Next generation map making: geo-referenced ground-level LIDAR point clouds for automatic retro-reflective road feature extraction

Authors:
Xin Chen;Brad Kohlmeyer;Matei Stroila;Narayana Alwar;Ruisheng Wang;Jeff Bach
Affiliations:
NAVTEQ Corporation, Chicago, IL;NAVTEQ Corporation, Chicago, IL;NAVTEQ Corporation, Chicago, IL;NAVTEQ Corporation, Chicago, IL;NAVTEQ Corporation, Chicago, IL;NAVTEQ Corporation, Chicago, IL
Venue:
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Year:
2009

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Abstract

This paper presents a novel method to process large scale, ground level Light Detection and Ranging (LIDAR) data to automatically detect geo-referenced navigation attributes (traffic signs and lane markings) corresponding to a collection travel path. A mobile data collection device is introduced. Both the intensity of the LIDAR light return and 3-D information of the point clouds are used to find retroreflective, painted objects. Panoramic and high definition images are registered with 3-D point clouds so that the content of the sign and color can subsequently be extracted.