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State estimation is a crucial component for the successful implementation ofrobotic systems, relying on sensors such as cameras, LiDAR, and IMUs. However,in real-world scenarios, the performance of these sensors is degraded bychallenging environments, e.g. adverse weather conditions and low-lightscenarios. The emerging 4D imaging radar technology is capable of providingrobust perception in adverse conditions. Despite its potential, challengesremain for indoor settings where noisy radar data does not present cleargeometric features. Moreover, disparities in radar data resolution and field ofview (FOV) can lead to inaccurate measurements. While prior research hasexplored radar-inertial odometry based on Doppler velocity information,challenges remain for the estimation of 3D motion because of the discrepancy inthe FOV and resolution of the radar sensor. In this paper, we address Dopplervelocity measurement uncertainties. We present a method to optimize body framevelocity while managing Doppler velocity uncertainty. Based on ourobservations, we propose a dual imaging radar configuration to mitigate thechallenge of discrepancy in radar data. To attain high-precision 3D stateestimation, we introduce a strategy that seamlessly integrates radar data witha consumer-grade IMU sensor using fixed-lag smoothing optimization. Finally, weevaluate our approach using real-world 3D motion data.

Multi-Radar Inertial Odometry for 3D State Estimation using mmWave Imaging Radar