Terrain Perception for Agricultural UAVs in Complex Farmland via Rotating mmWave Radar

2026-05-02

AI Summary

This study addresses the challenge of accurate terrain perception for agricultural drones operating in complex farmland environments, where occlusions, elevation variations, and environmental disturbances often degrade low-altitude flight performance. To overcome these limitations, the authors propose a terrain-awareness framework based on a low-cost rotating millimeter-wave radar. By mechanically rotating the radar to expand its field of view, they develop a pose-consistent pipeline for sparse radar point cloud registration and filtering, followed by a novel ground segmentation and surface reconstruction algorithm tailored for high-noise, partially observable data. As the first work to apply a rotating millimeter-wave radar to terrain perception in agricultural drones, the method achieves a ground segmentation F1-score of 94.42 in real-world experiments—significantly outperforming existing approaches (90.48)—and demonstrably enhances terrain coverage, estimation accuracy, and flight robustness.

Abstract

Accurate terrain perception is essential for terrain-following flight of agricultural unmanned aerial vehicles (UAVs), yet remains challenging in real-world farmland due to occlusions, complex terrain geometry, and environmental disturbances. Millimeter-wave (mmWave) radar is a promising sensing modality for this task due to its robustness to adverse conditions; however, existing UAV-mounted radar systems rely on fixed field of view (FoV) and terrain extraction methods designed for dense LiDAR data, leading to incomplete and unreliable terrain estimation. To address these limitations, we present a low-cost rotating mmWave radar-enabled terrain perception framework for agricultural UAVs operating in complex farmland environments. Specifically, a mechanically rotating sensing design is introduced to enlarge spatial coverage and improve terrain observability beyond the limitations of fixed-view radar under dynamic low-altitude flight. Building upon this sensing capability, we further design a pose-consistent terrain reconstruction pipeline tailored for sparse, noisy, and partially observable radar data, enabling reliable ground extraction and continuous terrain surface estimation in challenging agricultural scenarios. The complete system is deployed on a real agricultural UAV platform and comprehensively evaluated through extensive field experiments. Experimental results demonstrate improved terrain coverage and estimation accuracy, achieving an F1 score of 94.42 for ground segmentation, while the closest rival only achieves 90.48. Thus, leading to more robust terrain following flight.

Problem

• terrain perception
• agricultural UAVs
• mmWave radar
• complex farmland
• terrain-following flight

Innovation

• rotating mmWave radar
• terrain perception
• agricultural UAVs
• pose-consistent reconstruction
• sparse radar data