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<p>Reliable radar inertial odometry (RIO) demands precise correction of inertial measurement unit (IMU) acceleration drift during the synchronization of heterogeneous sensors. This bias drift, most evident in acceleration measurements, worsens in subterranean environments due to extreme cold or gravitational components acting along the robot’s feed-forward axis. Left uncorrected, such drifts substantially degrade sensor fusion performance, leading to dead-reckoning, particularly when employing cost-effective platforms such as the Pixhawk IMU in combination with multiple mmWave radars. In addition, radar point clouds differ fundamentally from LiDAR data, being inherently sparse, noisy, and prone to flickering effects, which further complicates the challenge of achieving stable and reliable odometry. To address these challenges, this article presents a novel two-stage multi-radar inertial odometry (MRIO) framework for resilient localization and mapping in GPS-denied subterranean environments. In the first stage, radar ego-velocity estimation, formulated through a least-squares approach, is incorporated into an Extended Kalman Filter (EKF) for IMU bias correction. The resulting drift-free accelerations are then fused in the second stage with measurements from multiple radars and the IMU to refine odometry performance. Beyond odometry, the framework also supports radar-based mapping by leveraging the generated robot’s translational and angular displacement by the proposed framework. Starting with a range-based outlier filter, least-squares ego-velocity reconstruction, the MRIO framework enables robust ego-velocity estimation, localisation, and mapping using only radar and IMU measurements. The proposed framework is extensively validated across multiple experimental scenarios, including real-time deployment in smoke-filled subterranean environments. Comparative evaluations involving multiple FMCW radar setups, LiDAR inertial odometry (LIO), and prior EKF-RIO methods further validate the framework’s robustness and its consistent capability to achieve precise localization and mapping in challenging perceptual environments.</p>

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Resilient Multi-Radar Inertial Odometry with Adaptive Bias Correction for Localization in Smoke-Filled Subterranean Environments

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月17日

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  • Moumita Mukherjee,
  • Magnus Norén,
  • Anton Koval,
  • Avijit Banerjee,
  • George Nikolakopoulos

摘要

Reliable radar inertial odometry (RIO) demands precise correction of inertial measurement unit (IMU) acceleration drift during the synchronization of heterogeneous sensors. This bias drift, most evident in acceleration measurements, worsens in subterranean environments due to extreme cold or gravitational components acting along the robot’s feed-forward axis. Left uncorrected, such drifts substantially degrade sensor fusion performance, leading to dead-reckoning, particularly when employing cost-effective platforms such as the Pixhawk IMU in combination with multiple mmWave radars. In addition, radar point clouds differ fundamentally from LiDAR data, being inherently sparse, noisy, and prone to flickering effects, which further complicates the challenge of achieving stable and reliable odometry. To address these challenges, this article presents a novel two-stage multi-radar inertial odometry (MRIO) framework for resilient localization and mapping in GPS-denied subterranean environments. In the first stage, radar ego-velocity estimation, formulated through a least-squares approach, is incorporated into an Extended Kalman Filter (EKF) for IMU bias correction. The resulting drift-free accelerations are then fused in the second stage with measurements from multiple radars and the IMU to refine odometry performance. Beyond odometry, the framework also supports radar-based mapping by leveraging the generated robot’s translational and angular displacement by the proposed framework. Starting with a range-based outlier filter, least-squares ego-velocity reconstruction, the MRIO framework enables robust ego-velocity estimation, localisation, and mapping using only radar and IMU measurements. The proposed framework is extensively validated across multiple experimental scenarios, including real-time deployment in smoke-filled subterranean environments. Comparative evaluations involving multiple FMCW radar setups, LiDAR inertial odometry (LIO), and prior EKF-RIO methods further validate the framework’s robustness and its consistent capability to achieve precise localization and mapping in challenging perceptual environments.