Abstract
We have been conducting a project to digitize the Bayon temple, located at the center of Angkor-Thom in the kingdom of Cambodia. This is a huge structure, more than 150 meters long on all sides and up to 45 meters high. Digitizing such a large-scale object in fine detail requires developing new types of sensors for obtaining data of various kinds related to irregular positions such as the very high parts of the structure occluded from the ground. In this article, we present a sensing system with a moving platform, referred to as the Flying Laser Range Sensor (FLRS), for obtaining data related to these high structures from above them. The FLRS, suspended beneath a balloon, can be maneuvered freely in the sky and can measure structures invisible from the ground. The obtained data, however, has some distortion due to the movement of the sensor during the scanning process. In order to remedy this issue, we have developed several new rectification algorithms for the FLRS. One method is an extension of the 3D alignment algorithm to estimate not only rotation and translation but also motion parameters. This algorithm compares range data of overlapping regions from ground-based sensors and our FLRS. Another method accurately estimates the FLRS’s position by combining range data and image sequences from a video camera mounted on the FLRS. We evaluate these algorithms using a IS-based method and verify that both methods achieve much higher accuracy than previous methods.
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References
Banno, A., & Ikeuchi, K. (2005). Shape recovery of 3D data obtained from a moving range sensor by using image sequences. In Proceedings of the international conference on computer vision (ICCV2005) (Vol. 1, pp. 792–799).
Besl, P. J., & McKay, N. D. (1992). A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14, 239–256.
Brown, D. (1976). The bundle adjustment—progress and prospect. In XIII congress of the ISPRS, Helsinki.
Chen, Y., & Medion, G. (1992). Object modeling by registration of multiple range images. Image and Vision Computing, 10(3), 145–155.
Christy, S., & Horaud, R. (1996). Euclidean shape and motion from multiple perspective views by affine iterations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(11), 1098–1104.
Chui, H., & Rangarajan, A. (2003). A new point matching algorithm for non-rigid registration. Computer Vision and Image Understanding, 89, 114–141.
Curless, B., & Levoy, M. (1996). A volumetric method for building complex models from range images. In Proceedings of SIGGRAPH’96 (pp. 303–312). New York: ACM.
Feldmar, J., & Ayache, N. (1996). Rigid, affine and locally affine registration of free-form surfaces. International Journal of Computer Vision, 18(2), 99–119.
Fischler, M. A., & Bolles, R. C. (1981). Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381–395.
Hähnel, D., Thrun, S., & Burgard, W. (2003). An extension of the ICP algorithm for modeling nonrigid objects with mobile robots. In Proceedings of the international joint conference on artificial intelligence (IJCAI).
Han, M., & Kanade, T. (1999). Perspective factorization methods for euclidean reconstruction (Technical report: CMU-RI-TR-99-22). Robotics Institute, Carnegie Mellon University.
Harris, C., & Stephens, M. (1988). A combined corner and edge detector. In Proceedings of Alvey vision conference (pp. 147–152).
Hirota, Y., Masuda, T., Kurazume, R., Ogawara, K., Hasegawa, K., & Ikeuchi, K. (2004). Designing a laser range finder which is suspended beneath a balloon. In Proceedings of the 6th Asian conference on computer vision (ACCV2004) (Vol. 2, pp. 658–663).
Ikeuchi, K., Nakazawa, A., Hasegawa, K., & Ohishi, T. (2003). The Great Buddha project: Modeling cultural heritage for VR systems through observation. In Proceedings of the 2nd IEEE and ACM international symposium on mixed and augmented reality (ISMAR2003).
Jacobs, D. A. (1977). The state of the art in numerical analysis. London: Academic Press.
Jain, V., Zhang, H., & van Kaick, O. (2007, accepted). Non-rigid spectral correspondence of triangle meshes. International Journal on Shape Modeling (via invitation to Special Issue of SMI 2006).
Kawakami, R., Tan, R. T., & Ikeuchi, K. (2005). Consistent surface color for texturing large objects in outdoor scenes. In Proceedings of the international conference on computer vision (ICCV2005) (Vol. 2, pp. 1200–1207).
