Robust 4D Radar-aided Inertial Navigation for Aerial Vehicles

While LiDAR and cameras are becoming ubiquitous for unmanned aerial vehicles (UAVs) but can be ineffective in challenging environments, 4D millimeter-wave (MMW) radars that can provide robust 3D ranging and Doppler velocity measurements are less exploited for aerial navigation. In this paper, we develop an efficient and robust error-state Kalman filter (ESKF)-based radar-inertial navigation for UAVs. The key idea of the proposed approach is the point-to-distribution radar scan matching to provide motion constraints with proper uncertainty qualification, which are used to update the navigation states in a tightly coupled manner, along with the Doppler velocity measurements. Moreover, we propose a robust keyframe-based matching scheme against the prior map (if available) to bound the accumulated navigation errors and thus provide a radar-based global localization solution with high accuracy. Extensive real-world experimental validations have demonstrated that the proposed radar-aided inertial navigation outperforms state-of-the-art methods in both accuracy and robustness.

@article{zhu2025_2502.15452,
  title={ Robust 4D Radar-aided Inertial Navigation for Aerial Vehicles },
  author={ Jinwen Zhu and Jun Hu and Xudong Zhao and Xiaoming Lang and Yinian Mao and Guoquan Huang },
  journal={arXiv preprint arXiv:2502.15452},
  year={ 2025 }
}