用分层融合方法解决雷达惯性系统漂移问题，提升定位精度和实时性。
H-RINS: Hierarchical Tightly-coupled Radar-Inertial State Estimation via Smoothing and Mapping
- 分层设计：高速重置图与持久全局图协同，实现高频率状态更新
- 在真实场景下实现低于0.5%的位姿误差，速度达实时以上
- 适合自动驾驶、机器人导航等对实时性与精度要求高的场景
毫米波雷达可在视觉退化环境下提供鲁棒感知，但雷达-惯性估计仍易产生漂移：体坐标系下的稀疏速度测量对绝对姿态约束弱，导致滑动窗口估计器在短时窗内难以观测IMU偏差。本文提出一种紧耦合的分层雷达-惯性因子图，将估计解耦为高频重置图与持久全局图。重置图融合IMU预积分、雷达速度与自适应零速停机（ZUPT），生成平滑低延迟里程计，用于实时控制。持久图通过关键帧几何建图与回环检测维护完整状态（位姿、速度、偏差）。其完全可观测的偏差及其精确协方差持续作为先验注入重置图，有效抑制积分漂移。大量实验表明，该方法在超过实时的速度下实现高精度、低漂移估计。代码与数据集将在论文录用后公开。
原文摘要 · Abstract (English)
Millimeter-wave radar enables robust perception in visually degraded environments, yet radar-inertial estimation remains prone to drift: sparse body-frame velocity measurements weakly constrain absolute orientation, leaving IMU biases poorly observable over the short horizons of sliding-window estimators. We propose a tightly coupled, hierarchical radar-inertial factor graph that decouples estimation into a high-rate resetting graph and a persistent global graph. The resetting graph fuses IMU preintegration, radar velocities, and adaptive ZUPT to produce smooth, low-latency odometry for real-time control. The persistent graph maintains a full state (poses, velocities, and biases) via keyframe-based geometric mapping and loop closures. Fully observable biases and their exact covariances are continuously injected from the persistent graph as priors into the resetting graph, anchoring the high-rate estimator against integration drift. Extensive evaluations demonstrate high accuracy and drift-reduced estimation at faster than real-time speeds. Code and datasets will be released upon paper acceptance.
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