论文标题
3D点云处理和自主驾驶的学习
3D Point Cloud Processing and Learning for Autonomous Driving
论文作者
论文摘要
我们对3D点云处理和自主驾驶的学习进行了评论。作为自动驾驶汽车中最重要的传感器之一，光检测和范围（LIDAR）传感器收集了3D点云，这些云精确地记录了物体和场景的外表面。 3D点云处理和学习的工具对于自动驾驶汽车中的地图创建，本地化和感知模块至关重要。尽管已经对从相机收集的数据（例如图像和视频）引起了很多关注，但越来越多的研究人员认识到激光雷达在自主驾驶中的重要性和意义，并提出了拟议的处理和学习算法来利用3D点云。我们回顾了该研究领域的最新进展，并总结了已尝试的方法以及实用和安全的自动驾驶汽车所需的内容。我们还提供有关未来需要解决的开放问题的观点。
We present a review of 3D point cloud processing and learning for autonomous driving. As one of the most important sensors in autonomous vehicles, light detection and ranging (LiDAR) sensors collect 3D point clouds that precisely record the external surfaces of objects and scenes. The tools for 3D point cloud processing and learning are critical to the map creation, localization, and perception modules in an autonomous vehicle. While much attention has been paid to data collected from cameras, such as images and videos, an increasing number of researchers have recognized the importance and significance of LiDAR in autonomous driving and have proposed processing and learning algorithms to exploit 3D point clouds. We review the recent progress in this research area and summarize what has been tried and what is needed for practical and safe autonomous vehicles. We also offer perspectives on open issues that are needed to be solved in the future.
