Looking beneath More: A Sequence-Based Localizing Ground Penetrating Radar Framework

Localizing ground penetrating radar (LGPR) has been proven to be a promising technology for robot localiza- tion in various dynamic environments. However, the extreme scarcity of underground features introduces false candidate matches and brings unique challenges to this task. In this paper, we propose a sequence-based framework for LGPR to address the aforementioned issues. Specifically, we first introduce a trainable strategy to extract robust underground features in multi-weather conditions. By further using sequential infor- mation, our LGPR system can observe richer underground scene contexts, and the associated multi-frame scans could also improve the performance of underground place recognition. We demonstrate the superiority of our proposed method by comparing it against several recent state-of-the-art baseline methods applied to GPR image tasks. Experimental results on large public and self-collected datasets show that our proposed framework significantly improves the performance of various baselines in different scenarios.