Underwater Simultaneous Localization and Mapping Based on EKF and Point Features

The paper describes a localization system for autonomous underwater vehicles (AUV). It uses a DVL (Doppler velocity log) sensor and AHRS (attitude and heading reference system) sensor to measure AUV's depth, attitude and velocities relative to the bottom. A mechanically scanning imaging sonar (MSIS) is employed to obtain acoustic images of objects in underwater environment. In order to estimate optimally AUV pose without a priori map of the environment, simultaneous localization and map building (SLAM), a prevailing method in the past decade, is presented based on point features extraction and EKF-based estimator. Use Fluvia Nautic marina data set we compare the proposed method with traditional dead-reckoning, results show that our solution can reduce estimation error significantly.