Robot-centered Localization and Map Building for Autonomous Underwater Vehicle

Navigation and localization with high precision has been one of most critical issues for the safety and effective completion of missions of autonomous underwater vehicles. Since the underwater environment is extremely complex and the external sensors of what can be used are limited to only sonar, as well as information obtained has too much noise and interference, thus all of the intractable will make data processing much more difficult. An EKF-based algorithm which builds feature map by using a robot centered representation is applies to an open-frame AUV in this paper, it delays the composition of the previous pose and the current vehicle motion until the feature map and the motion have been reiquestned by using new observations of the environment. Proposed method results in a better linearization. The features currently observed have an small uncertainty in the order of the sensor error. Simulations results show that the vehicle can locate itself with an improved precision and the algorithm can be a feasible way for AUVpsilas navigation.