毫米波雷达在物体材质分类中受姿态变化影响大，提出轻量级边缘分类方案。
Edge Radar Material Classification Under Geometry Shifts
- 用雷达回波强度特征+MLP实现边缘设备实时材质分类。
- 姿态偏移使分类准确率从94.2%降至68.5%，因强度缩放与角度效应。
- 适合做机器人低功耗感知的工程师参考，可提升鲁棒性设计。
材料感知能提升机器人导航与交互能力，尤其在摄像头和激光雷达性能下降时。本文针对超低功耗边缘设备（TI IWRL6432）设计了一套轻量级毫米波雷达材质分类流程，采用紧凑的距离-距离单元强度描述符与多层感知机（MLP）实现实时推理。尽管在标准几何条件下分类器宏平均F1达到94.2%，但在真实场景中的几何偏移（如传感器高度变化、微小倾角）下性能显著下降，系统性地出现强度缩放与角度依赖的雷达散射截面（RCS）效应，导致特征分布外溢，宏平均F1降至约68.5%。我们分析了这些失效模式，并提出通过归一化、几何增强和运动感知特征提升鲁棒性的实用方向。
原文摘要 · Abstract (English)
Material awareness can improve robotic navigation and interaction, particularly in conditions where cameras and LiDAR degrade. We present a lightweight mmWave radar material classification pipeline designed for ultra-low-power edge devices (TI IWRL6432), using compact range-bin intensity descriptors and a Multilayer Perceptron (MLP) for real-time inference. While the classifier reaches a macro-F1 of 94.2\% under the nominal training geometry, we observe a pronounced performance drop under realistic geometry shifts, including sensor height changes and small tilt angles. These perturbations induce systematic intensity scaling and angle-dependent radar cross section (RCS) effects, pushing features out of distribution and reducing macro-F1 to around 68.5\%. We analyze these failure modes and outline practical directions for improving robustness with normalization, geometry augmentation, and motion-aware features.
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