Abstract
Most existing drone-based inspection systems require the drone to fly dangerously close to the target or follow complex flight paths to capture small details. In addition, drone flight is affected by disturbances and localization inaccuracies, which can cause the drone to lose sight of its supposed target when it has a narrow view. Furthermore, trajectory planning often requires prior information about the target's geometry, position, and orientation, which is not always available for non-structural targets such as trees, vehicles, or people. To address these challenges, this paper presents aerial_micro_inspection, a generic pipeline for aerial micro-inspection across different use cases. The pipeline assumes a PX4-powered drone equipped with two cameras: (i) a zoomed, gimbal-mounted inspection camera that captures fine details without requiring the drone to fly very close to the target, and (ii) a wide-field-of-view stereo navigation camera that acquires the target surface on site, estimates its range, and partitions it into smaller inspection regions. In addition, a vision-based feedback loop compensates for drone motion while the inspection camera visits small partitions of a larger surface. We evaluate the pipeline in simulation and real-world experiments, mainly in two use-case scenarios: tree inspection for detecting oak processionary caterpillars and their eggs, and greenhouse inspection of sticky traps for detecting whiteflies. The results show improved coverage robustness under drone disturbances in simulation, as well as effective detection of caterpillars and eggs and high-detail imaging of insects in real-world experiments. The pipeline is open-source, developed in ROS 2, and can be adapted to new applications by replacing the surface-segmentation and micro-target detection checkpoints. The code is available at: this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2606.11419
