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.