发表机构
School of Computer Science, Robotics Institute, Carnegie Mellon University（卡内基梅隆大学计算机科学学院机器人研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对毫米波雷达数据创建概率地图的挑战，建立从原始信号到概率占用地图的完整流程，含合成孔径雷达处理与概率建模，经室内验证、性能分析及参数研究，展示方法有效性与局限性，并贡献开源数据集及处理管道。
AI 中文摘要
强大的概率地图构建对于在具有挑战性环境中运行的自主机器人系统至关重要。传统传感器在烟雾和雾气等恶劣条件下失效，毫米波雷达传感器在此类条件下能提供可靠传感。但由于无线电波测量和信号处理步骤固有的稀疏和噪声特性，从雷达数据创建精确概率地图面临重大挑战。为解决这些问题，我们建立了从原始雷达信号到概率占用地图的完整流程，包括合成孔径雷达处理和概率建模步骤。我们在室内环境中进行了广泛验证，将我们的方法与不同信号处理和概率建模方法进行比较。我们还通过下游路径规划性能分析评估地图质量。此外，我们研究了关键参数和天线阵列配置对地图性能的影响。实验结果证明了基于合成孔径雷达的概率地图在实际机器人部署中的有效性和局限性。为促进未来研究和更广泛采用，我们贡献了一个开源级联毫米波雷达数据集以及一个GPU加速信号处理管道，可从此https URL获取。
英文摘要
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.
