惯性测量单元漂移如何影响多雷达惯性里程计用于地下地形地面机器人
How IMU Drift Influences Multi-Radar Inertial Odometry for Ground Robots in Subterranean Terrains
AI总结：
本文提出一种两阶段MRIO框架，通过IMU偏移估计和雷达数据融合，提升地下环境中机器人定位和映射的鲁棒性。
摘要：
可靠的雷达惯性里程计（RIO）需要缓解IMU偏移漂移，这在地下环境中变得更加困难，因为极端温度和重力引起的加速度加剧了这一挑战。成本效益高的IMU，如Pixhawk，当与FMCW TI IWR6843AOP EVM雷达配对时，会受到漂移引起的退化，这进一步加剧了稀疏、噪声和闪烁的雷达返回数据，使融合不如基于激光雷达的里程计稳定。然而，在烟雾、尘埃和气溶胶中，激光雷达失效，而FMCW雷达仍能保持紧凑、轻量、成本效益和鲁棒性。为了解决这些挑战，我们提出了一种两阶段MRIO框架，该框架结合了IMU偏移估计器，以在GPS拒止的地下环境中实现稳健的定位和地图构建。通过最小二乘法公式化雷达基于的自我速度估计，并将其纳入EKF中进行在线IMU偏移校正；校正后的IMU加速度与多个雷达和IMU的异构测量相结合，以优化里程计。所提出的框架还支持仅雷达映射，通过利用机器人估计的平移和旋转位移。在地下实地试验中，MRIO实现了稳健的定位和映射，优于EKF-RIO。它在成本效益高的FMCW雷达设置和不同IMU中保持了准确性，展示了在Pixhawk和更高等级的VectorNav单元中的鲁棒性。该实现将作为开源资源提供给社区（代码可在https://github.com/LTU-RAI/MRIO获取）。
英文摘要：
Reliable radar inertial odometry (RIO) requires mitigating IMU bias drift, a challenge that intensifies in subterranean environments due to extreme temperatures and gravity-induced accelerations. Cost-effective IMUs such as the Pixhawk, when paired with FMCW TI IWR6843AOP EVM radars, suffer from drift-induced degradation compounded by sparse, noisy, and flickering radar returns, making fusion less stable than LiDAR-based odometry. Yet, LiDAR fails under smoke, dust, and aerosols, whereas FMCW radars remain compact, lightweight, cost-effective, and robust in these situations. To address these challenges, we propose a two-stage MRIO framework that combines an IMU bias estimator for resilient localization and mapping in GPS-denied subterranean environments affected by smoke. Radar-based ego-velocity estimation is formulated through a least-squares approach and incorporated into an EKF for online IMU bias correction; the corrected IMU accelerations are fused with heterogeneous measurements from multiple radars and an IMU to refine odometry. The proposed framework further supports radar-only mapping by exploiting the robot's estimated translational and rotational displacements. In subterranean field trials, MRIO delivers robust localization and mapping, outperforming EKF-RIO. It maintains accuracy across cost-efficient FMCW radar setups and different IMUs, showing resilience with Pixhawk and higher-grade units such as VectorNav. The implementation will be provided as an open-source resource to the community (code available at https://github.com/LTU-RAI/MRIO
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