一种改进的AGV障碍物检测方法

An Improved Obstacle Detection Method for AGV

针对智能工厂中AGV障碍物检测算法在不均匀光照、背景纹理等干扰下表现效果不佳的问题,文中提出了一种改进的Canny算子的障碍物检测方法。该方法从颜色空间、滤波方式、梯度方向以及自适应阈值角度实现了AGV对障碍物检测的优化。通过Lab颜色空间转换,提取其b分量后进行滤波处理。将改进的中值滤波与双边滤波融合,代替传统的Canny算子中的高斯滤波,在实现降噪的同时减少边缘细节的丢失,并且提高了算法的速度。通过增加梯度方向来增强边缘信息,使用最大类间方差法获取自适应阈值。实验结果表明,文中所提出的方法在降低噪声干扰的同时能够提高边缘检测的精确性,实现了对障碍物的稳定检测。

To solve the problem that AGV obstacle detection algorithm performs poorly under the interference of uneven illumination and background texture in smart factories,this study proposes an improved Canny operator for obstacle detection.The method achieves the optimization of AGV obstacle detection in terms of color space,filtering method,gradient direction and adaptive threshold.Through Lab color space conversion,the b component is extracted and then filtered.The improved median filter and bilateral filter are merged to replace the Gaussian filter in the traditional Canny operator,which reduces the loss of edge details while achieving noise reduction,and improves the speed of the algorithm.The edge information is enhanced by increasing the gradient direction,and adaptive thresholding is obtained using Otsu algorithm.Experiments show that the proposed method can improve the accuracy of edge detection and reduce noise interference,thus achieving stable detection of obstacles.