论文标题
自主驾驶的激光雷达：汽车激增和感知系统的原理，挑战和趋势
Lidar for Autonomous Driving: The principles, challenges, and trends for automotive lidar and perception systems
论文作者
论文摘要
自动驾驶汽车依靠其感知系统来获取有关其周围环境的信息。有必要发现其他车辆，行人和其他相关实体的存在。安全问题以及对准确估计的需求导致了与基于摄像机或基于雷达的感知系统相辅相成的光检测和范围（LIDAR）系统。本文介绍了最先进的汽车雷达技术以及这些技术使用的感知算法。首先通过分析从激光发射器到其光束扫描机构的主要组件来引入激光系统。引入并比较了各种解决方案的优势/缺点和当前状态。然后，从自动驾驶汽车的角度来看，对LiDAR数据处理的特定感知管道已详细介绍。审查了模型驱动的方法和新兴的深度学习解决方案。最后，我们概述了汽车激光雷达和感知系统的局限性，挑战和趋势。
Autonomous vehicles rely on their perception systems to acquire information about their immediate surroundings. It is necessary to detect the presence of other vehicles, pedestrians and other relevant entities. Safety concerns and the need for accurate estimations have led to the introduction of Light Detection and Ranging (LiDAR) systems in complement to the camera or radar-based perception systems. This article presents a review of state-of-the-art automotive LiDAR technologies and the perception algorithms used with those technologies. LiDAR systems are introduced first by analyzing the main components, from laser transmitter to its beam scanning mechanism. Advantages/disadvantages and the current status of various solutions are introduced and compared. Then, the specific perception pipeline for LiDAR data processing, from an autonomous vehicle perspective is detailed. The model-driven approaches and the emerging deep learning solutions are reviewed. Finally, we provide an overview of the limitations, challenges and trends for automotive LiDARs and perception systems.
