用雷达与惯性传感器融合，实时估计车辆速度并量化误差不确定性。
Uncertainty-Driven Radar-Inertial Fusion for Instantaneous 3D Ego-Velocity Estimation
- 用神经网络处理原始雷达数据，直接输出瞬时速度及不确定性
- 在ColoRadar数据集上误差显著低于现有方法，优于传统扫描匹配技术
- 适合对高精度运动估计有要求的自动驾驶系统使用
我们提出一种通过融合高分辨率成像雷达与惯性测量单元（IMU）来实现自主导航中自车速度估计的方法。针对传统雷达自车运动估计的局限性，该方法采用神经网络处理复数形式的原始雷达数据，直接估计瞬时线性自车速度及其对应的不确定性。该不确定性感知的速度估计结果通过扩展卡尔曼滤波器（EKF）与IMU数据融合，利用网络预测的不确定性动态调整IMU的噪声和偏差参数，从而提升整体运动估计的鲁棒性和精度。我们在公开的ColoRadar数据集上进行了评估，所提方法在误差指标上显著优于当前最接近的公开方法，并超越了所有即时与扫描匹配类技术。
原文摘要 · Abstract (English)
We present a method for estimating ego-velocity in autonomous navigation by integrating high-resolution imaging radar with an inertial measurement unit. The proposed approach addresses the limitations of traditional radar-based ego-motion estimation techniques by employing a neural network to process complex-valued raw radar data and estimate instantaneous linear ego-velocity along with its associated uncertainty. This uncertainty-aware velocity estimate is then integrated with inertial measurement unit data using an Extended Kalman Filter. The filter leverages the network-predicted uncertainty to refine the inertial sensor's noise and bias parameters, improving the overall robustness and accuracy of the ego-motion estimation. We evaluated the proposed method on the publicly available ColoRadar dataset. Our approach achieves significantly lower error compared to the closest publicly available method and also outperforms both instantaneous and scan matching-based techniques.
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