Localization of robots using subsurface features observed by ground-penetrating radar (GPR) enhances and adds robustness to common sensor modalities, as subsurface features are less affected by weather, seasons, and surface changes. We introduce an innovative multimodal odometry approach using inputs from GPR, an inertial measurement unit (IMU), and a wheel encoder. To efficiently address GPR signal noise, we introduce an advanced feature representation called the subsurface feature matrix (SFM). The SFM leverages frequency domain data and identifies peaks within radar scans. Additionally, we propose a novel feature matching method that estimates GPR displacement by aligning SFMs. The integrations from these three input sources are consolidated using a factor graph approach to achieve multimodal robot odometry. Our method has been developed and evaluated with the CMU-GPR public dataset, demonstrating improvements in accuracy and robustness with real-time performance in robotic odometry tasks.

翻译：利用探地雷达观测到的地下特征进行机器人定位，能够增强并提升常见传感器模态的鲁棒性，因为地下特征受天气、季节和地表变化的影响较小。我们提出了一种创新的多模态里程计方法，该方法融合了探地雷达、惯性测量单元和轮式编码器的输入。为了有效处理探地雷达信号噪声，我们引入了一种称为地下特征矩阵的高级特征表示方法。SFM利用频域数据并识别雷达扫描中的峰值。此外，我们提出了一种新颖的特征匹配方法，该方法通过对齐SFM来估计探地雷达位移。来自这三个输入源的集成信息通过因子图方法进行融合，以实现多模态机器人里程计。我们的方法已在CMU-GPR公共数据集上进行了开发和评估，结果表明其在机器人里程计任务中提高了精度和鲁棒性，并具备实时性能。