Abstract
This paper presents our simple and easy to use method to obtain a 3D textured model. Our algorithm consists of building a measurement-based 2D metric map which is acquired by laser range-finder, texture acquisition/stitching and texture-mapping to corresponding 3D model. The algorithm is applied to 2 cases which are corridor and space that has the four walls like room of building. The proposed algorithm can be applied to 2D/3D model-based remote surveillance system through WWW. The application terminals of this environment model for interactive VR that we consider are PC, PDA and Mobile phone. Especially, this paper introduces a case of service on WIPI-based mobile phone.
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References
Liu, Y., et al.: Using EM to Learn 3D Models of Indoor Environments with Mobile Robots. In: 18th Int’l. Conf. on Machine Learning, Williams College, June 28-July 1 (2001)
Hanel, D., et al.: Learning Compact 3D Models of Indoor and Outdoor Environments with a Mobile Robot. Elsevier Science, Robotics and Autonomous Systems 44(1), 15–27 (2003)
Wulf, O., et al.: 2D Mapping of Cluttered Indoor Environments by Means of 3D Perception. In: Proceedings of the 2004 IEEE, Int’l. Conf. on Robotics & Automation, New Orleans (April 2004)
Peter, B., et al.: 3D Modeling of Indoor Environments by a Mobile Robot with a Laser Scanner and Panoramic Camera. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2004 (2004)
Pervölz, K., et al.: Automatic Reconstruction of Colored 3D Models. In: Robotik 2004, Munich, Germany (June 2004)
Paulo, D., et al.: Registration and fusion of intensity and range data for 3D modeling of real world scenes. In: Fourth International Conference on 3-D Digital Imaging and Modeling (2003)
Stamos, I., Allen, P.K.: 3-D Model Construction Using Range and Image Data. In: IEEE International Conference on Computer Vision and Pattern Recognition, South Carolina, June 2000, vol. I, pp. 531–536 (2000)
Aliaga, D., Yanovsky, D., Carlbom, I.: Sea of images: A dense sampling approach for rendering large indoor environments. In: IEEE Computer Graphics & Applications, Special Issue on 3D Reconstruction and Visualization, November/December 2003, pp. 22–30 (2003)
Faugeras, O.D.: Three-Dimensional Computer Vision: A Geometric Viewpoint. Mit Press, Cambridge (1993)
Debevec, P.: Reconstructing and Augmenting Architecture with Image-Based Modeling, Rendering and Lighting. In: Proceedings of the International Symposium on Virtual Architecture (VAA 2001), Dublin, June 21-22, pp. 1–10 (2001)
Zhang: Flexible Camera Calibration by Viewing a Plane from Unknown Orientations. In: ICCV 1999 (1999)
Chae, H., Lee, J., Yu, W.: A Localization Sensor Suite for Development of Robotic Location Sensing Network. In: International Conference on Ubiquitous Robots and Ambient Intelligence, ICURAI 2005 (2005)
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© 2006 Springer-Verlag Berlin Heidelberg
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Jo, S., Kwon, YM., Ko, H. (2006). Indoor Environment Modeling for Interactive VR – Based Robot Security Service. In: Pan, Z., Cheok, A., Haller, M., Lau, R.W.H., Saito, H., Liang, R. (eds) Advances in Artificial Reality and Tele-Existence. ICAT 2006. Lecture Notes in Computer Science, vol 4282. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941354_130
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DOI: https://doi.org/10.1007/11941354_130
Publisher Name: Springer, Berlin, Heidelberg
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