利用雷达阴影重建近距垂直物体的三维倾角。
Beyond a Shadow of a Doubt: Close Proximity Geometry Reconstruction Using FMCW Radar Shadow Effects
- 通过车架遮挡形成的雷达阴影，分析回波边界推算物体倾角。
- 在真实雷达数据上实现了倾角估计，误差受物体分割精度影响。
- 适合需要恶劣环境感知的自动驾驶系统，拓展了雷达几何应用。
自主系统在恶劣条件下的可靠感知仍具挑战，因摄像头和激光雷达在光照或天气不佳时性能下降。毫米波调频连续波（FMCW）雷达具有抗干扰能力，但其俯仰方向信息缺失限制了几何推理。本文观察到车辆底盘会遮挡雷达波束，形成独特的几何阴影，且该阴影具有可重复性，可用于推断与之交叠的物体信息。基于此，提出一种无需场景先验的解析方法，通过雷达回波边界与物体开角间的闭式映射，恢复近距细长垂直物体的3D平面内倾角。仿真与实际测试使用Navtech CTS350-X雷达验证，表明在实际条件下可实现倾角估计，主要瓶颈在于雷达扫描中物体的分割精度。本工作揭示车架阴影为新型几何线索，将二维旋转雷达的应用从定位拓展至三维场景重建。
原文摘要 · Abstract (English)
Reliable perception in adverse conditions remains challenging for autonomous systems, as cameras and LiDAR degrade in poor lighting or weather. Millimetre-wave FMCW radar is robust to such conditions, but its elevation collapse limits geometric reasoning. We observe that vehicle chassis occlude radar rays and form a distinctive geometric shadow, and its consistency can enable us to infer useful information about objects whose returns intersect this shadow. Motivated by this observation, we propose a method to recover the 3D, in-plane inclination of nearby slender vertical objects from this cue. The object inclination is retrieved without assumptions about the wider scene, but through an analytical, closed-form mapping between its radar return boundaries and the opening angle. Validation in simulation and experimentation on a Navtech CTS350-X radar shows that inclinations can be estimated under practical conditions, with segmentation of the object in the radar scan emerging as the main bottleneck. This work highlights chassis shadows as a novel geometric cue, extending the role of 2D rotating radar beyond localisation and toward 3D scene reconstruction.
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