An Elevation Mapping Algorithm Based on a Markov Random Field Model for Underwater Exploration

The 2-D Forward-looking Sonar (2D-FLS) is a new technology of sonar signals that increases the performance of underwater exploration of an Autonomous Underwater Vehicle (AUV). Because the AUV is a fully automatic robot that can handle various missions and does not require humans to control its operation, FLS is the best tool for underwater navigation in poor visibility conditions. In this paper, we used an FLS image sequence to estimate the height of the pipeline and create an elevation map containing information about the height in each of the pixels. In our algorithm, we employed the Markov random field to model the elevation map, since elevations of neighboring pixels are more likely to have similar values. Next, the Metropolis Algorithm (MA) is used to find the solution of the maximum a posterori equation.