Curbs are one of the essential elements of urban and highway traffic environments. Robust curb detection provides road structure information for motion planning in an autonomous driving system. Commonly, video cameras and 3D LiDARs are mounted on autonomous vehicles for curb detection. However, camera-based methods suffer from challenging illumination conditions. During the long period of time before wide application of Deep Neural Network (DNN) with point clouds, LiDAR-based curb detection methods are based on hand-crafted features, which suffer from poor detection in some complex scenes. Recently, DNN-based dynamic object detection using LiDAR data has become prevalent, while few works pay attention to curb detection with a DNN approach due to lack of labeled data. A dataset with curb annotations or an efficient curb labeling approach, hence, is of high demand...
翻译:螺旋杆是城市和高速公路交通环境的基本要素之一。强力路路况探测为自主驾驶系统的运动规划提供了道路结构信息。通常,在自动车辆上安装了摄像头和3D激光雷达,以进行阻力探测。然而,以摄像头为基础的方法面临具有挑战性的照明条件。在广泛应用带有点云的深神经网络(DNN)之前的很长一段时间里,以利达雷达为主的阻力探测方法以手制特征为基础,在一些复杂场景中,这些特征得不到很好的探测。最近,利用激光雷达数据进行的DNNN动态物体探测已经变得普遍,而由于缺乏标签数据,很少有人注意用DNN方法阻止探测。因此,带有制动说明或有效路标方法的数据集的需求很大。