무인잠수정 3자유도 운동실험에 대한 무향 칼만 필터 기반 SLAM 기법 적용

Abstract

Increased usage of unmanned underwater vehicle (UUV) has led to the development of alternative navigational methods that do not employ the acoustic beacons and dead reckoning sensors. This paper describes a simultaneous localization and mapping (SLAM) scheme that uses range sonars mounted on a small uuv. The SLAM is one of such alternative navigation methods for measuring the environment that the vehicle is passing through and providing relative position of UUV by processing the data from sonar measurements. A technique for SLAM algorithm which uses several ranging sonars is presented. This technique utilizes an unscented Kalman filter to estimate the locations of the uuv and objects. In order for the algorithm to work efficiently, the nearest neighbor standard filter is introduced as the algorithm of data association in the SLAM for associating the stored targets the sonar returns at each time step. The proposed SLAM algorithm is tested by experiments under various3 degrees of freedom motion conditions. The results of the experiment show that the proposed SLAM algorithm is capable of estimating the position of the uuv and the object and demonstrates that the algorithm will perform well in various environments.