Detection and Vectorization of Roads from Lidar Data

This paper presents a method for the automatic detection and vectorization of roads from lidar data. To extract roads from a lidar point cloud, a hierarchical classification technique is used to classify the lidar points progressively into road and non-road points. During the classification process, both intensity and height values are initially used. Due to the homogenous and consistent nature of roads, a local point density is introduced to finalize the classification. The resultant binary classification is then vectorized by convolving a complex-valued disk termed the Phase Coded Disk (PCD) with the image to provide 3 separate pieces of information about the road. The centerline and width of the road are obtained from the resultant magnitude image while the direction is determined from the corresponding phase image, thus completing the vectorized road model. All algorithms used are described and applied to 2 urban test sites. Completeness values of 0.88 and 0.79 and correctness values of 0.67 and 0.80 were achieved for the classification phase of the process. The vectorization of the classified results yielded RMS values of 1.56 m and 1.66 m, completeness values of 0.84 and 0.81, and correctness values of 0.75 and 0.80 for 2 different data sets.

  • Availability:
  • Authors:
    • Clode, Simon
    • Rottensteiner, Franz
    • Kootsookos, Peter
    • Zelniker, Emanuel
  • Publication Date: 2007-5


  • English

Media Info

  • Media Type: Print
  • Features: Figures; Photos; References; Tables;
  • Pagination: pp 517-535
  • Serial:

Subject/Index Terms

Filing Info

  • Accession Number: 01049815
  • Record Type: Publication
  • Files: TRIS
  • Created Date: May 11 2007 8:29PM