基于信息融合的农业自主车辆障碍物检测方法

针对单一传感器在智能车辆环境感知中的局限性，提出一种基于摄像机与激光雷达信息融合的农业自主车辆前方障碍物检测方法。对单目摄像机获取的图像进行基于Ft（Frequency-tuned）算法的显著性检测，并生成显著图。同时对激光雷达反射点进行基于数据关联性评估的聚类分析，确定障碍物数量、边界与位置等先验信息。然后以激光雷达坐标相对应的图像像素坐标为种子点，由种子点激活经过处理的显著图，基于受限区域生长实现障碍物区域分割。试验结果表明，基于Ft算法的图像显著性检测具有更好的边缘检测效果，基于种子点的受限区域生长法可以有效地进行障碍物分割。在机器视觉的基础上融入激光雷达数据，可以更好地排除非障碍物的干扰，实现了障碍物的完整检出。

Detection of Obstacles Based on Information Fusion for Autonomous Agricultural Vehicles

Aiming at the limitations of single sensor in environment perception for intelligent vehicles, a method of detecting obstacles based on information fusion from camera and laser radar was proposed for autonomous agricultural vehicles. For the images captured from monocular camera, significance detection was carried out by using Ft algorithm and the significance images were generated. Meanwhile, cluster analysis based on data correlation assessment was conducted for reflection data points from laser radar to determine the priori information such as the number, boundary and location of obstacles. Then the pixel points corresponding to the laser radar data points were regarded as the seed points, and the significance images generated were activated by the seed points. Lastly, the region segmentation based on the region growth method was implemented to obstacles. The experimental results showed that the image significance detection based on Ft algorithm had a better edge detection effect, and the region growth method based on the seed points can effectively segment the obstacles. The information fusion of machine vision and laser radar can better eliminate the interference of non-obstacles and achieve the complete detection of obstacles.