Abstract
Survey-grade Lidar brands have commercialized Lidar-based mobile mapping systems (MMSs) for several years now. With this high-end equipment, the high-level accuracy quality of point clouds can be ensured, but unfortunately, their high cost has prevented practical implementation in autonomous driving from being affordable. As an attempt to solve this problem, we present a cost-effective MMS to generate an accurate 3D color point cloud for autonomous vehicles. Among the major processes for color point cloud reconstruction, we first synchronize the timestamps of each sensor. The calibration process between camera and Lidar is developed to obtain the translation and rotation matrices, based on which color attributes can be composed into the corresponding Lidar points. We also employ control points to adjust the point cloud for fine tuning the absolute position. To overcome the limitation of Global Navigation Satellite System/Inertial Measurement Unit (GNSS/IMU) positioning system, we utilize Normal Distribution Transform (NDT) localization to refine the trajectory to solve the multi-scan dispersion issue. Experimental results show that the color point cloud reconstructed by the proposed MMS has a position error in centimeter-level accuracy, meeting the requirement of high definition (HD) maps for autonomous driving usage.
Highlights
High definition maps (HD maps) play a crucial role in the development of autonomous driving systems
We have presented a cost effective mobile mapping system to generate a color pointIncloud, in which
PX2presented is employed as the computing platform forsystem saving to physical space and this paper, wean have a cost effective mobile mapping generate a color
Summary
High definition maps (HD maps) play a crucial role in the development of autonomous driving systems. A HD map consists of a point cloud and road/lane vectors with semantic information. A Lidar sensor plays an important role to assist localization algorithms to enhance the positioning performance achievable with GNSS/IMU receivers. It is important to have an accurate point cloud working together with GNSS/IMU to ensure reliable self-localization ability for autonomous driving. In order to deliver end-to-end transportation services, essential road/lane vectors with semantic information in the HD map are indispensable. Road/lane boundaries are provided to help runtime calculation for obstacle avoidance in the sight and make detour or stop decisions afterward. This kind of process is called local planning. An accurate color point cloud helps an autonomous vehicle
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