CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation 
192 
(Rc„+t-c M )-n M =0 
=1 
/?* 
1 -a 3 
-a, 
a 2 
-a, 
1 
(3-3) 
The rotation angles (a t , a 2 , a 3 ) and the translation components 
(/i, A. /3) are the six variables to be determined. Each 
corresponding pair of planes E D , E M yields two linear equations 
(3.3), therefore at least three pairs have to be identified in the 
data to compute the rigid transformation (R,t). In general, more 
correspondences can be found at urban areas. The resulting 
overdetermined system can be solved approximately by 
inverting the normal equations. In addition, the area of the 
planar patches can be used as a weighting factor. Finally, the 
corrected position of the sensor in the model coordinate system 
is given as R-pops +t an ^ the orientation is corrected to R R lMV . 
4. EXPERIMENTS 
We tested the proposed methods on the basis of real sensor data 
which were recorded 300 meters above the old town of Kiel, 
Germany. Data available from four flights over this urban 
terrain led to the database shown in Figure 4. Additional two 
flights were considered to prove the concept of terrain based 
navigation (Figure 8). For this purpose, 1000 randomly chosen 
displacement vectors in the range [5 m, 20 m] were added to the 
exact sensor positions and it has been checked if these offsets 
are corrected automatically. Figure 10 shows the average 
displacement between calculated and exact sensor position 
against the number of matching pairs of planes. With our data, 
we were able to reduce the average offset in sensor position to 
1.5 m if at least 25 pairs of associated surfaces can be found 
(standard deviation: 0.5 m). These numbers most likely depend 
on additional conditions, e.g. aircraft altitude, aircraft speed, 
number and orientation of facades and rooftops. 
Figure 10. Average displacement against number of planes. 
5. CONCLUSION AND FUTURE WORK 
The examples presented in this paper were obtained with an 
experimental sensor system, for which data analysis can only be 
done offline to show the feasibility of the proposed approach. 
Nevertheless, we guess that all computations can be 
accomplished in real-time, with an efficient implementation and 
appropriate hardware. In our experiments, we were able to align 
the model and the ALS data such that matching objects show an 
average distance of 8 cm after the registration. This absolute 
exactness is not necessarily transferable to the sensor position 
(see Section 4). With a larger distance between helicopter and 
the terrain, impreciseness of the sensor orientation has a 
considerably higher impact on the overall displacement. For 
example, an angular error of 0.1 0 would lead to a shift of 1 m in 
a distance of 600 m. The absolute exactness of the estimated 
sensor position improves significantly when considering larger 
areas and/or shorter ranges, e.g. when approaching the terrain at 
low altitude. In future work, we will analyze these influences in 
more detail, and we will focus on on-line change detection. 
6. REFERENCES 
Besl, P.J., McKay, N.D., 1992. A method for registration of 3-D shapes. 
IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 
14, No. 2, pp. 239-256. 
Brenner, €., Dold, C., Ripperda, N., 2008. Coarse orientation of 
terrestrial laser scans in urban environments. ISPRS Journal of 
Photogrammetry and Remote Sensing 63 (1), pp. 4-18. 
Filin, S., 2003. Recovery of Systematic Biases in Laser Altimetry Data 
Using Natural Surfaces. Photogrammetric Engineering & Remote 
Sensing 69 (11), pp. 1235-1242. 
Filin, S., Pfeifer, N., 2006. Segmentation of airborne laser scanning 
data using a slope adaptive neighborhood. ISPRS Journal of 
Photogrammetry and Remote Sensing 60 (2), pp. 71-80. 
Fischler, M.A., Bolles, R.C., 1981. Random sample consensus: a 
paradigm for model fitting with applications to image analysis and 
automated cartography. CACM 24 (6), pp. 381-395. 
Hebei, M., Stilla, U., 2008. Pre-classification of points and 
segmentation of urban objects by scan line analysis of airborne LiDAR 
data. International Archives of Photogrammetry, Remote Sensing and 
Spatial Information Sciences, Vol. 37, Part B3a, pp. 105-110. 
Jiang, X., Bunke, H„ 1994. Fast Segmentation of Range Images into 
Planar Regions by Scan Line Grouping. Machine Vision and 
Applications 7 (2), pp. 115-122. 
Rabbani, T., Dijkmann, S„ van den Heuvel, F., Vosselman, G., 2007. 
An integrated approach for modelling and global registration of point 
clouds. ISPRS Journal of Photogrammetry and Remote Sensing 61 (6), 
pp. 355-370. 
Rieger, P., 2008: The Vienna laser scanning survey. GEOconnexion 
International Magazine, May 2008, pp. 40-41. 
Schenk, T., 2001. Modeling and Analyzing Systematic Errors in 
Airborne Laser Scanners. Technical Notes in Photogrammetry 19. The 
Ohio State University, Columbus, USA. 42 p. 
Schnabel, R., Wahl, R., Klein, R., 2006. Shape Detection in Point 
Clouds. Technical report No. CG-2006-2, Universitaet Bonn, ISSN 
1610-8892. 
Schulz, K.R., Scherbarth, S., Fabry, U., 2002. HELLAS: Obstacle 
warning system for helicopters. Laser Radar Technology and 
Applications VII, Proceedings of the International Society for Optical 
Engineering 4723, pp. 1-8. 
Sithole, G., Vosselman, G., 2004. Experimental comparison of filter 
algorithms for bare-earth extraction from airborne laser scanning point 
clouds. ISPRS Journal of Photogrammetry and Remote Sensing 59 (1 - 
2), pp. 85-101. 
Skaloud, J., Lichti, D., 2006. Rigorous approach to bore-sight self 
calibration in airborne laser scanning. ISPRS Journal of 
Photogrammetry & Remote Sensing 61 (1), pp. 47-59. 
Toth, C.K., Grejner-Brzezinska, D.A., Lee, Y.-J., 2008. Recovery of 
sensor platform trajectory from LiDAR data using reference surfaces. 
Proceedings of the 13 th FIG Symposium and the 4 ,h IAG Symposium, 
Lisbon, Portugal, 10 p. 
Vosselman, G., Gorte, B.G.H., Sithole, G., Rabbani, T., 2004. 
Recognising structure in laser scanner point clouds. International 
Archives of Photogrammetry, Remote Sensing and Spatial Information 
Sciences 46 (8), pp. 33-38. 
Wehr, A., Lohr, U., 1999. Airborne Laser Scanning - an Introduction 
and Overview, ISPRS Journal of Photogrammetry and Remote Sensing 
54, pp. 68-82.