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

TL;DR

This paper introduces a rotating mmWave radar system for agricultural UAVs that enhances terrain perception accuracy and coverage in complex farmland environments, enabling more reliable terrain following.

Contribution

A mechanically rotating radar design and a pose-consistent reconstruction pipeline are proposed to improve terrain estimation in agricultural UAVs under challenging conditions.

Findings

Achieved an F1 score of 94.42 for ground segmentation.

Enhanced terrain coverage compared to fixed-view radar systems.

Demonstrated robustness in real agricultural UAV field experiments.

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.