The interest in single-chip mmWave Radar is driven by their compact form factor, cost-effectiveness, and robustness under harsh environmental conditions. Despite its promising attributes, the principal limitation of mmWave radar lies in its capacity for autonomous yaw rate estimation. Conventional solutions have often resorted to integrating inertial measurement unit (IMU) or deploying multiple radar units to circumvent this shortcoming. This paper introduces an innovative methodology for two-dimensional ego-motion estimation, focusing on yaw rate deduction, utilizing solely mmWave radar sensors. By applying a weighted Iterated Closest Point (ICP) algorithm to register processed points derived from heatmap data, our method facilitates 2D ego-motion estimation devoid of prior information. Through experimental validation, we verified the effectiveness and promise of our technique for ego-motion estimation using exclusively radar data.

翻译：单片毫米波雷达因其紧凑的尺寸、成本效益以及在恶劣环境下的鲁棒性而备受关注。尽管具有这些优势，毫米波雷达的主要局限性在于其自主偏航角速率估计的能力不足。传统解决方案通常需要集成惯性测量单元或部署多个雷达单元来弥补这一缺陷。本文提出了一种创新的二维自运动估计方法，专注于偏航角速率的推导，仅使用毫米波雷达传感器。通过对热图数据衍生的处理点应用加权迭代最近点算法进行配准，我们的方法无需先验信息即可实现二维自运动估计。通过实验验证，我们证实了该技术在仅使用雷达数据进行自运动估计时的有效性与前景。