用雷达图像差异与相似性提升地下定位精度
GPR-OdomNet: Difference and Similarity-Driven Odometry Estimation Network for Ground Penetrating Radar-Based Localization
- 通过分析连续雷达图像的特征差异与相似性估算移动距离
- 在CMU-GPR数据集上实现0.449米整体加权均方根误差
- 适合需要高精度地下环境定位的机器人与自动驾驶
在利用地面穿透雷达（GPR）进行机器人或车辆定位以应对恶劣天气和环境条件时，现有技术在处理差异微小的B-scan图像时往往难以准确估计距离。本文提出一种基于神经网络的里程计方法，利用GPR B-scan图像的相似性和差异性特征，精确估算连续图像间所行进的欧氏距离。该自定义神经网络从连续时刻获取的B-scan图像中提取多尺度特征，并通过分析特征间的相似性与差异性来确定移动距离。为评估方法性能，我们在公开的CMU-GPR数据集上进行了消融实验与对比实验。结果表明，本方法在所有测试中均优于现有最先进方法，整体加权均方根误差（RMSE）达到0.449米，较最佳对比方法降低10.2%。
原文摘要 · Abstract (English)
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.
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