectify the underwater measurements, and monitor for surface obstacles, and IMU data to model wave disturbances via time-frequency analysis. We verify the performance of this new method in both the indoor and outdoor experiments, which demonstrates that it achieves a 65.44% improvement in the depth accuracy at the water-air interface compared with raw depth measurements, and provides a range of safety levels for guiding USV operations. This new collision risk-assessment method significantly expands the USVs working areas while ensuring safety during field exploration. Additionally, the simplicity of the sensor setup and thus minimal cost of this method underscore its utility for supporting USV-based various explorations in clear waters.