Experiment on Simultaneous Localization and Mapping Based on Unscented Kalman Filter for Unmanned Underwater Vehicles

This paper proposes a simultaneous localization and mapping (SLAM) scheme applicable to the autonomous navigation of unmanned underwater vehicles (UUV). A SLAM scheme is an alternative navigation method for measuring the environment through which the vehicle in passing and providing the relative position of the unmanned vehicle. An unscented Kalman filter (UKF) is utilized in order to develop a SLAM that is suitable for estimating the locations of the UUV and the surrounding objects when the UUV's motion is highly nonlinear. A range sonar is used as a sensor for collecting the data of the spatial information of the environment in which the UUV navigates. The proposed UKF-SLAM scheme was tested in experiments that used various 3 degrees-of-freedom motion conditions with a real UUV under a tank environment. The results of these experiments showed that the proposed SLAM algorithm was capable of estimating the position of the UUV and the surrounding objects in real environments, and that the algorithm will perform well in various conditions.