一种用于空中机器人微检测的模块化双相机流水线
A Modular Dual-Camera Pipeline for Micro-Inspection Using Aerial Robots
AI总结：
提出一种模块化双相机空中微检测流水线，通过变焦云台相机和广角立体导航相机协同工作，结合视觉反馈回路，实现对树木和温室粘虫板等非结构目标的鲁棒微检测。
摘要：
现有大多数基于无人机的检测系统要求无人机危险地靠近目标飞行或遵循复杂飞行路径以捕获小细节。此外，无人机飞行受干扰和定位不准确的影响，当视野狭窄时可能导致无人机丢失目标。此外，轨迹规划通常需要目标几何、位置和方向的先验信息，这对于非结构目标（如树木、车辆或人员）并不总是可用。为解决这些挑战，本文提出了 aerial_micro_inspection，一种适用于不同用例的通用空中微检测流水线。该流水线假设一架搭载PX4的无人机配备两个摄像头：(i) 一个变焦云台检测摄像头，无需无人机飞得离目标很近即可捕获精细细节；(ii) 一个宽视场立体导航摄像头，现场获取目标表面，估计其距离，并将其分割成较小的检测区域。此外，当检测摄像头访问较大表面的小分区时，基于视觉的反馈回路补偿无人机运动。我们在仿真和真实实验中评估了该流水线，主要在两种用例场景中：用于检测橡树行军虫及其卵的树木检测，以及用于检测粉虱的温室粘虫板检测。结果显示，在仿真中，无人机干扰下的覆盖鲁棒性得到改善，在真实实验中，有效检测了幼虫和卵，并对昆虫进行了高细节成像。该流水线是开源的，基于ROS 2开发，可通过替换表面分割和微目标检测检查点来适应新应用。代码见：this https URL
英文摘要：
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: https://github.com/SaxionMechatronics/aerial_micro_inspection
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