Kurazume, R., Nishino, K., Zhang, Z., & Ikeuchi, K. (2002). Simultaneous 2D images and 3D geometric model registration for texture mapping utilizing reflectance attribute. In Proceedings of fifth Asian conference on computer vision (ACCV2002) (Vol. 1, pp. 99–106).
Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.
Marquardt, D. W. (1963). An algorithm for least-squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics, 11, 431–441.
Masuda, T., Hirota, Y., Nishino, K., & Ikeuchi, K. (2005). Simultaneous determination of registration and deformation parameters among 3D range images. In Proceedings of 5th international conference on 3-D digital imaging and modeling (3DIM2005) (pp. 369–376).
Matsui, K., Ono, S., & Ikeuchi, K. (2005). The climbing sensor: 3-D modeling of a narrow and vertically stalky space by using spatio-temporal range image. In International conference on intelligent robots and systems (IROS2005).
Miller, R., & Amidi, O. (1998). 3-D site mapping with the CMU autonomous helicopter. In The 5th international conference on intelligent autonomous systems.
Miyazaki, D., Oishi, T., Nishikawa, T., Sagawa, R., Nishino, K., Tomomatsu, T., Yakase, Y., & Ikeuchi, K. (2005). The Great Buddha project: Modelling cultural heritage through observation. In Proceedings of the 6th international conference on virtual systems and multimedia (VSMM2000) (pp. 138–145).
Moravec, H. P. (1977). Towards automatic visual obstacle avoidance. In Proceedings of 5th international joint conference on artificial intelligence (p. 584).
Nishino, K., & Ikeuchi, K. (2002). Robust simultaneous registration of multiple range images. In Proceedings of 5th Asian conference on computer vision (ACCV2002) (pp. 454–461).
Oishi, T., Sagawa, R., Nakazawa, A., Kurazume, R., & Ikeuchi, K. (2003). Parallel alignment of a large number of range images. In Proceedings of 4th international conference on 3-D digital imaging and modeling (3DIM2004) (pp. 195–202).
Oishi, T., Nakazawa, A., Kurazume, R., & Ikeuchi, K. (2005). Fast simultaneous alignment of multiple range images using index images. In Proceedings of 5th international conference on 3-D digital imaging and modeling (3DIM2005) (pp. 476–483).
Polak, E. (1971). Computational methods in optimization. New York: Academic Press.
Press, W. H., Flannery, B. P., Teukolsky, S. A., & Vetterling, W. T. (1988). Numerical recipes in C. Cambridge: Cambridge University Press.
Rusinkiewicz, S., & Levoy, M. (2001). Efficient variant of the ICP algorithm. In Proceedings of the 3rd international conference on 3-D digital imaging and modeling (3DIM2001) (pp. 145–152).
Smith, S. M., & Brady, M. (1997). SUSAN—a new approach to low level image processing. International Journal of Computer Vision, 23(1), 45–78.
Stoer, J., & Bulirsh, R. (1980). Introduction to numerical analysis. New York: Springer.
Szeliski, R., & Lavallée, S. (1996). Matching 3-D anatomical surfaces with non-rigid deformations using Octree-splines. International Journal of Computer Vision, 18(2), 171–186.
Thrun, S., Diel, M., & Haehnel, D. (2003). Scan alignment and 3-D surface modeling with a helicopter platform. In Proceedings of the 4th international conference on field and service robotics.
Wheeler, M. D. (1996). Automatic modeling and localization for object recognition. Ph.D. thesis, School of Computer Science, Carnegie Mellon University.
Wheeler, M. D., & Ikeuchi, K. (1995). Sensor modeling, probabilistic hypothesis generation, and robust localization for object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(3), 252–265.
Zhang, Z. (1994). Iterative point matching for registration of free-form curves and surfaces. International Journal of Computer Vision, 13, 119–152.
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Banno, A., Masuda, T., Oishi, T. et al. Flying Laser Range Sensor for Large-Scale Site-Modeling and Its Applications in Bayon Digital Archival Project. Int J Comput Vis 78, 207–222 (2008). https://doi.org/10.1007/s11263-007-0104-6
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DOI: https://doi.org/10.1007/s11263-007-0104-6