Title :
Vehicle localization using road markings
Author :
Tao Wu ; Ranganathan, A.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Maryland, College Park, MD, USA
Abstract :
Reliable lane-level localization is a requirement for many driver-assistance methods as well as for autonomous driving. Localization using cameras is desirable due to ubiquity and cheapness of sensors but is hard to achieve reliably. We propose a method towards reliable visual localization using traffic signs painted on the road such as arrows, pedestrian crossings, and speed limits. These road markings are relatively easily detected since they are designed to be highly conspicuous. Our method automatically recognizes road markings and uses features detected within them to compute the location of the vehicle. This provides an absolute global localization if the road markings have been surveyed before hand, and relative positioning information otherwise. We demonstrate using experiments and with groundtruth data that our method provides accurate lane-level visual localization under various lighting conditions and using various types of road markings.
Keywords :
feature extraction; lighting; object detection; object recognition; pose estimation; traffic engineering computing; feature detection; global localization; lane-level visual localization; lighting conditions; relative positioning information; reliable visual localization; road markings automatic recognition; traffic signs; vehicle localization; Boosting; Cameras; Estimation; Global Positioning System; Lighting; Roads; Vehicles;
Conference_Titel :
Intelligent Vehicles Symposium (IV), 2013 IEEE
Conference_Location :
Gold Coast, QLD
Print_ISBN :
978-1-4673-2754-1
DOI :
10.1109/IVS.2013.6629627