Vision-based positioning for internet-of-vehicles
Journal
IEEE Transactions on Intelligent Transportation Systems
Journal Volume
18
Journal Issue
2
Pages
364-376
Date Issued
2017
Author(s)
Abstract
This paper presents an algorithm for ego-positioning by using a low-cost monocular camera for systems based on the Internet-of-Vehicles. To reduce the computational and memory requirements, as well as the communication load, we tackle the model compression task as a weighted k-cover problem for better preserving the critical structures. For real-world vision-based positioning applications, we consider the issue of large scene changes and introduce a model update algorithm to address this problem. A large positioning data set containing data collected for more than a month, 106 sessions, and 14275 images is constructed. Extensive experimental results show that submeter accuracy can be achieved by the proposed ego-positioning algorithm, which outperforms existing vision-based approaches.
SDGs
Type
journal article