Please use this identifier to cite or link to this item: https://doi.org/10.1109/RAM.2013.6758570
Title: Metric mapping and topo-metric graph learning of urban road network
Authors: Qin, B.
Chong, Z.J.
Bandyopadhyay, T.
Ang Jr., M.H. 
Issue Date: 2013
Citation: Qin, B.,Chong, Z.J.,Bandyopadhyay, T.,Ang Jr., M.H. (2013). Metric mapping and topo-metric graph learning of urban road network. IEEE Conference on Robotics, Automation and Mechatronics, RAM - Proceedings : 119-123. ScholarBank@NUS Repository. https://doi.org/10.1109/RAM.2013.6758570
Abstract: A road map serves as a model of the road network, which is especially desired for a vehicle performing autonomous navigation in urban road environment. This paper first introduces a metric mapping algorithm for urban roads, which generates an occupancy grid map of road surfaces and boundaries. Based on the metric map, we further propose an approach to extract a topo-metric graph which captures both topological and metric information of the road network. As a detailed model of the urban roads, the metric map can be used for obstacle avoidance and local path planning, while the topo-metric graph as a compact representation that can be used for some high-level reasoning processes. Our proposed algorithms are tested in real experiments, and have shown good results. © 2013 IEEE.
Source Title: IEEE Conference on Robotics, Automation and Mechatronics, RAM - Proceedings
URI: http://scholarbank.nus.edu.sg/handle/10635/86020
ISBN: 9781479911998
ISSN: 2158219X
DOI: 10.1109/RAM.2013.6758570
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

4
checked on Oct 14, 2018

Page view(s)

52
checked on Oct 5, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.