  State estimation is a crucial component for the successful implementation of
robotic systems, relying on sensors such as cameras, LiDAR, and IMUs. However,
in real-world scenarios, the performance of these sensors is degraded by
challenging environments, e.g. adverse weather conditions and low-light
scenarios. The emerging 4D imaging radar technology is capable of providing
robust perception in adverse conditions. Despite its potential, challenges
remain for indoor settings where noisy radar data does not present clear
geometric features. Moreover, disparities in radar data resolution and field of
view (FOV) can lead to inaccurate measurements. While prior research has
explored radar-inertial odometry based on Doppler velocity information,
challenges remain for the estimation of 3D motion because of the discrepancy in
the FOV and resolution of the radar sensor. In this paper, we address Doppler
velocity measurement uncertainties. We present a method to optimize body frame
velocity while managing Doppler velocity uncertainty. Based on our
observations, we propose a dual imaging radar configuration to mitigate the
challenge of discrepancy in radar data. To attain high-precision 3D state
estimation, we introduce a strategy that seamlessly integrates radar data with
a consumer-grade IMU sensor using fixed-lag smoothing optimization. Finally, we
evaluate our approach using real-world 3D motion data.
