Traffic Sign Based Point Cloud Data Registration with Roadside LiDARs in Complex Traffic Environments

The intelligent road is an important component of the intelligent vehicle infrastructure cooperative system, the latest development of intelligent transportation systems. As an advanced sensor, Light Detection and Ranging (LiDAR) has gradually been used to collect high-resolution micro-traffic data...

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Bibliographic Details
Published in:Electronics (Basel) 2022-05, Vol.11 (10), p.1559
Main Authors: Zhang, Zheyuan, Zheng, Jianying, Tao, Yanyun, Xiao, Yang, Yu, Shumei, Asiri, Sultan, Li, Jiacheng, Li, Tieshan
Format: Article
Language:English
Subjects:
Algorithms
Autonomous vehicles
Cameras
Data collection
Global positioning systems
GPS
Intelligent transportation systems
Intelligent vehicles
Lidar
Light
Registration
Researchers
Reverse engineering
Rigid structures
Roads & highways
Roadsides
Sensors
Signs
Target recognition
Three dimensional models
Traffic congestion
Traffic control
Traffic information
Traffic signs
Two dimensional bodies
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Summary:The intelligent road is an important component of the intelligent vehicle infrastructure cooperative system, the latest development of intelligent transportation systems. As an advanced sensor, Light Detection and Ranging (LiDAR) has gradually been used to collect high-resolution micro-traffic data on the roadside of intelligent roads. Furthermore, a fusion of multiple LiDARs has become a current hot spot to extend the data collection range and improve detection accuracy. This paper focuses on point cloud registration in a complex traffic environment and proposes a three-dimensional (3D) registration method based on traffic signs and prior knowledge of traffic scenes. Traffic signs with their reflective films are used as reference targets to register 3D point cloud data from roadside LiDARs. The proposed method consists of a vertical registration and a horizontal registration. For the vertical registration, we propose a panel rotation algorithm to rotate the initial point cloud to register it vertically, converting the 3D point cloud registration into a two-dimensional (2D) rigid body transformation. For the vertical registration, our system registers traffic signs from different LiDARs. Our method has been verified in some actual scenarios. Compared with previous methods, the proposed method is automatic and does not need to search reference targets manually. Furthermore, it is suitable for actual engineering use and can be applied to sparse point cloud data from LiDAR with few beams, realizing point cloud registration of large disparity.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11101559