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

2025-02-28

AI Summary

To address the poor robustness of ground-penetrating radar (GPR) place recognition (PR) in large-scale underground environments—caused by sparse subsurface features and highly variable dielectric properties—this paper proposes an end-to-end deep network leveraging geometric directional characteristics of GPR echoes. The method innovatively introduces learnable Gabor filters to extract directional responses, designs a direction-aware attention mechanism and translation-invariant convolutional units, and fuses multi-scale features to enhance adaptability to dielectric variations. Evaluated on a public GPR dataset, the approach achieves a 12.6% improvement in PR accuracy, reduces model size by 37%, and accelerates inference speed by 2.1×, significantly outperforming state-of-the-art methods.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Addresses large-scale place recognition using GPR data.
• Overcomes sparsity and dielectric constant variability in underground features.
• Enhances localization robustness with directional feature extraction and encoding.

Innovation

• Learnable Gabor filters for directional response extraction
• Direction-aware attention mechanism for geometric encoding
• Shift-invariant unit and multi-scale aggregation strategy