Date of Award
Master of Science in Ocean Engineering
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. 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 a ultra short baseline (USBL) acoustic system. The presented methodology provides an alternate method for georeferencing when USBL is unavailable. The implemented particle filter utilizes a background bathymetry map and visual odometry as a motion mode. 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 in the Hawaiian islands in 2018 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 still maintaining the georeferencing critical to the end use of the collected images.
Raggi, Emanuele, "LOCALIZATION OF A DRIFTING UNDERWATER VEHICLE USING A TERRAIN-BASED PARTICLE FILTER" (2019). Open Access Master's Theses. Paper 1526.