Abstract
Though image processing algorithms are sophisticated and provided as software libraries, it is still difficult to assure that complicated programs can work in various situations. In this paper, we propose a novel global pose estimation method for network cameras to actualize auto-calibration. This method uses native information from images. The sets of partial information are integrated with particle filters. Though some kinds of limitation still exist in the method, we can verify that the particle filters can deal with the nonlinearity of estimation with the experiment.
This research is partially supported by the Ministry of Education, Science, Sports and Culture, Grant-in-Aid for Young Scientist (B) (No. 19760163).
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© 2009 Springer-Verlag Berlin Heidelberg
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Ueda, R., Nikolaidis, S., Hayashi, A., Arai, T. (2009). Global Pose Estimation of Multiple Cameras with Particle Filters. In: Asama, H., Kurokawa, H., Ota, J., Sekiyama, K. (eds) Distributed Autonomous Robotic Systems 8. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00644-9_7
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DOI: https://doi.org/10.1007/978-3-642-00644-9_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00643-2
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