用加速度约束提升雷达自车速度估计的鲁棒性，尤其在点云噪声多时表现更好。
CREVE: An Acceleration-based Constraint Approach for Robust Radar Ego-Velocity Estimation
- 基于惯性测量单元加速度，构建不等式约束滤波器，增强估计稳定性。
- 在两个公开数据集上分别降低36%、78%、12%的绝对轨迹误差。
- 适合高噪声环境下依赖雷达自车速度的导航系统使用。
基于毫米波调频连续波（mmWave FMCW）雷达点云测量的自车速度估计已成为雷达-惯性里程计（RIO）系统的关键组件。传统方法在点云中离群点数量超过内点时性能显著下降，导致导航精度恶化，尤其影响依赖雷达自车速度进行航位推算的RIO系统。本文提出CREVE，一种基于加速度的不等式约束滤波器，利用惯性测量单元（IMU）的额外测量实现鲁棒的自车速度估计。为进一步提升精度与抗传感器误差能力，引入实用的加速度计偏差估计方法及参数自适应规则，动态调整约束以匹配雷达点云内点情况。在两个开源IRS和ColoRadar数据集上的实验表明，所提方法显著优于三种先进方法，绝对轨迹误差分别降低约36%、78%和12%。
原文摘要 · Abstract (English)
Ego-velocity estimation from point cloud measurements of a millimeter-wave frequency-modulated continuous wave (mmWave FMCW) radar has become a crucial component of radar-inertial odometry (RIO) systems. Conventional approaches often exhibit poor performance when the number of outliers in the point cloud exceeds that of inliers, which can lead to degraded navigation performance, especially in RIO systems that rely on radar ego-velocity for dead reckoning. In this paper, we propose CREVE, an acceleration-based inequality constraints filter that leverages additional measurements from an inertial measurement unit (IMU) to achieve robust ego-velocity estimations. To further enhance accuracy and robustness against sensor errors, we introduce a practical accelerometer bias estimation method and a parameter adaptation rule that dynamically adjusts constraints based on radar point cloud inliers. Experimental results on two open-source IRS and ColoRadar datasets demonstrate that the proposed method significantly outperforms three state-of-the-art approaches, reducing absolute trajectory error by approximately 36\%, 78\%, and 12\%, respectively.
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