计算机工程 ›› 2020, Vol. 46 ›› Issue (9): 313-320.doi: 10.19678/j.issn.1000-3428.0055581

• 开发研究与工程应用 • 上一篇    

基于动态双窗口的机器人视觉导航与特征识别方法

崔坤坤1,2, 樊绍胜1,2   

  1. 1. 长沙理工大学 电气与信息工程学院, 长沙 410114;
    2. 电力机器人湖南省重点实验室, 长沙 410114
  • 收稿日期:2019-07-25 修回日期:2019-10-09 发布日期:2019-10-18
  • 作者简介:崔坤坤(1995-),男,硕士研究生,主研方向为智能机器人、机器视觉;樊绍胜,教授、博士、博士生导师。
  • 基金项目:
    国家自然科学基金(61473049);湖南省研究生科研创新项目(CX20190682)。

Visual Navigation and Feature Recognition Method of Robot Based on Dynamic Double Windows

CUI Kunkun1,2, FAN Shaosheng1,2   

  1. 1. School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, China;
    2. Hunan Key Laboratory of Power Robots, Changsha 410114, China
  • Received:2019-07-25 Revised:2019-10-09 Published:2019-10-18

摘要: 针对变电站巡检机器人导航精度低与巡检点识别鲁棒性差的问题,提出一种采用动态双窗口的视觉导航与路径特征识别方法。根据导航图像匹配结果和相机位姿偏差动态设置导航窗口,将图像由传统的红绿蓝颜色空间转换为色调、饱和度和亮度颜色空间进行灰度图重构,利用分区自适应阈值分割算法提取导航路径并将其简化为直线模型,使用最小二乘法拟合计算出机器人与导航路径的距离偏差,同时将全视野范围作为特征识别窗口,根据路径长宽比改进基于区域建议的Faster R-CNN算法,最终完成对5种路径特征的识别。实验结果表明,在强光照和弱光照条件下,该方法所得巡检机器人的直线跟踪与曲线跟踪偏差分别小于5 mm和25 mm,对5类路径特征的平均识别准确率达到98.6%,与传统HOG+SVM目标检测方法相比,有效提高了导航精度和路径特征识别鲁棒性。

关键词: 巡检机器人, 视觉导航, 动态双窗口, 灰度图重构, Faster R-CNN算法

Abstract: To address the problems of low navigation accuracy and poor robustness of inspection point recognition of substation inspection robots,this paper proposes a visual navigation and path feature recognition method based on dynamic double windows.According to the navigation image matching results and camera pose deviation,the navigation window is dynamically set,and the color space of the image is transformed from the traditional Red,Green and Blue(RGB) color space into the Hue,Saturation and Value(HSV) color space for gray image reconstruction.The navigation path is extracted by using the partition adaptive threshold segmentation algorithm and simplified into a linear model.The distance deviation between the robot and the navigation path is calculated by the least square method.At the same time,the full field of view is used as the feature recognition window,and the Faster R-CNN algorithm based on region recommendation is improved according to the length-width ratio of the path.Finally,the features of five kinds of path are recognized.Experimental results show that under strong light and weak light conditions,the deviation of linear tracking and curve tracking of inspection robots obtained by the proposed method is less than 5 mm and 25 mm respectively,and the average recognition accuracy of features of five kinds of path reaches 98.6%.Compared with the traditional HOG+SVM target detection method,the proposed method effectively improves the navigation accuracy and robustness of path feature recognition.

Key words: inspection robot, visual navigation, dynamic double windows, gray image reconstruction, Faster R-CNN algorithm

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