基于深度学习的四旋翼无人机单目视觉避障方法

针对无人机避障问题,提出一种基于深度学习的四旋翼无人机单目视觉避障方法。首先通过目标检测框选出目标在图像中的位置,并通过计算目标选框上下边距的长度,以此来估量出障碍物到无人机之间的距离;然后通过协同计算机判断是否执行避障动作;最后使用基于Pixhawk搭建的飞行实验平台进行实验。实验结果表明,该方法可用于无人机低速飞行条件下避障。该方法所用到的传感器只有一块单目摄像头,而且相对于传统的主动式传感器避障方法,所占用无人机的体积大幅减小。该方法鲁棒性较好,能够准确识别不同姿态的人,实现对人避障。

Monocular vision obstacle avoidance method for quadcopter based on deep learning

A monocular vision obstacle avoidance method for quadrotor based on deep learning was proposed to help quadrotors to avoid obstacles. Firstly, the position of object in the image was obtained by object detection, and by calculating the height of the object box in the image, the distance between quadcopter and obstacle was estimated. Then, whether performing obstacle avoidance was determined by synergetic computer. Finally, experiments were conducted on a flight test platform based on Pixhawk flight control board. The results show that the proposed method can be applied to quadcoptor obstacle avoidance with low speed. Compared with traditional active sensor methods, the proposed method greatly reduces the occupied volume with only one monocular camera as sensor. This method is robust and can identify people with different postures as obstacles.