CTRE is an Iowa State University center, administered by the Institute for Transportation.

Address: 2711 S. Loop Drive, Suite 4700, Ames, IA 50010-8664

Phone: 515-294-8103
FAX: 515-294-0467

Website: www.ctre.iastate.edu/

Iowa State University--Becoming the Best

Quantifying Road Roughness from Terrestrial Laser Scanning

1Image: Laser scanner set up on tripod in ditch pointed at newly constructed road pavement

Data collection using stationary laser scanner

2Flat square black and white reference point target on short pole

Flat black and white target for laser scan referenrce point used for post-processing registration

3Image: Sphere reference point on a short stand on aggregate road

Spherical target for laser scan reference point used for post-processing registration

4Image: Photographs being taken of aggregate road surface for photogrammetry

Taking images of aggregate road surface for photogrammetry

5Image: Comparison of terrestrial laser scanning and photogrammetry clouds for a parking lot after paving

Comparison of spatial difference in elevation between terrestrial laser scanning and photogrammetry clouds for a parking lot after paving


Principal investigators:

Project status


Start date: 06/15/15
End date: 10/31/15


Report: Comparison of Roadway Roughness Derived from LIDAR and SFM 3D Point Clouds (6.46 mb pdf) November 2015


Sponsor(s): Iowa Department of Transportation

About the research


The final report for this project describes a short-term study undertaken to investigate the potential for using dense three-dimensional (3D) point clouds generated from light detection and ranging (LIDAR) and photogrammetry to assess roadway roughness. Spatially continuous roughness maps have potential for the identification of localized roughness features, which would be a significant improvement over traditional profiling methods.

The report specifically illustrates the use of terrestrial laser scanning (TLS) and photogrammetry using a process known as structure from motion (SFM) to acquire point clouds and illustrates the use of these point clouds in evaluating road roughness.

Five roadway sections were chosen for scanning and testing: three aggregate road sections, one portland cement concrete (PCC) section, and one asphalt concrete (AC) section.

To compare clouds obtained from terrestrial laser scanning and photogrammetry, the coordinates of the clouds for the same section on the same date were matched using open source computer code.

The research indicates that the technologies described are very promising for evaluating road roughness. The major advantage of both technologies is the large amount of data collected, which allows the evaluation of the full surface.

Additional research is needed to further develop the use of dense 3D point clouds for roadway assessment.