量子电子学报 ›› 2021, Vol. 38 ›› Issue (3): 307-315.doi: 10.3969/j.issn.1007-5461.2021.03.006

• 图像与信息处理 • 上一篇    下一篇

一种基于机器视觉的无人机同心圆靶精准降落方法

洪富祥1, 陈冲2, 丘仲锋2∗   

  1. 1 南京信息工程大学电子与信息工程学院, 江苏南京210044; 2 南京信息工程大学海洋科学学院, 江苏南京210044
  • 收稿日期:2020-04-26 修回日期:2020-07-06 出版日期:2021-05-28 发布日期:2021-05-28
  • 通讯作者: zhongfeng.qiu@nuist.edu.cn
  • 作者简介:洪富祥( 1994 - ), 湖北黄冈人, 研究生, 主要从事嵌入式系统设计方面的研究。E-mail: 20181218010@nuist.edu.cn
  • 基金资助:
    Supported by National Natural Science Foundation of China (国家自然科学基金, 41976165)

A precision landing method for unmanned aerial vehicle concentric round targets based on machine vision

HONG Fuxiang1, CHEN Chong2, QIU Zhongfeng2∗   

  1. 1 School of Electronic & Information Engineering, Nanjing University of Information & Technology, Nanjing 210044, China; 2 School of Marine Sciences, Nanjing University of Information & Technology, Nanjing 210044, China
  • Received:2020-04-26 Revised:2020-07-06 Published:2021-05-28 Online:2021-05-28

摘要: 随着图像处理技术的发展、嵌入式硬件的进步, 无人机(UAV) 的机器视觉成为一个十分热门的研究领 域。UAV 自动降落控制是UAV 飞行控制系统的关键技术之一, 对UAV 降落的稳定性、精确性、可靠性、实时 性具有重要的作用。针对车载UAV 自动返航降落误差大的缺点, 通过机载视觉处理单元对UAV 降落的平台进 行图像处理后, 将获取的UAV 和降落平台的位置信息通过坐标转换算法转换为真实坐标信息, 然后通过模拟遥 控器杆量的方式来控制UAV 实现精准降落。实验表明, 利用所提出方法能够精准识别UAV 降落平台, 与只有 GPS 定位的实验结果相比, 该方法能够有效地增加UAV 降落的精度, 具有一定的实际应用价值。

关键词: 图像处理, 机器视觉, 自动返航, 坐标转换算法, 车载无人机

Abstract: With the development of image processing technology and the progress of embedded hardware, machine vision of unmanned aerial vehicle (UAV) has become a very hot research field. Automatic landing control of UAV is one of the key technologies of UAV flight control system, which plays an important role in the stability, accuracy, reliability and real-time performance of UAV landing. Aiming at the shortcomings of vehicle-mounted UAV with large error in automatic return and landing, through the airborne visual processing unit, the landing platform of the UAV is processed by using image processing algorithm, and the acquired location information of the UAV and landing platform are converted into real coordinate information by using the coordinate conversion algorithm, and then the UAV is controlled to achieve precise landing by simulating the remote control lever. Experiment shows that the UAV landingplatform can be accurately identified by using the proposed method. Compared with the experimental results of only GPS positioning, this method can effectively increase the accuracy of UAV landing and has certain practical application value.

Key words: image processing, machine vision, automatic return, coordinate conversion algorithm; vehicle-mounted unmanned aerial vehicle

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