GPR-OdomNet: Difference and Similarity-Driven Odometry Estimation Network for Ground Penetrating Radar-Based Localization

TL;DR

This paper presents GPR-OdomNet, a neural network that improves ground penetrating radar-based localization by effectively analyzing similarity and difference features in B-scan images, leading to more accurate odometry estimation under challenging conditions.

Contribution

The study introduces a novel neural network architecture that leverages similarity and difference features of GPR B-scan images for improved odometry estimation, outperforming existing methods.

Findings

Achieves a weighted RMSE of 0.449 m, reducing error by 10.2% compared to state-of-the-art.

Outperforms existing methods in all evaluation tests on the CMU-GPR dataset.

Demonstrates robustness in adverse weather and environmental conditions.

Abstract

When performing robot/vehicle localization using ground penetrating radar (GPR) to handle adverse weather and environmental conditions, existing techniques often struggle to accurately estimate distances when processing B-scan images with minor distinctions. This study introduces a new neural network-based odometry method that leverages the similarity and difference features of GPR B-scan images for precise estimation of the Euclidean distances traveled between the B-scan images. The new custom neural network extracts multi-scale features from B-scan images taken at consecutive moments and then determines the Euclidean distance traveled by analyzing the similarities and differences between these features. To evaluate our method, an ablation study and comparison experiments have been conducted using the publicly available CMU-GPR dataset. The experimental results show that our method consistently outperforms state-of-the-art counterparts in all tests. Specifically, our method achieves a root mean square error (RMSE), and achieves an overall weighted RMSE of 0.449 m across all data sets, which is a 10.2\% reduction in RMSE when compared to the best state-of-the-art method.