A Robust Registration Method for the Reconstruction of 3D Urban Objects using Ground-based Laser Range Scanner

There is potentially strong demand for detailed 3D urban spatial data especially for advanced automobile/pedestrian guidance system and radio disturbance analysis for telecommunication and so forth. Ground-based laser range scanner is one of the promising devices to efficiently acquire detailed 3D s...

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Published inJournal of the Japan society of photogrammetry and remote sensing Vol. 39; no. 2; pp. 52 - 63
Main Authors ZHAO, Huijing, SHIBASAKI, Ryosuke
Format Journal Article
LanguageEnglish
Published Japan Society of Photogrammetry and Remote Sensing 2000
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ISSN0285-5844
1883-9061
1883-9061
DOI10.4287/jsprs.39.2_52

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Abstract There is potentially strong demand for detailed 3D urban spatial data especially for advanced automobile/pedestrian guidance system and radio disturbance analysis for telecommunication and so forth. Ground-based laser range scanner is one of the promising devices to efficiently acquire detailed 3D spatial data such as facade of buildings and detailed urban objects along streets. From a single laser range image, however, entire surface of an urban object can not be covered due to occlusions. Multiple range images acquired from different locations and with different angles have to be registered with each other to minimize occlusions and to generate more complete 3D models. In this paper, a robust method for registering laser range images of an urban object like buildings is proposed under the assumption that the laser scanner is located so that the rotating axis is vertical to the ground. Registration is achieved in two steps; a pairwise registration of successive range images and simultaneous registration of multiple range images to adjust errors accumulated in the pairwise registration process. Pair-wise registration method to determine 4 transformation parameters, a horizontal rotation angle and three translation parameters is based on “Z-image”, which is generated by projecting 3D points computed from a range image onto the horizontal (x-y) plane. In the simultaneous registration of multiple images, accumulation of estimation error in pairwise registration is adjusted with a least square minimization method. An outdoor experiment is conducted to reconstruct a 3D model of the buildings in the campus of the University of Tokyo using twenty-nine range images. With the registration method, around twenty-three pairs of range images were automatically registered, while six pairs were registered with a manually assigned initial transformation. A 3D model of the buildings was generated in an automated mode. Accuracy of the model was examined using a 1: 500 scale digital map and GPS measured location of viewpoints.
AbstractList There is potentially strong demand for detailed 3D urban spatial data especially for advanced automobile/pedestrian guidance system and radio disturbance analysis for telecommunication and so forth. Ground-based laser range scanner is one of the promising devices to efficiently acquire detailed 3D spatial data such as facade of buildings and detailed urban objects along streets. From a single laser range image, however, entire surface of an urban object can not be covered due to occlusions. Multiple range images acquired from different locations and with different angles have to be registered with each other to minimize occlusions and to generate more complete 3D models. In this paper, a robust method for registering laser range images of an urban object like buildings is proposed under the assumption that the laser scanner is located so that the rotating axis is vertical to the ground. Registration is achieved in two steps; a pairwise registration of successive range images and simultaneous registration of multiple range images to adjust errors accumulated in the pairwise registration process. Pair-wise registration method to determine 4 transformation parameters, a horizontal rotation angle and three translation parameters is based on “Z-image”, which is generated by projecting 3D points computed from a range image onto the horizontal (x-y) plane. In the simultaneous registration of multiple images, accumulation of estimation error in pairwise registration is adjusted with a least square minimization method. An outdoor experiment is conducted to reconstruct a 3D model of the buildings in the campus of the University of Tokyo using twenty-nine range images. With the registration method, around twenty-three pairs of range images were automatically registered, while six pairs were registered with a manually assigned initial transformation. A 3D model of the buildings was generated in an automated mode. Accuracy of the model was examined using a 1: 500 scale digital map and GPS measured location of viewpoints.
