Robust 4D Radar-aided Inertial Navigation for Aerial Vehicles

TL;DR

This paper introduces a robust radar-inertial navigation system for UAVs using 4D millimeter-wave radars, which enhances accuracy and robustness in challenging environments compared to traditional LiDAR and camera-based methods.

Contribution

The paper presents a novel error-state Kalman filter approach with point-to-distribution scan matching and keyframe-based map matching for improved UAV navigation.

Findings

Outperforms state-of-the-art methods in accuracy.

Demonstrates robustness in challenging environments.

Provides high-precision global localization.

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.