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Robust 4D Radar-aided Inertial Navigation for Aerial Vehicles

Jinwen Zhu, Jun Hu, Xudong Zhao, Xiaoming Lang, Yinian Mao, Guoquan Huang

TL;DR

The paper develops a robust radar‑inertial navigation system for UAVs, integrating a 4D radar with an error‑state Kalman filter (ESKF) backbone to fuse Doppler velocity and scan‑to‑localmap constraints for accurate pose estimation. It introduces robust point‑to‑distribution scan matching, keyframe‑based map matching, and a map‑based radar localization module to bound drift and achieve global localization, validated through extensive ground and flight experiments showing superior accuracy and robustness. The approach maintains real‑time performance on consumer hardware, with ablation studies confirming the positive contribution of Doppler and distribution matching versus traditional point‑to‑point methods. Overall, the work advances radar‑based UAV navigation in adverse weather and cluttered environments, enabling safer and more reliable autonomous operation.

Abstract

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.

Robust 4D Radar-aided Inertial Navigation for Aerial Vehicles

TL;DR

The paper develops a robust radar‑inertial navigation system for UAVs, integrating a 4D radar with an error‑state Kalman filter (ESKF) backbone to fuse Doppler velocity and scan‑to‑localmap constraints for accurate pose estimation. It introduces robust point‑to‑distribution scan matching, keyframe‑based map matching, and a map‑based radar localization module to bound drift and achieve global localization, validated through extensive ground and flight experiments showing superior accuracy and robustness. The approach maintains real‑time performance on consumer hardware, with ablation studies confirming the positive contribution of Doppler and distribution matching versus traditional point‑to‑point methods. Overall, the work advances radar‑based UAV navigation in adverse weather and cluttered environments, enabling safer and more reliable autonomous operation.

Abstract

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.

Paper Structure

This paper contains 25 sections, 10 equations, 4 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Overview of Radar-Inertial Navigation
  4. System Design
  5. Inertial Navigation as Backbone
  6. Radar-Inertial Odometry
  7. Doppler Velocity Measurement
  8. Scan-to-Localmap Matching
  9. Pre-processing Pointcloud
  10. Point-to-Distribution Matching
  11. Iterated Update
  12. Augmenting Localmap
  13. System Initialization
  14. Map-based Radar Localization
  15. Pointcloud Accumulation and Projection
  16. ...and 10 more sections

Figures (4)

  • Figure 1: Our hardware platform used in urban drone delivery. The radar is mounted underneath the drone, with the FOV facing down. RTK-INS is employed to ensure accurate ground truth for evaluation.
  • Figure 2: System overview of the proposed radar-inertial navigation system.
  • Figure 3: Our RIO in ground data.
  • Figure 4: Our RIO in UAV data.