发表机构
Imperial College London（帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对视觉退化环境中雷达自速度估计的传统CFAR方法易丢失有效线索的问题，提出密集软加权雷达前端，结合鲁棒加权最小二乘估计自速度，在多数据集上显著降低位姿误差且可实时运行。
AI 中文摘要
在视觉退化环境中，自速度估计是状态估计的基础，基于相机和激光雷达的流程在此类环境中可能变得不可靠。毫米波雷达因能直接感知多普勒速度，且对不良光照、无纹理场景和空气中颗粒物具有鲁棒性，非常适合此类场景。然而，传统雷达自速度流程通常采用恒虚警率（CFAR）阈值处理，将密集雷达频谱转换为稀疏点云，过早丢弃了可能仍包含有用多普勒运动线索的亚阈值回波。本文提出密集软加权（Dense Soft Weighting），这是一种分析型雷达前端，将每个距离-多普勒单元映射为连续置信度指标，而非强制采用二元检测阈值。随后使用确定性鲁棒加权最小二乘公式估计自速度，同时相同的加权测量值提供闭式、由测量值推导的速度协方差，用于与共享惯性后端集成。该方法无需特定平台训练数据或基于学习的不确定性模型，支持在单片雷达配置间迁移。在两个公开数据集和一个自行采集的数据集上，当使用相同惯性后端时，密集软加权相比最强的CFAR点云基线，将平均绝对位姿误差降低了31%-45%，且能在嵌入式硬件上实时运行。
英文摘要
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.
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