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

Haifeng Li, Jiajun Guo, Dezhen Song

Abstract: Robot localization using subsurface features captured 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 measurements 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 segmentation, 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.