In this work, we propose the use of Ground Penetrating Radar (GPR) for rover localization on Mars. Precise pose estimation is an important task for mobile robots exploring planetary surfaces, as they operate in GPS-denied environments. Although visual odometry provides accurate localization, it is computationally expensive and can fail in dim or high-contrast lighting. Wheel encoders can also provide odometry estimation, but are prone to slipping on the sandy terrain encountered on Mars. Although traditionally a scientific surveying sensor, GPR has been used on Earth for terrain classification and localization through subsurface feature matching. The Perseverance rover and the upcoming ExoMars rover have GPR sensors already equipped to aid in the search of water and mineral resources. We propose to leverage GPR to aid in Mars rover localization. Specifically, we develop a novel GPR-based deep learning model that predicts 1D relative pose translation. We fuse our GPR pose prediction method with inertial and wheel encoder data in a filtering framework to output rover localization. We perform experiments in a Mars analog environment and demonstrate that our GPR-based displacement predictions both outperform wheel encoders and improve multi-modal filtering estimates in high-slip environments. Lastly, we present the first dataset aimed at GPR-based localization in Mars analog environments, which will be made publicly available upon publication.

翻译：本文提出利用探地雷达（GPR）实现火星车在火星表面的定位。在缺乏全球定位系统的行星探测环境中，移动机器人的精确位姿估计至关重要。尽管视觉里程计能提供准确的位置信息，但其计算成本高昂，且在光照昏暗或高对比度环境下可能失效。轮式编码器虽可提供里程估计，但在火星常见的沙质地形上易发生打滑现象。传统上作为科学勘测传感器的探地雷达，已在地球上通过地下特征匹配技术成功应用于地形分类与定位领域。当前"毅力号"火星车及即将发射的"ExoMars"火星车均已配备探地雷达传感器用于水资源与矿物勘探。本研究创新性地利用探地雷达辅助火星车定位，开发了一种基于深度学习的GPR模型，可预测一维相对位姿平移量。通过滤波框架将GPR位姿预测方法与惯性测量单元、轮式编码器数据融合，最终输出火星车定位结果。在火星模拟环境中进行的实验表明：基于GPR的位移预测不仅优于轮式编码器，还能在多模态滤波估计中显著提升高滑移环境下的定位精度。此外，本研究首次构建了面向火星模拟环境的GPR定位数据集，该数据集将在论文发表后公开共享。