Acoustic-inertial underwater navigation

Abstract

In this paper, we introduce a novel acoustic-inertial navigation system (AINS) for Autonomous Underwater Vehicles (AUVs). We are aiming to reduce the cost and latency of current underwater navigation systems that typically employ high-accuracy and thus high-cost inertial sensors. In particular, the proposed approach efficiently fuses the acoustic observations from a 2D imaging sonar and the inertial measurements from a MEMS inertial measurement unit (IMU) within a tightly-coupled EKF framework, while having no need to keep the acoustic features in the state vector. As a result, the computational complexity of the proposed AINS is independent from the scale of the operating environment. Moreover, we develop an acoustic feature linear triangulation to provide accurate initial estimates for iterative solvers, and perform an in-depth observability analysis to investigate the effects of sensor motion on the triangulation. Additionally, since it is challenging to perform a priori sensor extrinsic calibration underwater, we advocate to calibrate IMU-sonar online. The proposed AINS has been validated extensively in Monte-Carlo simulations.