Computer Vision-Based Position Estimation for an Autonomous Underwater Vehicle

Abstract

Autonomous Underwater Vehicles (AUVs) are currently one of the most intensively developing branches of marine technology. Their widespread use and versatility allow them to perform tasks that, until recently, required human resources. One problem in AUVs is inadequate navigation, which results in inaccurate positioning. Weaknesses in electronic equipment lead to errors in determining a vehicle’s position during underwater missions, requiring periodic reduction of accumulated errors through the use of radio navigation systems (e.g., GNSS). However, these signals may be unavailable or deliberately distorted. Therefore, in this paper, we propose a new computer vision-based method for estimating the position of an AUV. Our method uses computer vision and deep learning techniques to generate the surroundings of the vehicle during temporary surfacing at the point where it is currently located. The next step is to compare this with the shoreline representation on the map, which is generated for a set of points that are in a specific vicinity of a point determined by dead reckoning. This method is primarily intended for low-cost vehicles without advanced navigation systems. Our results suggest that the proposed solution reduces the error in vehicle positioning to 30–60 m and can be used in incomplete shoreline representations. Further research will focus on the use of the proposed method in fully autonomous navigation systems.

Advancements in Underwater Navigation: Integrating Deep Learning and Sensor Technologies for Unmanned Underwater Vehicles

“…It can extract land features accurately, reduce dead reckoning errors, and adapt to difficult sea situations. The technique could enable fully autonomous AUV navigation in GNSS-denied conditions, improving low-cost AUV technology [59]. A comprehensive dataset from a controllable AUV with high-precision fiber-optic inertial sensors, a Doppler Velocity Log (DVL), and depth sensors by Can Wang et al advances autonomous underwater vehicle (AUV) navigation.…”