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

翻译：可靠的雷达惯性里程计（RIO）需要抑制IMU偏置漂移，这一挑战在地下环境中因极端温度和重力引起的加速度而加剧。低成本IMU（如Pixhawk）与FMCW TI IWR6843AOP EVM雷达配对使用时，会因漂移导致性能下降，加之雷达回波稀疏、噪声大且闪烁，使得融合稳定性不及基于LiDAR的里程计。然而，LiDAR在烟雾、灰尘和气溶胶环境中失效，而FMCW雷达在这些情况下仍保持紧凑、轻量、经济高效且鲁棒的特性。为应对这些挑战，我们提出了一种两阶段MRIO框架，结合IMU偏置估计器，在受烟雾影响的GPS拒止地下环境中实现鲁棒的定位与建图。基于雷达的自体速度估计通过最小二乘法构建，并集成到EKF中进行在线IMU偏置校正；校正后的IMU加速度与多个雷达及IMU的异构测量值融合以优化里程计。该框架还通过利用机器人估计的平移和旋转位移，进一步支持纯雷达建图。在地下实地试验中，MRIO实现了鲁棒的定位与建图，性能优于EKF-RIO。它在经济高效的FMCW雷达配置和不同IMU（包括Pixhawk及VectorNav等更高精度单元）上均保持准确性，展现了良好的适应性。该实现将作为开源资源提供给社区（代码发布于https://github.com/LTU-RAI/MRIO）。