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

TL;DR

This paper introduces a two-stage multi-radar inertial odometry framework that effectively mitigates IMU drift, enabling robust localization and mapping for ground robots in subterranean environments with challenging conditions.

Contribution

It presents a novel MRIO framework combining IMU bias estimation and radar-based ego-velocity estimation, improving localization accuracy in GPS-denied subterranean terrains.

Findings

Outperforms EKF-RIO in subterranean trials

Maintains accuracy with low-cost IMUs and radars

Supports radar-only mapping in challenging environments

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 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