DocumentCode :
138197
Title :
Topometric localization on a road network
Author :
Danfei Xu ; Badino, Hernan ; Huber, Daniel
Author_Institution :
Robot. Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA
fYear :
2014
fDate :
14-18 Sept. 2014
Firstpage :
3448
Lastpage :
3455
Abstract :
Current GPS-based devices have difficulty localizing in cases where the GPS signal is unavailable or insufficiently accurate. This paper presents an algorithm for localizing a vehicle on an arbitrary road network using vision, road curvature estimates, or a combination of both. The method uses an extension of topometric localization, which is a hybrid between topological and metric localization. The extension enables localization on a network of roads rather than just a single, non-branching route. The algorithm, which does not rely on GPS, is able to localize reliably in situations where GPS-based devices fail, including “urban canyons” in downtown areas and along ambiguous routes with parallel roads. We demonstrate the algorithm experimentally on several road networks in urban, suburban, and highway scenarios. We also evaluate the road curvature descriptor and show that it is effective when imagery is sparsely available.
Keywords :
computer vision; road traffic control; GPS-based device; highway scenario; road curvature descriptor; road curvature estimates; road network; suburban scenario; topometric localization; vision; Databases; Global Positioning System; Measurement; Probability density function; Roads; Vehicles; Visualization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International Conference on
Conference_Location :
Chicago, IL
Type :
conf
DOI :
10.1109/IROS.2014.6943043
Filename :
6943043
Link To Document :
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