Millimeter-wave radar provides robust perception in visually degraded environments. However, radar-inertial state estimation is inherently susceptible to drift. Because radar yields only sparse, body-frame velocity measurements, it provides weak constraints on absolute orientation. Consequently, IMU biases remain poorly observable over the short time horizons typical of sliding-window filters. To address this fundamental observability challenge, we propose a tightly coupled, hierarchical radar-inertial factor graph framework. Our architecture decouples the estimation problem into a high-rate resetting graph and a persistent global graph. The resetting graph fuses IMU preintegration, radar velocities, and adaptive Zero-Velocity Updates (ZUPT) to generate the smooth, low-latency odometry required for real-time control. Concurrently, the persistent graph is a full-state factor graph maintaining the complete information of poses, velocities, and biases by fusing inertial data with keyframe-based geometric mapping and loop closures. Leveraging Incremental Smoothing and Mapping, the persistent graph can operate without explicit marginalization of variables, preserving their information while ensuring long-term bias observability. The cornerstone of our approach is a probabilistic tight-coupling mechanism: fully observable, optimized biases and their exact covariances are continuously injected from the persistent graph into the resetting graph's prior, effectively anchoring the high-rate estimator against integration drift. Extensive evaluations demonstrate our system achieves high accuracy with drift-reduced estimation at 27x real-time execution speeds. We release the implementation code and datasets upon the acceptance of the paper.

翻译：毫米波雷达在视觉退化环境中具备鲁棒的感知能力。然而，雷达-惯性状态估计本质上易受漂移影响。由于雷达仅能提供稀疏的、机体坐标系下的速度测量，其对绝对姿态的约束较弱。因此，在滑动窗口滤波器典型的短时间范围内，IMU偏差的可观测性仍然较差。针对这一根本性的可观测性挑战，我们提出了一种紧耦合的层次化雷达-惯性因子图框架。我们的架构将估计问题解耦为一个高速率重置图和一个持久全局图。重置图融合了IMU预积分、雷达速度以及自适应零速度更新，以生成实时控制所需的平滑、低延迟里程计。同时，持久图是一个全状态因子图，通过将惯性数据与基于关键帧的几何建图及回环检测相融合，持续维护位姿、速度和偏差的完整信息。利用增量平滑与映射技术，持久图无需显式边缘化变量即可运行，在保持变量信息的同时确保了长期偏差可观测性。我们方法的核心是一个概率紧耦合机制：从持久图中持续将完全可观测、优化后的偏差及其精确协方差注入重置图的先验中，从而有效锚定高速率估计器，抑制积分漂移。大量评估表明，我们的系统在达到27倍实时执行速度的同时，实现了高精度且漂移显著降低的估计。论文一经录用，我们将公开实现代码与数据集。