基于自适应导航参数的智能车辆视觉导航

Navigation of Vision-guided Intelligent Vehicle Based on Adaptive Navigation Parameters

为提高导航路径识别的鲁棒性和实时性,采用了分区阈值二值化、噪声点搜索及滤波等图像处理方法,并对导航路径进行分区逐段识别;在路径跟踪方面,在获取的导航路径图像中选取远端路径和近端路径,以远端路径和近端路径的方位偏差量作为确定目标路径的依据,使提取的导航参数能适应导航路径的变化。根据四轮智能车辆模型进行路径跟踪仿真计算。在此基础上,采用两块数字信号处理器,对基于路径导航的视觉智能车辆进行了设计和试验验证。试验结果表明采用该方法设计的智能车辆具有较好的路径识别和跟踪控制效果。

In order to obtain better robustness and real-time processing of path recognition, the binary segmentation image, noise searching and filtering procedures were employed. The whole path was classified into far part and near part, and the difference between the two parts was used as a threshold for selecting target road. So the navigating parameters were adapted to the changing path. Based on a model of four-wheel intelligent vehicle, a path tracking simulation was performed. Then, the navigation system based on path tracking was designed by using two digital signal processors. The experiments showed the accuracy and robustness of the system.