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.

翻译：状态估计是机器人系统成功实现的关键组成部分，依赖摄像头、激光雷达和惯性测量单元等传感器。然而，在真实场景中，恶劣天气条件和弱光环境等挑战会降低这些传感器的性能。新兴的四维成像雷达技术能够在恶劣条件下提供稳健的感知能力。尽管其潜力巨大，但在室内环境中仍面临挑战——雷达数据噪声大且缺乏清晰的几何特征。此外，雷达数据分辨率和视场角的差异可能导致不精确的测量。虽然已有研究基于多普勒速度信息探索了雷达-惯性里程计，但由于雷达传感器视场角和分辨率的差异，三维运动估计仍存在挑战。本文针对多普勒速度测量中的不确定性展开研究，提出了一种在管理多普勒速度不确定性的同时优化本体坐标系速度的方法。基于实验观察，我们提出了一种双成像雷达配置方案以缓解雷达数据差异问题。为实现高精度三维状态估计，我们引入了一种策略——通过固定滞后平滑优化，将雷达数据与消费级惯性测量单元无缝融合。最后，我们使用真实三维运动数据对所提方法进行了评估。