Localization of a Drifting Underwater Vehicle Using a Terrain-Based Particle Filter

Abstract

In this paper we present a terrain-aided particle filter to localize a freely drifting underwater vehicle. The vehicle is a bottom imaging Lagrangian float used for habitat classification, monitoring and fish abundance studies. During operation the vehicle captures down looking images at a controlled altitude above the bottom. Direct navigation information is often, but not always, recorded with an ultra short baseline (USBL) acoustic system. The presented methodology provides an alternate means for georeferencing when USBL is unavailable. The implemented particle filter utilizes a background bathymetry map and visual odometry measurements from the camera system. The particle filter is implemented using the Robot Operating System (ROS) and Orocos Bayesian Filtering Library (BFL). The Grid Map package is used to store and retrieve the bathymetryic data. Results using data collected on field deployments show the method is able to effectively utilize the terrain information and produce drift trajectories which closely match the recorded USBL data. Utilizing the method allows the float system to be deployed with minimal ship-side support while providing georeferencing that is critical to the end use of the collected images.