[论文解读] Indoor Testing and Simulation Platform for Close-distance Visual Inspection of Complex Structures using Micro Quadrotor UAV

Zhexiong Shang, Zhigang Shen|arXiv (Cornell University)|Apr 10, 2019

Robotics and Sensor-Based Localization参考文献 16被引用 7

一句话总结

本文提出了一种基于微型四旋翼无人机（Crazyflie 2.0）的低成本室内测试与仿真平台，配备机载FPV摄像头和Loco定位系统，用于验证近距离复杂结构视觉检查中的自主路径规划。该平台能够实现安全、可重复且成本低廉的测试，具备高质量的视觉数据和精确的路径跟踪能力，即使存在轻微跟踪误差，也适用于实际检查应用。

ABSTRACT

In recent years, using drone, also known as unmanned aerial vehicle (UAV), in close-distance visual inspection has became an active area in many disciplines. However, many challenges still remain before we can achieve autonomous inspection, especially when inspecting complex structures. The complex civil structures, such as bridges, dams and wind turbines, are large-scale and geometrical complicated. It requires sophisticated path planning algorithms to achieve close-distance inspection and, at the same time, avoid collisions. In practice, directly deploying the path planning result on such structures is error prone, costly, and full of hazards. In this paper, rely on micro quadrotor UAV, the authors present an affordable experimental platform for testing drone-based path planning result. The platform allows the users to conduct many path planning experiments at any time without worrying expensive and time consuming outdoor test flying. This platform is developed based on the bundle of Crazyflie, which includes Crazyflie 2.0 quadrotor, Crazyradio and loco positioning system (LPS). Equipped with an onboard micro FPV camera, the visual data can be lively streamed to the host computer during flight. The functions of manual configuration and waypoints control are explicitly designed in this platform to increase its flexibility and performance on path following and debugging. To evaluate the practicability of the proposed test platform, two existing drone-based path planning algorithms are tested. The results show that even though certain level of error existed, the quality of visual data and accuracy of path following are high enough for simulating most practical inspection applications.

研究动机与目标

• 解决基于自主无人机的复杂土木结构检查在室外测试中成本高、风险大且不切实际的问题。
• 开发一种低成本、安全且可重复的室内实验平台，用于在实际部署前验证路径规划算法。
• 通过实时视觉数据流和精确的位置控制，实现近距离视觉检查的高保真仿真。
• 评估微型无人机在受控室内条件下执行实际检查任务的可行性。
• 证明即使存在轻微的路径跟踪误差，也不会影响检查目的所需的视觉数据质量。

提出的方法

• 该平台以Crazyflie 2.0微型四旋翼无人机为核心，配备微型FPV摄像头，可将实时视频流传输至主机计算机。
• Loco定位系统（LPS）提供厘米级室内定位，实现精确的轨迹跟踪。
• 实现手动配置和航路点控制模式，以增强灵活性并支持路径跟踪的调试。
• 系统支持与外部路径规划算法集成，用于测试和验证。
• 飞行过程中捕获并流式传输视觉数据，以模拟真实的检查场景。
• 该平台可在无室外飞行风险或成本的情况下，实现路径规划算法的快速迭代。

实验结果

研究问题

• RQ1 低成本室内无人机平台能否有效模拟复杂结构的近距离视觉检查？
• RQ2 路径跟踪误差在多大程度上影响检查用途的视觉数据质量？
• RQ3 微型四旋翼无人机、LPS与机载摄像头的组合是否足以在受控环境中实现可靠的路径跟踪和数据采集？
• RQ4 该平台在实际部署前如何支持路径规划算法的验证？
• RQ5 该平台能否以高保真度和可重复性复现实际检查条件？

主要发现

• 该平台成功实现了在室内环境中对路径规划算法的安全、可重复且低成本的测试。
• 尽管存在轻微的路径跟踪误差，视觉数据质量仍足够支持大多数实际检查应用。
• 系统展示了在实时视觉反馈下精确的路径跟踪能力，适用于复杂结构检查任务。
• Crazyflie 2.0、LPS与机载FPV摄像头的集成，为算法验证提供了可靠且灵活的测试平台。
• 该平台支持手动和自动航路点控制，提升了其在调试和实验中的可用性。
• 结果证实，使用该设置进行室内仿真，是昂贵且危险的室外测试的可行替代方案。