Robust High-Speed State Estimation for Off-road Navigation using Radar Velocity Factors

TL;DR

This paper presents a robust high-speed state estimation method for off-road navigation that integrates FMCW radar velocity measurements with LiDAR and IMU data, improving robustness in challenging environments.

Contribution

It introduces a novel radial speed factor for radar velocity integration into a sliding-window estimator, enhancing robustness against sensor degradation in complex terrains.

Findings

Improved robustness in state estimation under environmental challenges.

Effective fusion of radar velocity, LiDAR, and IMU data at high speeds.

Outperforms existing radar-inertial odometry methods on public datasets.

Abstract

Enabling robot autonomy in complex environments for mission critical application requires robust state estimation. Particularly under conditions where the exteroceptive sensors, which the navigation depends on, can be degraded by environmental challenges thus, leading to mission failure. It is precisely in such challenges where the potential for FMCW radar sensors is highlighted: as a complementary exteroceptive sensing modality with direct velocity measuring capabilities. In this work we integrate radial speed measurements from a FMCW radar sensor, using a radial speed factor, to provide linear velocity updates into a sliding-window state estimator for fusion with LiDAR pose and IMU measurements. We demonstrate that this augmentation increases the robustness of the state estimator to challenging conditions present in the environment and the negative effects they can pose to vulnerable exteroceptive modalities. The proposed method is extensively evaluated using robotic field experiments conducted using an autonomous, full-scale, off-road vehicle operating at high-speeds (~12 m/s) in complex desert environments. Furthermore, the robustness of the approach is demonstrated for cases of both simulated and real-world degradation of the LiDAR odometry performance along with comparison against state-of-the-art methods for radar-inertial odometry on public datasets.