SLAM for an AUV using vision and an acoustic beacon

The estimation of the position and attitude of an autonomous underwater vehicle (AUV) is a challenging and important problem in marine robotics. It is well known that the underwater environment posses considerable problems, that include i) the fact that there is no GPS signal, ii) the communication is usually done through acoustic signals, which suffers from faults, delays and low bandwidth, and iii) the use of vision and/or laser is very limited due to poor visibility. In this paper, we combine a multiple set of sensors to address the full state 6DOF pose estimation of an AUV. The problem is formulated assuming that we have partial measurements from an Inertial Measurement Unit (IMU), an acoustic ranging from a single beacon buoy, and a monocular camera attached to the AUV. Using multiple model estimation techniques and the concept of Extended Kalman Filters with Simultaneous Localization and Mapping (EKF-SLAM), we propose an algorithm that integrates the AUV measurements (that arrive at different sampling-times) and compute in real time an estimate of the position and attitude of the AUV. Simulation results are presented and discussed.