LoCal-RIO: Radar-Inertial Odometry with Loop-Closure IMU Bias Calibration

Abstract:Millimeter-wave radar enables robust perception in visually degraded environments, yet radar-inertial estimation remains prone to drift: body-frame velocity measurements do not constrain heading and position, and the gyroscope bias, which governs heading drift, is poorly observable over the short horizons of sliding-window estimators. We propose a hierarchical radar-inertial factor graph that separates estimation into a fixed-lag navigation graph, which fuses IMU preintegration, radar velocities, ZUPT, and ground-plane constraints into smooth, low-latency odometry, and a keyframe mapping graph, which combines this odometry with submap registration and loop closures. Loop closures additionally calibrate the IMU: the part of a loop residual explained by a bias error is estimated through preintegration Jacobians chained over the loop interval and enters the navigation graph as a prior on the bias alone. Since this calibration is irreversible, it uses only loop closures accepted by the mapping graph and a cycle-consistency test. Extensive evaluations demonstrate high accuracy and drift-reduced estimation at real-time speeds.

Robotics (cs.RO)

arXiv:2603.14109 [cs.RO]

(or arXiv:2603.14109v4 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2603.14109 Focus to learn more arXiv-issued DOI via DataCite

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