A Quantitative Evaluation of Bathymetry-Based Bayesian Localization Methods for Autonomous Underwater Robots

This article presents an evaluation of four probabilistic algorithms for bathymetry-based localization of autonomous underwater vehicles (AUVs). The algorithms fuse a priori bathymetry information with depth and range measurements to localize an AUV underwater using four different Bayes filters [extended Kalman filter, unscented Kalman filter, particle filter, and marginalized PF (MPF)]. We develop the algorithms using the robot operating system (ROS), build a realistic simulation platform using ROS Gazebo incorporating real-world bathymetry, and evaluate the performance of these four Bayesian bathymetry-based AUV localization approaches on real-world lake data. The simulation allows the evaluation of algorithms with accurate knowledge of the robot's true location, which is otherwise infeasible to obtain underwater in the field. By relying on the data from a depth sensor and echo sounder, the localization algorithms overcome challenges faced by visual landmark-based localization. Our results show the efficacy of each algorithm under a variety of conditions, with the MPF being the most accurate in general.