Synchronous and Asynchronous Application of a Filtering Method for Underwater Robot Localization

Abstract

This paper reports a method that fuses multiple sensor measurements for location estimation of an underwater robot. Synchronous and asynchronous (AS) implementation of the method are also proposed. Extended Kalman filter (EKF) is used to fuse four types of measurements: linear velocity by Doppler velocity log (DVL), angular velocity by gyroscope, ranges to acoustic beacons, and depth. The EKF approach is implemented in three ways to deal with asynchrony in measurements in correction step. The three implementation methods are synchronous collective (SC), synchronous individual (SI), and AS application. These methods are verified and compared through simulation and test tank experiments. The test reveals that the application methods need to be selected depending on the measurement properties: dependency between the measurements and degree of asynchrony. The distinctive features proposed in this study are three application methods together with derivation of an EKF approach to sensor fusion for underwater navigation.