How IMU Drift Influences Multi-Radar Inertial Odometry for Ground Robots in Subterranean Terrains

Abstract

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 odometries. Yet, LiDAR fails under smoke, dust and aerosols, whereas FMCW radars remain compact, lightweight, cost-effective, and robust to 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. In this, radar's ego velocity estimation is formulated through a least square approach and incorporated into an EKF for online IMU bias correction, thus, the corrected IMU accelerations are fused with heterogeneous measurements from multiple radars and 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 single stage EKF-RIO. It maintains accuracy across cost-efficient FMCW radar setups and different IMUs, with resilience on Pixhawk and using higher-grade units like VectorNav. The implementation will be provided as an open-source resource to the community:url{https://github.com/LTU-RAI/MRIO}