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

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 information can subsequently be extracted. Categories and Subject Descriptors I.4 [Artificial Intelligence]: Image Processing and Computer Vision General Terms algorithm Keywords LIDAR, geo-reference, retro-reflective, lane marking, sign, road, ground-level

• Type: Conference
• Year: 2009
• Where: GIS
• Authors: Xin Chen, Brad Kohlmeyer, Matei Stroila, Narayanan Alwar, Ruisheng Wang, Jeff Bach
• Keywords: Automated Reasoning, GIS 2009, Lane Marking, LIDAR Light Return, Point Clouds