The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008
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158 157 156 155 154 62 61 60 59 58
—♦—Original data from IMU
in Terrestrial
Photogrammetry
Coordinate System -
The results calculated
through resection
—•—Corrected results
computed through
deviation angle error
- The results
calculated through
resection
Figure 4. The comparative results of Omega between the
original attitude data and the corrected attitude data
Original data fr»» IMU
in T«r*stri*l
ftiotograsatetry
Coordinate Sfst<* "
Ibe results calculated
through resection
Corrected results
oompttted through
deviation angle error
~ The results
calculated through
resect ion
188 1ST 18$ 188 184 $2 61 60 SS 88
Skaloud, J., 2006. Rigorous approach to bore-sight self
calibration in airborne laser scanning. ISPRS Journal of
Photogrammetry & Remote Sensing, 61, pp. 47-59.
Naci Yastikli, 2005. Direct sensor orientation for large scale
mapping-potential problems solutions. Photogrammetric Record,
20(111), pp. 274-284.
Mostafa, M, 2002. Camera/IMU boresight calibration: new
advances and performance analysis. ASPRS Meeting,
Washington, DC.
Pinto, L., 2002. Integrated INS/DGPS Systems: Calibration and
combined block adjustment. OEEPE Official Publications, 43,
pp. 85-96.
Mohamed, M., 2001. Calibration in multi-Sensor environment.
ION GPS, 11-14, PP. 2693-2699.
OTTO HOFMANN, 2005. Calibration and georeference of
aerial digital cameras. Photogrammetric Week 05’, pp. 105-109
Pinto, L., 2005. Experimental tests on the benefits of a more
rigorous model in IMU/GPS system calibration. ISPRS
Commission III, WG III/1.
Figure 5. The comparative results of Kappa between the
original attitude data and the corrected attitude data
Through analyzing Figure 3, Figure 4 and Figure5, we can find
the UAV remote sensing images attitude data correction model
proposed in this paper improves the precision of images’
attitude data. But the accuracy of the deviation angle error is
affected by systematic error of GPS / IMU, such as the error of
gyro random drift. So the systematic error correction model for
gyro random drift will be established to obtain higher precision
deviation angle error in the future studies.
Skaloud, J., 2003. Towards a more rigorous boresight
calibration. ISPRS International Workshop on Theory,
Technical and Realities of Inertial/GPS Sensor Orientation,
Commission 1, WGI/5, Castelldefels, Spain, 22-23.
ACKNOWLEDGEMENTS
This paper is sponsored by Nature Science Fund (NSF) of
China .No. 60602042. We would like to express our gratitude to
all the people who have participated in the UAV experiments.
4. CONCLUSIONS
In this paper, the error calibration model of the UAV airborne
attitude data is established, then the model is tested through a
set of UAV aerial remote sensing images. Experimental results
show that the model can effectively correct the original attitude
data acquired from the GPS/INS. Meanwhile, from the data
analysis we can see that the accuracy of deviation angle error is
affected by other systematic errors of the GPS / IMU, such as
the error of gyro random drift etc. So the systematic error
correction model for gyro random drift will be established to
obtain higher precision deviation angle error in the future
studies.
REFERENCES
Baumker, M., 2002. New calibration and computing method
for direct georeferencing of image and scanner data using the
position and angular data of an hybrid inertial navigation
System. Integrated Sensor Orientation, 43, pp. 197-212.
Cramer, M., 2002. System calibration for direct georeferencing.
International Archives of Photogrammetry and Remote Sensing,
34, pp. 79-84.