Pseudo-3D Vision-Inertia Based Underwater Self-Localization for AUVs

Abstract

Autonomous underwater vehicles (AUVs) play an important role in subaqueous construction and oceanographic survey, in which AUV self-localization is a crucial component. Current localization methods mainly depend on expensive acoustic positioning systems, which limit the wide application of AUVs. In this paper, referring to the low-cost visual localization method and focusing on challenges caused by underwater environment, we propose an underwater self-localization method based on Pseudo-3D vision-inertia for AUVs. The proposed method merges depth information into 2D visual image to achieve continuous and robust localization under dramatically changing underwater environment. In order to decrease errors, we propose an online fusion method based on tightly-coupled nonlinear optimization to fuse the measurements of the pre-integrated inertial measurement unit and the observations from the down-looking camera. We also optimize four degrees-of-freedom pose graph to enhance the global consistency and design an online loop detection module to realize the underwater relocalization. In addition, we develop a low-cost, portable, and small volume sensor suite for underwater vehicle localization and test the proposed self-localization method. We test the proposed method in the underwater environment using the custom-made sensor suite, and the experimental results demonstrate the effectiveness of the proposed method under dramatically changing underwater environment.