Abstract: 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.

Key words: infrastructure-based RGB-D camera, vehicle localization, point cloud registration

摘要： 准确的车辆定位是室内停车场自动驾驶任务的关键技术，例如自主代客泊车。与此同时，注重场端的协作式驾驶系统已成为实现智能驾驶的一条重要途径。本文提出了一种新颖且实用的车辆定位系统，该系统将场端RGB-D相机阵列用于室内停车场。在所提出的系统中，本文设计了一种兼具简便性与高效性的深度数据预处理方法，以减轻庞大数据量所带来的计算负担。同时，本系统未实现传感器网络中所有相机的硬件同步功能，这主要是考虑到其异常繁琐且会显著降低本系统在大规模部署中的可扩展性。因此，为了解决伴随车辆运动所带来的数据畸变问题，本文提出了一种通过在分布式深度数据之中进行模板点云配准的车辆定位方法。最后，本文在真实环境中搭建了一套完整的硬件系统，验证了本文方案的可行性。具体实验表明，与真值数据相比，本文方法可实现输出频率为15 Hz且最大均方根误差为5 cm的车辆定位效果，证明了本文所提出的车辆定位系统的有效性和准确性。

关键词: 场端RGB-D相机阵列，车辆定位，点云配准