Updating Point Cloud Layer of High Definition (HD) Map Based on Crowd-Sourcing of Multiple Vehicles Installed LiDAR

Abstract

A high-definition (HD) map is becoming an integral component of future mobility systems such as autonomous and connected vehicles. Advances in computing systems, LiDAR technologies, and vehicle communication technologies have enabled the HD map to directly treat a point cloud map (PCM), modeling road environments as LiDAR signal-level data. However, if actual road environments are changed, the PCM, modeling the environments before changes, can not be used for vehicle applications. Accordingly, the PCM has to stay up-to-date states by reflecting the environment changes continuously. This paper presents a crowd-sourcing framework to update the PCM from environment changes continuously using LiDAR and vehicle communication. Multiple intelligent vehicles installed with the LiDAR sensors download the PCM from a map server via wireless vehicle communication. To minimize the effects of environment changes, a robust localization based on a hierarchical Simultaneous Localization and Mapping (SLAM) estimates the pose (position and direction). The estimated pose is used to detect the differences between the PCM and environments, which are defined as map changes. The map changes are detected by the probabilistic and evidential theory considering the LiDAR characteristics, such as beam divergence and multi-echo. The detected map changes are uploaded to the map cloud server and merged into the PCM. The proposed crowd-sourcing framework to keep the PCM up-to-date is verified and evaluated via simulations and experiments in sites with road environment changes.

Keywords

• High-definition (HD) map
• point cloud
• crowd-sourcing
• light detection and ranging (LiDAR)
• localization