The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
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laboratory and in situ calibrations is the only viable option for 
LiDAR system calibration. Typical anomalies in the LiDAR 
data indicating system calibration errors are: edges of the strips 
could bend up or down (scan angle error), horizontal surfaces 
have a visible mismatch between the known and the LiDAR 
point-defined surfaces (boresight misalignment or navigation 
error), vertical coordinates of LiDAR points over flat areas do 
not match the known vertical coordinate of the area (ranging or 
navigation error), objects, such as pavement markings made of 
retro reflective coatings, may show up above the surface level, 
although they should practically have identical vertical 
coordinates (lack of intensity correction of the range data), etc. 
The techniques to detect and ultimately compensate for errors 
fall into two broad categories based on whether they use 
absolute control or not. The first group includes most of the 
strip adjustment techniques and some of the sensor and 
boresight calibration methods. The ground control-based 
techniques encompass comparisons to reference surfaces, such 
as parking lots and buildings, and methods using LiDAR- 
specific control targets. Another categorization of the 
techniques is whether they only aim to remove observed 
differences, also called data driven methods, or they try to 
achieve the same objective through the sensor model, in other 
words, to calibrate the sensor model parameters. 
The use of dedicated LiDAR targets is a basic method to 
observe LiDAR point cloud differences at reference points and, 
consequently, to estimate errors. One of the first approaches to 
use LiDAR-specific ground targets was developed at OSU 
(Csanyi and Toth, 2007). The circular targets, optimized for a 
point density of 3-4 pts/m 2 and above, had a diameter of 2 m 
and used a different reflective coating on the center circle and 
outer ring. At the required point cloud density, the number of 
points returned from the targets allowed for accurate estimation 
of both vertical and horizontal differences. The technique has 
been used in several projects and provided highly accurate 
ground control for QA/QC (Toth et al., 2007a). In a similar 
implementation, small retro reflectors are placed in a certain 
shape of similar size, in which case the construction of the 
target is simpler while the processing is more complicated. 
Although, these solutions provide excellent results, their use is 
somewhat limited by economic factors; i.e., the installation and 
the necessary survey of the targets could be quite labor- 
intensive. Note that the processing of the LiDAR-specific 
ground targets is highly automated, and human intervention is 
only needed for the final evaluation of the results. 
To advance the use of ground targets for transportation corridor 
surveys, an economic method is proposed here that can achieve 
results comparable to using LiDAR-specific ground targets 
(Toth et al, 2007b). The use of pavement markings as ground 
control offers the advantage of being widely available in 
excellent spatial distribution, and require no installation. 
Certainly, the surveying of the targets is still needed, but it 
becomes less difficult with the increasing use of GPS VRS 
systems that can provide cm-level accuracy in real-time. The 
other condition of using pavement markings is the availability 
of LiDAR intensity data that is hardly a restriction with modem 
LiDAR systems. Note that the distinct appearance of the 
pavement markings in the LiDAR intensity image is essential to 
the proposed method, see Figure 1. The main steps of using 
pavement marking as ground control are briefly described in 
this paper. 
Figure 1. Typical pavement markings at an intersection 
(LiDAR point density was about 4 pts/m 2 ). 
2. THE CONCEPT 
The concept of the proposed method, including pavement 
marking extraction together with the parameterization of the 
marks based on LiDAR intensity data, the comparison with 
ground truth, and the determination of a transformation to 
correct the point cloud, analysis of the result, etc., is shown in 
Figure 2. The GPS-surveyed data of the pavement markings, 
represented in a series of points with cm-level accuracy are 
assumed to be available. For sensor calibration and/or strip 
adjustment, sufficient number of pavement markings with good 
spatial distribution is required to achieve good performance. 
Currently, only the most commonly found types of pavement 
markings are considered, such as stop bars, straight edge lines 
and curved edge lines. In each case, the survey data of the 
pavement markings are provided as point observed along the 
centerline of the markings. The LiDAR data, including range 
and intensity components, are assumed to be of a reasonable 
quality; i.e., with no gross errors, and thus, the point cloud 
accuracy is better than a meter. 
Figure 2. Overall workflow of the proposed method.