In this paper, we propose a vision-based solution for indoor Micro Air Vehicle (MAV) navigation, with a primary focus on its application within autonomous warehouses. Our work centers on the utilization of a single camera as the primary sensor for tasks such as detection, localization, and path planning. To achieve these objectives, we implement the HSV color detection and the Hough Line Transform for effective line detection within warehouse environments. The integration of a Kalman filter into our system enables the camera to track yellow lines reliably. We evaluated the performance of our vision-based line following algorithm through various MAV flight tests conducted in the Gazebo 11 platform, utilizing ROS Noetic. The results of these simulations demonstrate the system capability to successfully navigate narrow indoor spaces. Our proposed system has the potential to significantly reduce labor costs and enhance overall productivity in warehouse operations. This work contributes to the growing field of MAV applications in autonomous warehouses, addressing the need for efficient logistics and supply chain solutions.

翻译：本文提出一种基于视觉的室内微型飞行器（MAV）导航解决方案，重点研究其在自主仓库中的应用。我们以单目相机作为核心传感器，完成检测、定位及路径规划等任务。为实现上述目标，采用HSV颜色检测与霍夫线变换算法，在仓库环境中实现高效线检测。通过集成卡尔曼滤波器，系统可实现黄线的稳定跟踪。我们在Gazebo 11仿真平台中，利用ROS Noetic框架开展了多项MAV飞行测试，以评估所提视觉循线算法的性能。仿真结果表明，该系统可在狭窄室内空间成功完成自主导航。所提方案有望显著降低仓库运营中的人力成本并提升整体生产力。这项研究为自主仓库中MAV应用这一新兴领域做出贡献，回应了高效物流与供应链解决方案的迫切需求。