用毫米波雷达实现复杂环境下的概率地图构建
Millimeter Wave Radar: From Synthetic Aperture to Probabilistic Mapping
- 结合合成孔径雷达与概率建模，从原始信号生成地图
- 在室内环境中验证，路径规划性能提升显著
- 开源数据集与加速处理工具，便于后续研究
在烟雾、雾霾等恶劣环境下，传统传感器失效，而毫米波雷达仍可可靠工作。但其信号稀疏且噪声大，难以生成精确的概率地图。为此，本文构建了从原始雷达信号到概率占据地图的完整流程，融合合成孔径雷达（SAR）处理与概率建模。在多个室内场景中进行广泛验证，对比不同信号处理与建模方法，并通过下游路径规划性能评估地图质量。同时分析天线阵列配置与关键参数对性能的影响。实验表明，基于SAR的概率映射在真实机器人部署中有效，但也存在局限性。为促进研究，项目提供开源级联毫米波雷达数据集及配套的GPU加速信号处理流水线，详见https://github.com/rpl-cmu/rpm。
原文摘要 · Abstract (English)
Robust probabilistic mapping is essential for autonomous robotic systems operating in challenging environments. While traditional sensors fail in adverse conditions such as smoke and fog, millimeter wave (mmWave) radar sensors offer reliable sensing in such conditions. However, creating accurate probabilistic maps from radar data presents significant challenges due to the inherently sparse and noisy characteristics of radio wave measurements and signal processing steps. In an attempt to address these issues, we establish a complete pipeline from raw radar signals to probabilistic occupancy maps, incorporating Synthetic Aperture Radar processing followed by a probabilistic modeling step. We conduct extensive validation across indoor environments, comparing our approach against different signal processing and probabilistic modeling approaches. We also evaluate mapping quality through downstream path planning performance analysis. Furthermore, we investigate the impact of key parameters and antenna array configuration on mapping performance. The experimental results demonstrate both the effectiveness and limitations of SAR-based probabilistic mapping for real-world robotic deployment. To facilitate future research and broader adoption, we contribute an open-source cascaded mmWave radar dataset with an accompanying GPU-accelerated signal processing pipeline available at https://github.com/rpl-cmu/rpm.
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