A Multi-Lidar-based Point Cloud Acquisition Platform and Data Fusion for Autonomous Vehicle in Complex Urban Environment

Autonomous driving has become the focus of research and development in recent years. However, the capability of precise positioning and obstacle avoidance for autonomous driving often rely on accurate and dense point cloud images. In particular, the environment in the metropolitan area is relatively complex and there are many viaducts and expressways in modern cities. The spatial features obtained from flat roads are no longer sufficient. If we can obtain the features of higher buildings or higher landforms, a better positioning information and security can be provided when autonomous vehicles are driving on expressways or elevated roads. In addition, the high density of vehicles in the metropolitan area and vision blind spots of vehicles also form a great safety concern for autonomous driving. In order to provide higher density point cloud information so that autonomous driving can ensure the accuracy and safety of navigation and positioning when operating in metropolitan areas, we propose in this paper a data acquisition platform base on multi-LiDAR (VLP-16) as well as a data calibration and fusion algorithm to solve the problems of vision blind spots, low vertical resolution, and sparse high-level point clouds in most of the LiDAR-based system. The proposed system adopts the industrial computer (IPC) of x86 architecture and Robotic Operating System (ROS) for overall operation. In addition, the homogeneous transformation is applied for the calibration and fusion of multi-Lidar point cloud coordinate system. Experimental results have proved that the proposed system can obtain reliable point cloud data, effectively improve the vertical resolution of the point cloud, and increase the point cloud density by more than three times, which can effectively improve the positioning reliability of autonomous driving in metropolitan areas.