Acoustic PCD-Based AUV Perception for Enhanced Automated Garage Docking

Automated docking for AUVs is an important application for prolonged AUV usage. However, current AUV perception systems experience several limitations that are accentuated in more turbid waters. A potential factor that causes this issue is the limited spatial information of the objects extractable from the 2D images utilized by both acoustic and optical-based modalities commonly found on such perception systems today.Inspired by the current progress done for underwater Point Cloud Data (PCDs), this paper thus proposes an acoustic PCD-based system that can synthesize PCD data with minimal acoustic image inputs, and utilize the additional spatial data from PCDs to potentially enhance the AUV perception for complex applications, such as automated docking. The proposed system consists of two main components: acoustic-based PCD reconstruction module, and a PCD-based classifier/pose-estimator (CPE) Convolutional Neural Network (CNN) module. Several simulation and in-field based experiments have been conducted to validate the feasibility of the system’s modules. Current results discussed in this paper show a potential feasibility for the proposed system for use in complex applications such as automated garage docking, noting further works to be conducted to improve the design and viability of the proposed system.