基于编码标志的无人机电力巡检自主定位方法

doi:10.3969/j.issn.0255-8297.2018.05.012

应用科学学报 ›› 2018, Vol. 36 ›› Issue (5): 845-858.doi: 10.3969/j.issn.0255-8297.2018.05.012

基于编码标志的无人机电力巡检自主定位方法

许忠雄¹, 邵瑰玮², 谢予星¹, 吴亮¹, 季铮¹

1. 武汉大学遥感信息工程学院, 武汉 430079;
2. 中国电力科学院, 武汉 430079

收稿日期:2017-06-20 修回日期:2017-07-21 出版日期:2018-09-30 发布日期:2018-09-30
通信作者: 季铮,副教授,研究方向:摄影测量与遥感、计算机视觉,E-mail:jz07@whu.edu.cn E-mail:jz07@whu.edu.cn
基金资助:
国家重点研发计划项目（No.2016YFB0501403）资助

Method of UAV Autonomous Positioning Based on Encoded Sign during Power Inspection

XU Zhong-xiong¹, SHAO Gui-wei², XIE Yu-xing¹, WU Liang¹, JI Zheng¹

1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China;
2. China Electric Power Research Institute, Wuhan 430079, China

Received:2017-06-20 Revised:2017-07-21 Online:2018-09-30 Published:2018-09-30

摘要/Abstract

摘要： 为了实现无人机在变电站巡检时自主避障，提出一种利用编码标志作为控制点的无人机自主定位方法.首先讨论编码标志的设计与解码，然后利用灰度方向直方图（histogram of oriented gradient，HOG）特征和支持向量机（support vector machine，SVM）检测并跟踪图像中的编码标志，以提高算法运行效率，最后根据编码标志在电塔上的位置及其在图像上的对应坐标，由物点与像点的透视关系解算无人机位置.实验表明：HOG+SVM组合检测图像中的编码标志召回率为99%；解码算法在编码标志模糊、变形等极端条件下依然保持强鲁棒性，解码错误率仅为0.05%；无人机定位误差不超过±0.03 m，算法运行速度达10帧/s，可用于变电站的实际巡检.

关键词: 变电站巡检, 无人机, 支持向量机, 灰度方向直方图, 编码标志

Abstract: In order to achieve the automatic obstacle avoidance of unmanned aerial vehicle (UAV) during substation inspection, this paper proposes a UAV autonomous positioning method by using encoded sign as control point. First, the design and decoding of encoded sign are discussed. Second, the encoded signs in the image are detected by using the SVM+HOG algorithm, and the tracking algorithm is used to track the encoded sign efficiency. At last, according to the location of encoded signs on the pylon and its corresponding coordinates on the image, the location of UAV can be calculated by using the projective relation between the object points and their corresponding image points. Experiments show that the set of SVM+HOG can detect the encode signs in the image at a recall rate of 99%, and the decoding algorithm remains strong robustness in signs blur, deformation and other extreme conditions, with decoding error rate of only 0.05%. The error of UAV positioning is not more than ±0.03 m, and the algorithm runs at a speed of 10 frames per second, fast enough for practical substation inspections.

Key words: support vector machine (SVM), encode sign, unmanned aerial vehicle (UAV), histogram of oriented gradient (HOG), substation inspection

中图分类号:

V249.3

许忠雄, 邵瑰玮, 谢予星, 吴亮, 季铮. 基于编码标志的无人机电力巡检自主定位方法[J]. 应用科学学报, 2018, 36(5): 845-858.

XU Zhong-xiong, SHAO Gui-wei, XIE Yu-xing, WU Liang, JI Zheng. Method of UAV Autonomous Positioning Based on Encoded Sign during Power Inspection[J]. Journal of Applied Sciences, 2018, 36(5): 845-858.

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基于编码标志的无人机电力巡检自主定位方法

Method of UAV Autonomous Positioning Based on Encoded Sign during Power Inspection

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