Abstract
In this paper we present a method for mapping 3D unknown environments from stereo images. It is based on a dense disparity image obtained by a process of window correlation. To each image in the sequence a geometrical rectification process is applied, which is essential to remove the conical perspective of the images obtained with a photographic camera. This process corrects the errors in coordinates x and y to obtain a better matching for the map information. The mapping method is an application of the geometrical rectification and the 3D reconstruction, whose main purpose is to obtain a realistic appearance of the scene.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Sanchiz, J.M., Fisher, R.B.: Viewpoint Estimation in Three-Dimensional Images Taken with Perspective Range Sensors. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(11), 1324–1329 (2000)
Moravec, H.P.: Robot spatial perception by stereoscopic vision and 3D evidence grids. The Robotics Institute Carnegie Mellon University. Pittsburgh, Pennsylvania (1996)
Se, S., Lowe, D., Little, J.: Vision-based mobile robot localization and mapping using scale-invariant features. In: Proc. of IEEE International Conference on Robotics and Automation, Seoul, Korea (2001)
Martin, C., Thrun, S.: Real-time acquisition of compact volumetric maps with mobile robots. In: Proceedings of ICRA 2002: IEEE International Conference on Robotics and Automation (2002)
Martinsanz, G.P., de la Cruz García, J.M.: Visión por computador: imágenes digitales y aplicaciones. In: Ra-Ma, D.L. (ed.), Madrid (2001)
Broggi, A.: Robust Real-Time Lane and Road Detection in Critical Shadow Conditions. In: Proceedings IEEE International Symposium on Computer Vision, Coral Gables, Florida. IEEE Computer Society, Los Alamitos (1995)
Trucco, E., Verri, A.: Introductory techniques for 3-D Computer Vision. Prentice Hall, Englewood Cliffs (1998)
Cox, I., Ignoran, S., Rao, S.: A maximum lilelihood stereo algorithm. Computer Vision and Image Understanding 63 (1996)
Faugeras, O.: Three-dimensional computer vision: a geometric viewpoint. The MIT Press, Cambridge (1993)
Compañ, P., Satorre, R., Rizo, R.: Disparity estimation in stereoscopic vision by simulated annealing. In: Artificial Intelligence research and development, pp. 160–167. IOS Press, Amsterdam (2003)
Compañ, P., Satorre, R., Rizo, R., Molina, R.: Inproving depth estimation using colour information in stereo vision. In: IASTED International Conference on Visualization, Imaging and Image Processing (VIIP 2005), Benidorm (Spain), pp. 377–389 (2005)
Sánchez, A.J.G., Carmona, R.M., Arnedo, C.V.: Scene reconstruction and geometrical rectification from stereo images. In: World Multi-Conference on Systemics, Cybernetics and Informatics (WMSCI), Orlando (2005)
Sánchez, A.J.G., Carmona, R.M., Arnedo, C.V.: Discrete and Continuous Reconstruction of 3D Scenes from Disparity Maps. In: IASTED International Conference on Visualization, Imaging, and Image Processing, Benidorm (Spain), pp. 366–371 (2005)
Pollefeys, M., Koch, R., Van Gool, L.: A simple and efficient rectification method for general motion. In: Proc. International Conference on Computer Vision, Corfu (Greece), pp. 496–501 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sánchez, A.J.G., Carmona, R.M., Arnedo, C.V. (2006). Three-Dimensional Mapping from Stereo Images with Geometrical Rectification. In: Perales, F.J., Fisher, R.B. (eds) Articulated Motion and Deformable Objects. AMDO 2006. Lecture Notes in Computer Science, vol 4069. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11789239_22
Download citation
DOI: https://doi.org/10.1007/11789239_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36031-5
Online ISBN: 978-3-540-36032-2
eBook Packages: Computer ScienceComputer Science (R0)