Author SHIBASAKI, Ryosuke
ZHAO, Huijing
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References_xml – reference: 17) R. E. Blahut, Principles and Practice of Information Theory, Addison-Wesley, Reading, MA, 1987.
– reference: 14) M. Okutomi, T. Kanade, A Multiple-Baseline Stereo, IEEE Trans. PAMI, vol.15, no.4, 1993.
– reference: 12) K. Ng, V. Sequeira, S. Butterfield, D. Hogg, J. G. M. Goncalves, An Integrated Multi-sensory System for Photo-Realistic 3D Scene Reconstruction, Int. Archive of Photogrammetry and Remote Sensing, vol. XXXII, part 5, pp356-363, 1998.
– reference: 2) B. Parvin and G. Medioni, A Constraint Satisfaction Network for Matching 3D Objects, Proc. Int. Conf. on Neural Networks, Vol.2, pp.281-286, 1989
– reference: 9) K. Higuchi, M. Hebert, and K. Ikeuchi, Building 3-D Models from Unregistered Range Images, Graphical Models and Image Processing, vol.57, No.4, pp.315-333, 1995.
– reference: 4) F. M. Ingels, Information and Coding Theory, In text educational publishers, Scranton, Pennsylvania, 1971.
– reference: 13) M. J. P. M. Lemmens, H. Deijkers, P. A. M. Looman, Building detection by fusion airborne laser-altimeter DEMs and 2D digital maps, Int. Archive of Photogrammetry and Remote Sensing, vol. 32, Part3-4W2, pp.42-49, 1997.
– reference: 15) N. Haala, C. Brenner, C. Statter, An integrated system for urban model generation, Int. Archive of Photogrammetry and Remote Sensing, vol. XXXII, part 2, pp.96-103, 1998.
– reference: 3) C. Thorpe, M. H. Hebert, T. Kanade, S. A. Shafer, Vision and Navigation for the Carnegie -Mellon Navlab, IEEE Trans. PAMI, vol.10, no.3, pp.363-373, 1988.
– reference: 20) S. Ozawa, M. Notomi, H. Zen, A Wide Scope Modeling to Reconstruct Urban Scene, Int. Archive of Photogrammetry and Remote Sensing, vol. XXXII, part 5, pp.370-376, 1998.
– reference: 18) R. Krishnapuram, D. Casasent, Determination of Three-Dimensional Object Location and Orientation from Range Images, IEEE Trans. PAMI, vol.11, no.11, pp.1158-1167, 1989.
– reference: 23) 金谷健一, 空間データの数理, 1995.
– reference: 16) P. Wang, C. Li, H. Zen, M. Sakauchi, 3D Information Acquisition From Plane Spatio-Temporal Images with Complicated Camera Motion, Proc. of IAPR Workshop on Machine Vision Applica-tion, Tokyo, 1996.
– reference: 8) H. Zhao, R. Shibasaki, Automated Registration of Ground-Based Laser Range Image for Reconstructing Urban 3D Object, Int. Archive of Photogrammetry and Remote Sensing, Vol. 32, Part3-4 W2, pp.27-34, 1997.
– reference: 11) K. Kanatani, Geometric Computation for Machine Vision, Oxford University Press, Oxford, U. K., 1993.
– reference: 22) Y. Chen, G. Medioni, Object Modelling by Registration of Multiple Range Images, Image and Vision Computing, vol.10, no.3, pp.145-155, 1992.
– reference: 21) W. Forstner, Image Analysis Techniques for Digital Photogrammetry, Proc. 42th Photogrammetric Week, Series of the Institute of Photogrammetry, Stuttgart Univ., vol.13, pp.205-221, 1989.
– reference: 1) B. Kamgar-Parsi, J. L. Jones, A. Rosenfeld, Registration of Multiple Overlapping Range Image without Distinctive Features, IEEE Trans. PAMI, vol.13, no.9, 1991.
– reference: 5) G. Champleboux, S. Lavallee, R. Szeliski, L. Brunie, From Accurate Range Imaging Sensor Calibration to Accurate Model-Based 3-D Object Localization, Proc. CVPR, 1992.
– reference: 6) G. Vosselman, Relational matching, Lecture notes in computer science 628, Springer-Verlag, Berlin Heidelberg, 1992.
– reference: 7) H. Shum, K. Ikeuchi, and R. Reddy, Virtual Reality Modeling from a Sequence of Range Images, Proc. IEEE/RSJ Intern. Conf on Intelligent Robots and Systems, pp.703-710, 1994.
– reference: 10) K. L. Boyer and A. C. Kak, Structural Stereopsis for 3-D Vision, IEEE Trans. PAMI, vol.10, no.2, pp.144-166, 1988.
– reference: 19) Sanfeliu and K. S. Fu, A Distance Measure between Attributed Relational Graphs for Pattern Recognition, IEEE TSMC, vol.13, no.3, pp.353-362, 1983.
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Title A Robust Registration Method for the Reconstruction of 3D Urban Objects using Ground-based Laser Range Scanner
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