Subsurface Feature-Based Ground Robot/Vehicle Localization Using a Ground Penetrating Radar

Robot localization using subsurface features cap- tured by Ground-Penetrating Radar (GPR) complements and improves robustness over existing common sensor modalities, as subsurface features are less sensitive to weather, season and surface scene changes. Here, we propose a novel subsurface feature-based localization method that uses only GPR mea- surements with a known subsurface map. An efficient feature descriptor, the dominant energy curve (DEC), is designed to identify different locations in cluttered conditions. Specifically, image processing techniques that involve background segmen- tation, energy point detection, and energy curve refinement are designed to extract DEC features from a 2D radargram. With DECs features obtained, a metric subsurface feature map is constructed. Finally, we perform robot localization by feature matching under a particle swarm optimization framework. We have implemented our method and tested it with the public CMU-GPR dataset. The results show that our algorithm improves accuracy and robustness with real-time performance for robot localization tasks. Specifically, the mean localization error is 0.50 m for all cases.