Close-Proximity Underwater Terrain Mapping Using Learning-based Coarse Range Estimation

Abstract

This paper presents a novel approach to underwater terrain mapping for Autonomous Underwater Vehicles (AUVs) operating in close proximity to complex 3D environments. The proposed methodology creates a probabilistic elevation map of the terrain using a monocular image learningbased scene range estimator as a sensor. This scene range estimator can filter transient objects such as fish and lighting variations. The mapping approach considers uncertainty in both the estimated scene range and robot pose as the AUV moves through the environment. The resulting elevation map can be used for reactive path planning and obstacle avoidance to allow robotic systems to approach the underwater terrain as closely as possible. The performance of our approach is evaluated in a simulated underwater environment by comparing the reconstructed terrain to ground truth reference maps, as well as demonstrated using AUV field data collected within in a coral reef environment. The simulations and field results show that the proposed approach is feasible for obstacle detection and range estimation using a monocular camera in reef environments.

Guidance method of underwater vehicle for rugged seafloor observation in close proximity

“…In recent years, substantial research has been dedicated to developing affordable AUV systems for seafloor optical imaging. One strategy for reducing AUV system costs involves utilizing optical cameras to estimate the AUV's pose and interpret the surrounding environment (Arain et al, 2020;Dunbabin et al, 2019;Manderson et al, 2018). This approach includes technologies such as visual simultaneous localization and mapping (V-SLAM), where an optical camera is utilized to capture the surroundings, extract feature points in the image, and simultaneously estimate the AUV's pose and map the environment.…”