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

2026-02-27

AI Summary

This work addresses the challenge of unstable multi-radar inertial odometry (MRIO) fusion in underground environments, where low-cost IMUs suffer from bias drift induced by temperature variations and gravity, compounded by sparse, noisy, and flickering FMCW radar echoes. To tackle this, the authors propose a two-stage MRIO framework: first estimating radar ego-motion velocity via least squares, then performing online IMU bias correction through an extended Kalman filter (EKF) to tightly couple multi-radar and IMU measurements. This approach is the first to integrate IMU bias estimation with multi-FMCW-radar fusion for underground robotics, enabling radar-only mapping when LiDAR fails (e.g., in smoky or dusty conditions) and significantly enhancing localization and mapping robustness in GPS-denied settings. Real-world experiments demonstrate that MRIO outperforms EKF-RIO on both low-cost (e.g., Pixhawk) and high-precision (e.g., VectorNav) IMUs, with code to be publicly released.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Multi-instance/Multi-view Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

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

Problem

• IMU drift
• radar inertial odometry
• subterranean environments
• GPS-denied localization
• FMCW radar

Innovation

• Multi-Radar Inertial Odometry
• IMU Bias Estimation
• FMCW Radar
• Underground Localization
• EKF-based Sensor Fusion