Abstract
In this paper we introduce a new approach to adaptive stereoscopic Vision. We use genetic programming, where the input to the individuals is raw pixel data from stereo image-pairs acquired by two CCD cameras. The output from the individuals is the disparity map, which is transformed to a 3D map of the captured scene using triangulation. The used genetic engine evolves machine-coded individuals, and can thereby reach high Performance on weak computer architectures. The evolved individuals have an average disparity-error of 1.5 pixels, which is equivalent to an uncertainty of about 10% of the true distance. This work is motivated by applications to the control of autonomous humanoid robots — The Humanoid at Project at Chalmers.
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References
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© 2000 Springer-Verlag Berlin Heidelberg
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Graae, C.T.M., Nordin, P., Nordahl, M. (2000). Stereoscopic Vision for a Humanoid Robot Using Genetic Programming. In: Cagnoni, S. (eds) Real-World Applications of Evolutionary Computing. EvoWorkshops 2000. Lecture Notes in Computer Science, vol 1803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45561-2_2
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DOI: https://doi.org/10.1007/3-540-45561-2_2
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