Point Cloud Map Generation and Localization for Autonomous Vehicles Using 3D Lidar Scans
Point Cloud Map Generation and Localization for Autonomous Vehicles Using 3D Lidar Scans
复制标题
使用 3D 激光雷达扫描为自动驾驶车辆生成和定位点云地图
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Dong Seog Han
中科院分区：
文献类型：
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作者：
Alwin Poulose;Minjin Baek;Dong Seog Han
自动驾驶汽车是未来的智能汽车，有望减少人类驾驶员的数量，提高效率，避免碰撞，成为未来理想的城市交通工具。为了实现这一目标，汽车制造商已经开始在这一领域开展工作，利用潜力并解决当前的挑战，以达到预期的结果。从这个意义上说，第一个挑战是将传统汽车改造成满足用户期望的自动驾驶汽车。传统车辆向自动驾驶车辆的演变包括采用和改进不同的技术和计算机算法。除了感知、路径规划和控制之外，影响自动驾驶汽车性能的关键任务是其定位，定位的准确性和效率在自动驾驶中起着至关重要的作用。在本文中，我们描述了实现基于地图的定位使用点云匹配的自主车辆。机器人操作系统（ROS）沿着，与Autoware，这是一个开源的软件平台，自动驾驶汽车，用于实现本文提出的车辆定位系统。基于三维激光雷达点生成点云地图，并使用正态分布变换（NDT）匹配算法通过将实时激光雷达测量值与预先构建的点云地图进行匹配来定位测试车辆。实验结果表明，使用3D激光雷达扫描的基于地图的定位系统能够实现实时定位性能，对于校园环境中的自动驾驶来说足够准确和有效。本文包括用于点云地图生成和车辆定位的方法，以及一步一步的程序，实现与基于ROS的系统的目的，自动驾驶。
Autonomous vehicles are the future intelligent vehicles, which are expected to reduce the number of human drivers, improve efficiency, avoid collisions, and become the ideal city vehicles of the future. To achieve this goal, vehicle manufacturers have started to work in this field to harness the potential and solve current challenges to achieve the desired results. In this sense, the first challenge is transforming conventional vehicles into autonomous ones that meet users’ expectations. The evolution of conventional vehicles into autonomous vehicles includes the adoption and improvement of different technologies and computer algorithms. The essential task affecting the autonomous vehicle’s performance is its localization, apart from perception, path planning, and control, and the accuracy and efficiency of localization play a crucial role in autonomous driving. In this paper, we describe the implementation of map-based localization using point cloud matching for autonomous vehicles. The Robot Operating System (ROS) along with Autoware, which is an open-source software platform for autonomous vehicles, are utilized for the implementation of the vehicle localization system presented in this paper. Point cloud maps are generated based on 3D lidar points, and a normal distributions transform (NDT) matching algorithm is used for localizing the test vehicle through matching real-time lidar measurements with the pre-built point cloud maps. The experiment results show that the map-based localization system using 3D lidar scans enables real-time localization performance that is sufficiently accurate and efficient for autonomous driving in a campus environment. The paper comprises the methods used for point cloud map generation and vehicle localization as well as the step-by-step procedure for the implementation with a ROS-based system for the purpose of autonomous driving.