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