用雷达谱与惯性数据自监督融合，提升恶劣环境定位精度。
S$^3$E: Self-Supervised State Estimation for Radar-Inertial System
- 利用雷达信号谱替代稀疏点云，结合惯性信息实现自监督融合
- 通过旋转相关性增强空间结构，解决单次雷达角度分辨率低问题
- 无需地面真值监督，适合复杂场景下无人系统定位
毫米波雷达因其成本低、在恶劣环境下可靠性高，正成为状态估计的热门选择。现有方案多依赖后处理的雷达点云作为路标，但雷达点云固有的稀疏性、多路径效应产生的伪点以及单次啁啾雷达的角度分辨率有限，严重制约了定位性能。为此，我们提出S³E——一种自监督状态估计算法，充分利用更具信息量的雷达信号谱，规避点云稀疏问题，并融合互补的惯性信息以实现高精度定位。S³E充分挖掘外部感知雷达与内部感知惯性传感器之间的关联，实现互补增益。针对角度分辨率低的问题，引入一种新颖的跨模态融合技术，通过捕捉异构数据间的微小旋转位移相关性，增强空间结构信息。实验结果表明，本方法在不依赖定位真值监督的情况下，仍能实现鲁棒且准确的性能。据我们所知，这是首个以互补自监督方式融合雷达谱与惯性数据实现状态估计的工作。
原文摘要 · Abstract (English)
Millimeter-wave radar for state estimation is gaining significant attention for its affordability and reliability in harsh conditions. Existing localization solutions typically rely on post-processed radar point clouds as landmark points. Nonetheless, the inherent sparsity of radar point clouds, ghost points from multi-path effects, and limited angle resolution in single-chirp radar severely degrade state estimation performance. To address these issues, we propose S$^3$E, a \textbf{S}elf-\textbf{S}upervised \textbf{S}tate \textbf{E}stimator that employs more richly informative radar signal spectra to bypass sparse points and fuses complementary inertial information to achieve accurate localization. S$^3$E fully explores the association between \textit{exteroceptive} radar and \textit{proprioceptive} inertial sensor to achieve complementary benefits. To deal with limited angle resolution, we introduce a novel cross-fusion technique that enhances spatial structure information by exploiting subtle rotational shift correlations across heterogeneous data. The experimental results demonstrate our method achieves robust and accurate performance without relying on localization ground truth supervision. To the best of our knowledge, this is the first attempt to achieve state estimation by fusing radar spectra and inertial data in a complementary self-supervised manner.
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