论文标题
使用3D激光雷达数据的越野驱动区域提取
Off-Road Drivable Area Extraction Using 3D LiDAR Data
论文作者
论文摘要
我们提出了一种使用3D激光雷达数据的方法，以自主驾驶应用程序的目的。一个特定的深度学习框架旨在处理模棱两可的领域，这是越野环境中的主要挑战之一。为了减少对网络培训的人类注销数据的大量需求，我们利用了大量车辆路径和自动生成的障碍标签的信息。使用这些自动化注释，可以使用弱监督或半监督的方法对所提出的网络进行训练，这些方法可以通过更少的人类注释来实现更好的性能。我们数据集上的实验说明了我们框架的合理性以及我们弱和半监督方法的有效性。
We propose a method for off-road drivable area extraction using 3D LiDAR data with the goal of autonomous driving application. A specific deep learning framework is designed to deal with the ambiguous area, which is one of the main challenges in the off-road environment. To reduce the considerable demand for human-annotated data for network training, we utilize the information from vast quantities of vehicle paths and auto-generated obstacle labels. Using these autogenerated annotations, the proposed network can be trained using weakly supervised or semi-supervised methods, which can achieve better performance with fewer human annotations. The experiments on our dataset illustrate the reasonability of our framework and the validity of our weakly and semi-supervised methods.
