基于机器视觉的玉米根茎导航基准线提取方法

摘要

提出一种在大田环境下快速、精确提取中晚期玉米行中心线作为农业机器人导航基准线的新方法.改进了传统的2G-R-B算法,实时地获取植株绿色特征.根据玉米垂直投影图生成根茎轮廓特点并采用峰值点检测算法生成玉米根茎候补定位点,再对候补定位点进行二次判别,提取玉米根茎定位点.利用最小二乘法对已知特征点进行拟合,得到作物行线.求取左右行斜率后,计算出实际需要的导航基准线.实验结果表明,与其它算法相比,处理一幅700像素×350像素的彩色图像平均耗时小于185 ms,实时性好.在多种环境下生成的导航基准线准确率在90％以上,有较强的鲁棒性,为农业自动导引车(Automated guided vehicle,AGV)在中后期玉米大田中的自主行走提供了一种可靠的导航方法.%In order to achieve small agricultural automated guided vehicle (AGV) which could navigate autonomously between corn rows,a method was proposed which could quickly and accurately extract the centerlines of middle-late corn rows as the innovative methods of navigation baseline of agricultural robot in field environment.The algorithm was improved by the traditional 2G-R-B algorithm so that it could obtain the characteristics of green plants in real-time and also improve the robustness of image preprocessing.According to the vertical projection of corn crop's line,points of profile features of corn roots were generated.Using the detection algorithm of peak points,the backup location points of corn roots were obtained and then the location points of corn roots were got after the second judgment and detection.The least square method was used for fitting the location points of corn roots and two lines of crop rows were generated.The actual navigation baseline was calculated based on the formula angle bisector after the line slopes of two crop rows were generated respectively.In addition,the camera calibration process was simplified so that the image pixel coordinates could be converted into world coordinates quickly.The extracted angle and lateral deviation of navigation baselines were used as input parameters of navigation to control agriculture AGV.The experimental results showed that the method had strong robustness which could adapt to different environments and the accuracy of navigation baselines by detecting was more than 90％.The average processing time of a 700 pixels × 350 pixels color image was less than 185 ms which had a better real-time.The results provided a reliable reference method for autonomous navigation of the agricultural AGV in middle-late corn field.