93 
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
Target 
Number 
Day 
Model 
Number 
MSE of 
Intersectio 
n [pm] 
RMS at Target 
Points [m] 
X 
Y 
Z 
43142 
Ma 
y 23 
501 
59.8 
1.20 
-0.09 
0.80 
43143 
Ma 
y 23 
506 
30.8 
0.39 
0.68 
2.0 
43144 
Ma 
y 23 
505 
29.6 
0.85 
1.257 
-2.5 
47803 
Ma 
y 24 
457 
48.4 
2.44 
2.89 
4.89 
47804 
Ma 
y 24 
457 
48.4 
2.13 
2.59 
4.28 
46465 
Ma 
y 26 
199 
30.9 
0.24 
1.54 
0.71 
46466 
Ma 
y 26 
324 
53.8 
0.56 
-0.59 
5.82 
Table 3. The results of performance check in-situ and camera 
calibration with LiDAR specific target points 
calibration process was tested by using a limited number of 
target points which were originally used for the validation of 
the LiDAR accuracy. Figure 9 shows the LiDAR-specific 
targets, made of trampolines with 2m diameter, and GPS- 
positioned at cm-level accuracy. 
(a) (b) 
Figure 8. The geo-registered LiDAR intensity image and 
orthoimages based on reference adjustment (a) and 
direct georeferencing (b). 
Figure 9. Target points 43142 (a) and 46466 (b) 
The image coordinates of check points were measured and the 
object coordinates were computed by combined intersection 
(direct sensor orientation). The overall results of the in-situ 
camera and boresight calibration performance check as a root 
means square errors at target points are shown in Table 3. The 
obtained root mean square errors at target points and mean 
square error of intersection confirmed that the determined in- 
situ camera and boresight calibration is optimal and stable, as 
there is not change day to day and from one location to another. 
5. CONCLUSIONS 
The system calibration of multi sensor airborne systems, 
including the boresight misalignment and geometric calibration 
of the imaging sensor has vital importance for achieving 
accurate geospatial data extraction performance. Any 
discrepancies between the assumed mathematical model and 
physical condition during the data acquisition can cause an 
error in object space. Because of this, the determination of the 
displacement vector and attitude difference between the camera 
and IMU body frame (boresight misalignment) and geometric 
calibration of camera are a critical issue for direct 
georeferencing. 
In this paper, in-situ sensor calibration was performed to 
integrate a small format camera into a high-performance 
LiDAR system. The investigation clearly showed that the 
Redlake MS 4100 digital camera could be successfully 
calibrated and boresighted to the LiDAR system, using only 
control information derived from the LiDAR data. Using a 
block of 21 images and 25 LiDAR-derived control points, 
bundle block adjustments with various parameters were used to 
perform an in-situ camera calibration. The focal length, the 
radial symmetric lens distortions and systematic image errors 
were accurately estimated (with respect to the camera quality). 
In addition, the boresight misalignment was simultaneously 
estimated using the BLUH and Applanix software products. 
The effect of orientation discrepancies were checked by 
computing y-parallaxes for each model at the reference block. 
In addition, an independent performance check was performed 
using LiDAR-specific ground targets. The combined results 
clearly proved that the determined in-situ calibration parameters 
were optimal and stable; in fact, the achieved accuracy appears 
to be quite good compared to the camera quality. 
ACKNOWLEDGEMENTS 
The B4 project was funded by the National Science Foundation 
and the ALTM 3100 system was generously provided by 
Optech International. The authors wish to acknowledge the 
support from the National Science Foundation and the 
contribution of many researchers from OSU, USGS, NCALM, 
UNAVCO and Optech International who participated in the B4 
mapping project, as well as, the many OSU students who did 
much of the GPS observations. The authors acknowledge the 
financial support given by The Scientific and Technological 
Council of Turkey (TUBITAK) provided for the post doctoral 
research studies of Naci Yastikli at The Ohio State University. 
REFERENCES 
Cramer M., 2005. EuroSDR: Digital Camera Calibration and 
Validation, Geolnformatic 2(8), March 2005, pp. 16-19 .