EDENet: Echo Direction Encoding Network for Place Recognition Based on Ground Penetrating Radar

TL;DR

EDENet introduces a novel approach for large-scale underground place recognition using ground penetrating radar, employing directional features, learnable filters, and attention mechanisms to improve robustness and efficiency.

Contribution

The paper presents EDENet, a new neural network architecture that leverages directional encoding and multi-scale strategies for improved large-scale underground place recognition.

Findings

Outperforms existing methods in place recognition accuracy

Reduces model size and computational complexity

Effectively handles variability in underground dielectric properties

Abstract

Ground penetrating radar (GPR) based localization has gained significant recognition in robotics due to its ability to detect stable subsurface features, offering advantages in environments where traditional sensors like cameras and LiDAR may struggle. However, existing methods are primarily focused on small-scale place recognition (PR), leaving the challenges of PR in large-scale maps unaddressed. These challenges include the inherent sparsity of underground features and the variability in underground dielectric constants, which complicate robust localization. In this work, we investigate the geometric relationship between GPR echo sequences and underground scenes, leveraging the robustness of directional features to inform our network design. We introduce learnable Gabor filters for the precise extraction of directional responses, coupled with a direction-aware attention mechanism for effective geometric encoding. To further enhance performance, we incorporate a shift-invariant unit and a multi-scale aggregation strategy to better accommodate variations in di-electric constants. Experiments conducted on public datasets demonstrate that our proposed EDENet not only surpasses existing solutions in terms of PR performance but also offers advantages in model size and computational efficiency.