# 用于雷达自速度估计的 Dense Soft Weighting

> 原文链接：https://robotics.hellobike.com/papers/2026-07-29-2607-26980
> 频道：papers | 更新日期：2026-07-29 | 标签：arxiv, cs.RO
> 核心摘要：针对退化视觉环境中的雷达自速度估计，提出 Dense Soft Weighting 前端，并在三个数据集和嵌入式硬件上验证。

# 用于雷达自速度估计的 Dense Soft Weighting

::: v-pre
> 原文链接：[Dense Soft Weighting for Radar Ego-Velocity Estimation](https://arxiv.org/abs/2607.26980)（arXiv 2607.26980 · 发布日期：2026-07-29）

**Dense Soft Weighting for Radar Ego-Velocity Estimation**

针对退化视觉环境中的雷达自速度估计，提出 Dense Soft Weighting 前端，并在三个数据集和嵌入式硬件上验证。

## 核心要点

- 相机和 LiDAR 在退化视觉环境中可能不可靠，毫米波雷达可直接感知多普勒速度。
- 方法将每个距离-多普勒单元映射为连续置信度，避免 CFAR 二值阈值丢弃弱回波。
- 采用确定性鲁棒加权最小二乘估计自速度，并由加权测量闭式得到速度协方差。
- 在两个公开数据集和一个自采数据集上，相比最强 CFAR 点云基线平均绝对位姿误差降低 31-45%，并可在嵌入式硬件实时运行。

**验证范围与局限**：验证范围为两个公开数据集和一个自采数据集，均使用相同惯性后端。

## 英文摘要 (Abstract)

Sensing ego-velocity estimation is fundamental to state estimation in visually degraded environments, where camera- and LiDAR-based pipelines can become unreliable. Millimetre-wave radar is well suited to these conditions because it provides direct Doppler velocity sensing and remains robust to poor illumination, textureless scenes, and airborne particulates. However, conventional radar ego-velocity pipelines typically apply constant false alarm rate (CFAR) thresholding to convert dense radar spectra into sparse point clouds, prematurely discarding sub-threshold returns that may still retain useful Doppler motion cues. We present Dense Soft Weighting, an analytic radar front-end that maps every range-Doppler cell to a continuous confidence metric rather than enforcing a binary detection threshold. Ego-velocity is then estimated using a deterministic robust weighted least-squares formulation, while the same weighted measurements provide a closed-form, measurement-derived velocity covariance for integration with a shared inertial back-end. The method requires no platform-specific training data or learning-based uncertainty model, supporting transfer across single-chip radar configurations. Across two public datasets and one self-collected dataset, Dense Soft Weighting reduces mean absolute pose error by 31-45% relative to the strongest CFAR point-cloud baseline under an identical inertial back-end, while running in real time on embedded hardware.

作者：Atar Fuady Babgei, Chenyu Zhao, Michael Breza, Julie A. McCann
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## 原始参考来源 (Sources)

- [arXiv 2607.26980：Dense Soft Weighting for Radar Ego-Velocity Estimation](https://arxiv.org/abs/2607.26980)