Infrastructure-Based Vehicle Localization System for Indoor Parking Lots Using RGB-D Cameras

• Published in: Shanghai jiao tong da xue xue bao Vol. 28; no. 1; pp. 61 - 69
• Main Authors: Cao, Bingquan, He, Yuesheng, Zhuang, Hanyang, Yang, Ming
• Format: Journal Article
• Language: English
• Published: Shanghai Shanghai Jiaotong University Press 01.02.2023; Springer Nature B.V; Key Laboratory of System Control and Information Processing of Ministry of Education,Shanghai 200240,China; Shanghai Engineering Research Center of Intelligent Control and Management,Shanghai 200240,China%University of Michigan-Shanghai Jiao Tong University Joint Institute,Shanghai Jiao Tong University,Shanghai 200240,China; Department of Automation,Shanghai Jiao Tong University,Shanghai 200240,China
• Subjects: Architecture; Automobile parking; Cameras; Computer applications; Computer Science; Electrical Engineering; Engineering; Hardware; Indoor environments; Infrastructure; Life Sciences; Localization; Localization method; Materials Science; Original Paper; Parking facilities; Synchronism; Synchronization; TP 242; A; point cloud registration; 场端RGB-D相机阵列; 车辆定位; 点云配准; TP 391; vehicle localization; infrastructure-based RGB-D camera; infrastructure-based RGB-D camera vehicle localization
• ISSN: 1007-1172; 1995-8188
• DOI: 10.1007/s12204-023-2569-z

Accurate vehicle localization is a key technology for autonomous driving tasks in indoor parking lots, such as automated valet parking. Additionally, infrastructure-based cooperative driving systems have become a means to realizing intelligent driving. In this paper, we propose a novel and practical vehicle localization system using infrastructure-based RGB-D cameras for indoor parking lots. In the proposed system, we design a depth data preprocessing method with both simplicity and efficiency to reduce the computational burden resulting from a large amount of data. Meanwhile, the hardware synchronization for all cameras in the sensor network is not implemented owing to the disadvantage that it is extremely cumbersome and would significantly reduce the scalability of our system in mass deployments. Hence, to address the problem of data distortion accompanying vehicle motion, we propose a vehicle localization method by performing template point cloud registration in distributed depth data. Finally, a complete hardware system was built to verify the feasibility of our solution in a real-world environment. Experiments in an indoor parking lot demonstrated the effectiveness and accuracy of the proposed vehicle localization system, with a maximum root mean squared error of 5 cm at 15 Hz compared with the ground truth.