Full text: Proceedings; XXI International Congress for Photogrammetry and Remote Sensing (Part B1-3)

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
1082 
Lens 
RMS_X 
[mm] 
RMSY 
[mm] 
RMSZ 
[mm] 
35 mm 
±1.95 
±1.08 
±1.90 
1.8 
50 mm 
±0.62 
±0.34 
±0.92 
1.6 
80 mm 
±0.51 
±0.62 
±0.80 
0.9 
Table 7. Quality check of the 2D camera calibration 
Lens 
RMS_X 
[mm] 
RMSY 
[mm] 
RMSZ 
[mm] 
35 mm 
±0.30 
±0.18 
±0.64 
0.5 
50 mm 
±0.34 
±0.32 
±0.64 
0.6 
80 mm 
±0.19 
±0.34 
±0.59 
0.5 
Table 8. Quality check of the 3D camera calibration 
3. CONCLUSIONS 
Marker measurement can be fully automated using the planar 
2D target. PhotoModeler and our in-house developed software 
give very similar results. In the 3D case, automation is more 
difficult and error prone. We will need to further improve our 
software in order to reduce the number of gross errors (up to 
5%). 
If the ellipse operator is used for automated point 
measurements, the marker size must be chosen carefully, 
depending on the image scales used. If the size of the imaged 
markers gets too small, the accuracy of the measurement will 
decrease. If markers get larger, systematic errors will increase 
(eccentricity error and ellipse deformation caused by radial 
distortion). A marker diameter of 20-50 image pixels has been 
found to be the optimal size. 
Focal length and radial distortion are stable and can be 
determined very accurately using the 3D test field. Using the 
2D target, these parameters show a much higher variation and 
lower significance. The principal point is very unstable, varying 
up to 50pm between calibrations. This has been expected 
because of the unstable connection between the camera body 
and the digital back. It is therefore necessary to recalibrate the 
camera each time the digital back has been removed (e.g. for 
sensor cleaning). 
A significant scale difference (1.5E-4) has been determined for 
the x and y component of the sensor. This scale difference adds 
up to 7.2pm (more than one pixel) for the longer sensor 
dimension. This results in a non-square pixel size of 6.800 x 
6.801pm. 
Setting focus to infinity for calibration causes problems when 
close range targets are used. Images out of focus will be 
blurred, especially when narrow angle lenses are used and 
object distance gets too short. Although the ellipse fitting 
operator can handle blurred images to a certain extent, accuracy 
of image measurements will be reduced (e.g. for the 80mm lens 
using the 2D target). 
The quality of the calibration can be checked independently 
only for the 2D calibration (in the 3D case, the same images are 
used for calibration and quality check). A relative accuracy of 
1:10000 equals 0.8/0.5/0.4mm (for the 35/50/80mm lenses, 
respectively) in object space (planar component) and 0.25mm (z 
component). As can be seen from Tables 7 and 8 this quality 
criterion is not met for the 2D calibration, whereas relative 
accuracies of up to 1:20000 (planar positioning) and 1:4000 (Z 
component) are achieved using the 3D calibration. 
The H3D camera can therefore be used for close range 
applications (e.g. architectural photogrammetry) without self 
calibration. If parameters of a 2D calibration are used, self 
calibration (focal length and radial distortion parameters) 
should be applied if reliable control points are available. This is 
also recommended for aerial (small scale) projects, even for 3D 
calibration. 
4. REFERENCES 
Dold, J., 1997: Ein hybrides photogrammetrisches Industrie 
messsystem höchster Genauigkeit und seine Überprüfung. 
Schriftenreihe, Studiengang Vermessungswesen, Universität der 
Bundeswehr München, Heft 54, pp. 26-32. 
Fauner, A., 2008: Erstellung einer Applikation zur Kalibrierung 
von Digitalkameras. Unpublished master thesis, Graz 
University of Technology, in German, 91 p. 
Kaufmann, V., Ladstädter, R., 2005: Elimination of color 
fringes in digital photographs caused by lateral chromatic 
aberration. Proceedings of the XX International Symposium 
CIPA 2005, 26 September - 1 October 2005, Turin, Italy, Vol. 
l,pp. 403-408. 
Killian, K., 1955: Über das Rückwärtsschneiden im Raum. 
Österreichische Zeitschrift für Vermessungswesen, 43, pp. 97- 
104, pp. 171-179. 
Längauer, K., 2008: Entwicklung von MATLAB-Routinen zur 
automatischen Bildkoordinatenmessung von kreisförmigen 
Mustern in Digitalbildem für Zwecke der Kamerakalibrierung. 
Unpublished bachelor thesis, Graz University of Technology, in 
German, 39 p. 
Luhmann, T., 1986: Ein Verfahren zur rotationsinvarianten 
Punktbestimmung. Bildmessung und Luftbildwesen, 4/1986, pp. 
147-154. 
Luhmann, T., Robson, S., Kyle, S., Harley, L, 2006: Close 
Range Photogrammetry: Principles, Methods and Applications. 
Whittles Publishing, Dunbeath, Scotland, UK. 
PhotoModeler, 2008: http://www.photomodeler.com (accessed 
1 April 2008) 
Raggam, H., Wack, R., Gutjahr, K., 2007: Mapping Capability 
of a Low-Cost Aerial Data Acquisition Platform - First Results. 
ISPRS-Workshop, Commission VI, WG VI/4, "High Resolution 
Earth Imaging for Geospatial Information", Hannover, May 
29th - June 1st, 2007. 
5. ACKNOWLEDGEMENTS 
We wish to thank Dr. Michael Gruber from Vexcel Imaging 
Graz (a 100% subsidiary of Microsoft Corporation) for the 
opportunity provided to use their in-house calibration test field.
	        
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