用雷达地下特征提升机器人定位精度与稳定性
Ground Penetrating Radar-Assisted Multimodal Robot Odometry Using Subsurface Feature Matrix
- 构建地下特征矩阵，从雷达信号中提取高频峰值作为特征
- 通过特征匹配实现毫米级位移估计，定位误差降低40%
- 融合雷达、惯性与轮速数据，适合复杂环境下的机器人导航
利用地面穿透雷达（GPR）观测的地下特征可增强并提升常见传感器模态的定位鲁棒性，因地下特征受天气、季节及地表变化影响较小。本文提出一种创新的多模态里程计方法，融合GPR、惯性测量单元（IMU）和轮编码器输入。为有效应对GPR信号噪声，提出一种先进特征表示——地下特征矩阵（SFM），利用频域数据识别雷达扫描中的峰值。此外，提出一种新型特征匹配方法，通过对齐SFM估算GPR位移。三源信息通过因子图框架整合，实现多模态机器人里程计。方法在CMU-GPR公开数据集上开发并评估，实现在机器人里程计任务中实时性能下精度与鲁棒性的显著提升。
原文摘要 · Abstract (English)
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.
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