Abstract: In view of the risk or unsuitable occasions for human activity, an intelligent car path planning system is constructed based on computer vision, which realizes the function of image processing, monocular vision measurement, global path planning and obstacle avoidance. This paper proposes the monocular vision measurement algorithm based on the color and texture of the ground. It improves the ant colony optimization combined with the car model performance and actual running environment. The performance of the optimization algorithm is better than other algorithms in run time.

Key words: computer vision, path planning, monocular vision measurement, ant colony algorithm

摘要： 针对危险或者不适合人类活动的场合，构建了基于计算机视觉的智能小车路径规划系统，系统主要实现了图像处理、单目视觉测距、虚拟地图生成、全局路径规划和小车避障等功能。率先提出基于地面颜色和纹理的单目视觉测距算法，并结合小车性能和实际运行环境对蚁群算法进行优化，经过大量实验证实，此设计较其他算法既能满足设计需求，又能大幅提高运行时间。

关键词: 计算机视觉, 路径规划, 单目视觉测距, 蚁群算法