H-RINS: Hierarchical Tightly-coupled Radar-Inertial State Estimation via Smoothing and Mapping
arXiv:2603.14109v3 Announce Type: replace Abstract: 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 pers
Overview
arXiv:2603.14109v3 Announce Type: replace Abstract: 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.
Source
Originally published at arxiv.org.
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Source: https://arxiv.org/abs/2603.14109
