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 Project at Chalmers.
CITATION STYLE
Graae, C. T. M., Nordin, P., & Nordahl, M. (2000). Stereoscopic vision for a humanoid robot using genetic programming. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1803, pp. 12–21). Springer Verlag. https://doi.org/10.1007/3-540-45561-2_2
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