Abstract
Accurate and seamless vehicle positioning is fundamental for autonomous driving tasks in urban environments, requiring the provision of high-end measuring devices. Light Detection and Ranging (lidar) sensors, together with Global Navigation Satellite Systems (GNSS) receivers, are therefore commonly found onboard modern vehicles. In this paper, we propose an integration of lidar and GNSS code measurements at the observation level via a mixed measurement model. An Extended Kalman-Filter (EKF) is implemented to capture the dynamic of the vehicle movement, and thus, to incorporate the vehicle velocity parameters into the measurement model. The lidar positioning component is realized using point cloud registration through a deep neural network, which is aided by a high definition (HD) map comprising accurately georeferenced scans of the road environments. Experiments conducted in a densely built-up environment show that, by exploiting the abundant measurements of GNSS and high accuracy of lidar, the proposed vehicle positioning approach can maintain centimeter-to meter-level accuracy for the entirety of the driving duration in urban canyons.
Highlights
Accurate and seamless positioning in urban environments is fundamental for autonomous vehicle and driving systems, where minimum human intervention is required to perform driving tasks [1,2]
System (GNSS) receivers are capable of positioning the vehicle with respect to a global reference coordinate frame, they are vulnerable to signal blockages, the Earth’s atmosphere, and errors caused by multipath effects in urban canyons [3,4]
The present study aims to explore the performance of lidar, in standalone and in combination with Global Navigation Satellite Systems (GNSS), for vehicle positioning in deep urban environments
Summary
Accurate and seamless positioning in urban environments is fundamental for autonomous vehicle and driving systems, where minimum human intervention is required to perform driving tasks [1,2]. The provision of positioning solutions, with such stringent requirements, demands the utilization of multiple measuring devices. This is because each device possesses its own distinctive characteristics, imposing limitations which need to be overcome through the integration of the device with other devices offering ‘complementary’ characteristics. System (GNSS) receivers are capable of positioning the vehicle with respect to a global reference coordinate frame, they are vulnerable to signal blockages, the Earth’s atmosphere, and errors caused by multipath effects in urban canyons [3,4].
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