Application of Terrestrial LiDAR for Landslide Monitoring: Lessons Learned from Feature-Based Point Cloud Registration

The use of light detection and ranging (LiDAR) technology for slope monitoring and landslide/rockslide risk assessment on highways is proven to be one of the best practices. Registration is one of the crucial steps in data processing which involves aligning and merging of multiple point cloud datasets collected from different scan positions to obtain a complete scene. The presence of a sufficient number of planar surfaces in the data can typically improve registration results. However, due to the limited number of planar surfaces that can be extracted from the point cloud data of cut/fill slopes on highways, data registration is one of the biggest challenges for data processing. Although there exist many point cloud registration algorithms, there are no definitive guidelines for choosing the right algorithm suitable for data. This paper explains the registration procedure using the point cloud data obtained from two different landslide/rockslide potential sites on Oklahoma highways using a terrestrial LiDAR and reports technical challenges experienced during the registration process. This study presents a qualitative comparison of various registration algorithms, such as iterative close point (ICP), multi-station adjustments (MSA), and normal distribution transform (NDT). The result indicates that the most effective registration was achieved by using MSA plane patch features. These comparison results can facilitate the selection of the best registration algorithms suitable for the data and more accurate results. This paper also presents recommendations for the technical challenges experienced during the registration process.


  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: pp 40-50
  • Monograph Title: Construction Research Congress 2018: Infrastructure and Facility Management

Subject/Index Terms

Filing Info

  • Accession Number: 01683613
  • Record Type: Publication
  • ISBN: 9780784481295
  • Files: TRIS, ASCE
  • Created Date: Oct 18 2018 10:13AM