Lane detection algorithms are crucial for the development of autonomous vehicles technologies. The more extended approach is to use cameras as sensors. However, LIDAR sensors can cope with weather and light conditions that cameras can not. In this paper, we introduce a method to extract road markings from the reflectivity data of a 64-layers LIDAR sensor. First, a plane segmentation method along with region grow clustering was used to extract the road plane. Then we applied an adaptive thresholding based on Otsu s method and finally, we fitted line models to filter out the remaining outliers. The algorithm was tested on a test track at 60km/h and a highway at 100km/h. Results showed the algorithm was reliable and precise. There was a clear improvement when using reflectivity data in comparison to the use of the raw intensity data both of them provided by the LIDAR sensor.
翻译:通道探测算法对于开发自主车辆技术至关重要。 更为广泛的方法是使用相机作为传感器。 然而, LIDAR 传感器可以应对摄像头无法应对的天气和光线条件。 在本文中,我们引入了一种方法,从64层LIDAR传感器的反射数据中提取道路标记。 首先,使用平面分割法以及区域集聚法来提取公路飞机。 然后,我们采用了基于Otsu 方法的适应性阈值,最后,我们安装了线模型来过滤其余的外层。 该算法在60公里/小时的测试轨线上和100公里/小时的高速公路上进行了测试。结果显示算法是可靠和准确的。 与使用LIDAR 传感器提供的原始强度数据相比,在使用反射数据方面有了明显的改进。