论文标题
使用反射率信息从激光雷达点云中分割的路标分割
Road Markings Segmentation from LIDAR Point Clouds using Reflectivity Information
论文作者
论文摘要
车道检测算法对于自动驾驶技术的发展至关重要。更扩展的方法是将相机用作传感器。但是，激光雷达传感器可以应对摄像机无法的天气和光线条件。在本文中，我们介绍了一种从64层激光雷达传感器的反射率数据中提取道路标记的方法。首先，使用平面分割方法以及区域生长聚类来提取道路平面。然后，我们基于OTSU方法应用了自适应阈值，最后，我们拟合了线模型以滤除其余的异常值。该算法在60km/h的测试轨道上测试，高速公路以100km/h的速度进行了测试。结果表明该算法是可靠且精确的。与使用原始强度数据相比，使用反射率数据时，这两者都是LIDAR传感器提供的。
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.
