CMRT09

Stilla, Uwe

Bibliographic data

Monograph

Persistent identifier:: 856955019

Author:: Stilla, Uwe

Title:: CMRT09

Sub title:: object extraction for 3D city models, road databases, and traffic monitoring ; concepts, algorithms and evaluation ; Paris, France, September 3 - 4, 2009 ; [joint conference of ISPRS working groups III/4 and III/5]

Scope:: X, 234 Seiten

Year of publication:: 2009

Place of publication:: Lemmer

Publisher of the original:: GITC

Identifier (digital):: 856955019

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: UTILIZATION OF 3D CITY MODELS AND AIRBORNE LASER SCANNING FOR TERRAIN-BASED NAVIGATION OF HELICOPTERS AND UAVs M. Hebel, M. Arens, U. Stilla

Document type:: Monograph

Structure type:: Chapter

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.

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