The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
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represents the airborne LiDAR data. Terrestrial data covers 
very narrow areas compared to the airborne data, but the point 
density is much higher. Even though small areas are selected 
from the terrestrial data for the process, the process can take 
some time, and one should give attention to the lack of a 
computer memory for computation. Both data were captured in 
the same area, but the overlap area of the two systems is not 
large. 
Figure 11. Terrestrial laser scanning data (a) is used as 
reference data to calibrate airborne laser scanning data (b) 
AX AY AZ Aco A $ Ak 
(m) (m) (m) (deg) (deg) (deg) 
Given 
0.210 0.190 -0.003 
0.168 
-0.035 
0.000 
parameters 
Normfml : 0.159 
N/A 
ICP 
-0.163 -0.080 -0.087 
0.068 
-0.052 
0.118 
Norm[m] : 0.119 
0 : 0.113 
ICPatch 
-0.032 -0.015 0.026 
0.009 
-0.012 
0.018 
Norm[m] : 0.139 
G : 0.125 
Planar 
0.108 -0.495 -0.003 
0.174 
0.040 
-0.032 
Patches 
Norm[m] : 0.129 
0 : 0.120 
Table 3. The LiDAR system calibration test using real data (6 
bore-sighting parameters) 
Table 3 shows the adjusted bore-sighting parameters from the 
proposed methods, respectively. To evaluate the adjusted 
parameters, average normal distances between the adjusted 
surface and reference surface are calculated. After the object 
surfaces are re-constructed using the new system parameters, a 
surface matching procedure was then carried out to find 
corresponding points between both data; ICPatch was used for 
this purpose, in this case. Consequently, normal distances 
between matched points and triangular patches are calculated. 
The real LiDAR data was captured along the rail-road areas in 
eastern Canada, and these areas are quite rural. Hence, it was 
hard to extract the planar patches, especially from man-made 
objects such as buildings. Since the distribution and 
configuration of control patches are important in terms of 
possible correlations between calibration parameters, one 
should give attention to the extraction of control data. From the 
control feature selection point of view, the other two 
approaches, using ICP and ICPatch, appear easier and more 
effective. These two methods, however, are very sensitive to the 
initial approximations and random error size. Even though 
pseudo-conjugate points from the ICP procedure and triangular 
patches from TIN are easier approaches in terms of establishing 
corresponding points for the rural areas, like this test area, the 
method using segmented planar patches can have reliable 
solutions and is not sensitive to the ill conditioned data like 
high random errors, if planar patches can satisfy the required 
condition; configuration and distribution. For these reasons, 
large and abundant planar patches are relatively better than the 
closest points and the closest TIN element; which are not very 
sensitive to random errors and initial approximations. 
Furthermore artificial control planar targets and well-known 
man-made objects can be considered as the ideal control data 
for the system calibration. 
Figure 12. (a) the TIN represents the reference data and points 
denote target data in 2D display, (b) in 3D display, reference 
data (terrestrial LiDAR) is mainly appeared along the vertical 
wall, while the points of the target data (airborne LiDAR) are 
very dense on the ground. 
4. CONCLUSIONS 
In this paper, the author introduces the airborne LiDAR system 
calibration procedure using the terrestrial LiDAR data which is 
capture in the same area. Because terrestrial LiDAR systems 
usually have shorter ranges and much higher point density, 
those object surface data works well for the airborne LiDAR 
system as reference data. Three approaches are used for 
extracting conjugate features; pseudo-conjugate points by ICP, 
conjugate poin'ts/triangles by ICPatch, and conjugate planar 
patches by plane segmentation. And the real data test shows 
that existing bore-sighting parameters are improved after 
calibrating system using LiDAR raw measurement, which is 
confirmed by calculating the normal distances between 
reference surfaces and adjusted surfaces. For increasing the 
robustness and reliability of the LiDAR system calibration, 
strong surface match procedure should be also considered in the 
future. 
REFERENCES 
Baltsavias, E., 1999. Airborne laser scanning: existing systems 
and firms and other resources, ISPRS Journal of 
Photogrammetry and Remote Sensing, 54 (2-3): 164-198. 
Bretar F., M. Pierrot-Deseilligny, and M. Roux, 2004. Solving 
the Strip Adjustment Problem of 3D Airborne Lidar Data., 
Proceedings of the IEEE IGARSS’04, 20-24 September, 
Anchorage, Alaska.