The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
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Based on the comparison of the two representations of 
pavement markings, one obtained from the GPS survey and the 
other one from LiDAR intensity and range data, 2D/3D offset 
and orientation differences can be detected. Since the road 
surfaces are predominantly flat and mostly horizontal, the 
horizontal and vertical discrepancies can be separated in most 
of the cases. Analyzing the magnitudes of the observed 
differences and their spatial distribution, the LiDAR data 
quality can be assessed and, if needed, corrections can be 
applied to the LiDAR point cloud to improve the point position 
accuracy. The methodology for the correction could be based 
on either introducing a spatial transformation to reduce the 
differences at the controls, or trying to adjust the sensor 
parameters to achieve the same objective. In most of the cases, 
a 3D similarity transformation is applied, and the accuracy 
terms for both data sets are needed to properly characterize the 
data quality after applying the correction. Note that assessing 
the horizontal accuracy of the LiDAR point cloud is difficult, as 
it is mainly defined by the footprint of the laser pulse, which 
depends on the flying height and beam convergence; in addition, 
the impact of object surface characteristics could be also 
significant. In the following sections, the three key components 
of the proposed method, pavement marking extraction, curve 
fitting and matching, are discussed in detail. 
3. EXTRACTING PAVEMENT MARKINGS 
One of the first attempts on using LiDAR intensity data was 
demonstrated by Maas (2001), who describes the extension of a 
TIN-based matching technique using reflectance data (LiDAR 
intensity data) to replace surface height texture for the 
determination of planimetric strip offsets in flat areas with 
sufficient reflectance texture. Later, research interest steered 
toward conventional classification use of the intensity data. 
Song et al. (2002) proposed a technique to use intensity data for 
land-cover classification. A comprehensive study on processing 
both range and intensity data is provided by Sithole (2005). 
Kaasasalainen et al. (2005) provides a review on intensity data 
as applied to calibration. Finally, Ahokas et al. (2006) presents 
the results of a calibration test on intensity data using the 
Optech ALTM 3100. All these demonstrations emphasize the 
relative nature of the LiDAR intensity data; namely, different 
surfaces, data from different flying heights, and different 
surface orientations can produce exactly the same intensity 
values. Therefore, techniques to normalize or calibrate the 
intensity data, such as to reference the intensity and range 
values with respect to each other started becoming more 
common. 
The extraction of the pavement markings is based on the 
typically significant difference in the LiDAR intensity values 
between road surfaces and pavement markings, as illustrated in 
Figure 1. The selection of LiDAR points obtained from the 
pavement markings is greatly simplified by the availability of 
GPS survey data of the pavement markings, which can 
drastically reduce the search window. Figure 3 shows a typical 
case, where the GPS survey points are overlaid on the LiDAR 
image; note the minor, yet visible, mismatch between the 
pavement marking and the survey points. 
Depending on the overall LiDAR data quality, more precisely 
the horizontal accuracy of the point cloud, the actual search 
area is typically a narrow patch along the GSP-surveyed points 
with a width of less than 1 m. Ideally, an extracted patch should 
only contain points of road surfaces and pavement markings, 
with two dominant intensity ranges. Figure 4 depicts the 
histogram of the LiDAR intensities in such an area. The 
distribution shows a typical shape, characterized by most of the 
points clustered at lower intensities with slowly decreasing 
frequencies toward the higher intensities of the pavement 
markings. The reason why there is no clear separation between 
the points of the road surface and pavement markings is 
illustrated in Figure 5, which shows that the points falling on 
the boundary regions between the two areas have varying 
intensity values; note that the LiDAR footprint size is 
comparable to the pavement makings’ dimension. 
Figure 3. Freeway ramp with pavements markings and GPS- 
surveyed points (green).