해저 지형정보를 이용하는 수중 로봇 위치추정 방법의 구현 및 성능 비교

Abstract

This paper proposes an application of unscented Kalman filter(UKF) for localization of an underwater robot. The method compares the bathymetric measurement from the robot with the seabed terrain information. For the measurement of bathymetric range to seabed, it uses a DVL which typically yields four range data together with velocity of the robot. Usual extended Kalman filter is not appropriated for application in case of terrain navigation, since it is not feasible to derive Jacobian for the bathymetric range measurement. Though particle filter(PF) is a nice solution which doesn`t require Jacobian and can deal with non-linear and non-Gaussian system and measurement, it suffers from heavy computational burden. The paper compares the localization performance and the computation time of the UKF approach and PF approach. Though there have been some UKF methods which are used for underwater navigation, application of the UKF for bathymetric localization is rare. Especially, the proposed method uses only four range data whereas many of the bathymetric navigation methods have used multibeam sonar which yields hundreds of scanned range data. The result shows feasibility of the UKF approach for terrain-based navigation using small numbers of range data.