Linear Combinations of Optic Flow Vectors for Estimating Self-Motion: a 
Real-World Test of a Neural Model

Franz, MO; Chahl, JS

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Linear Combinations of Optic Flow Vectors for Estimating Self-Motion: a Real-World Test of a Neural Model

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Franz, MO
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Franz, M., & Chahl, J. (2003). Linear Combinations of Optic Flow Vectors for Estimating Self-Motion: a Real-World Test of a Neural Model. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Advances in Neural Information Processing Systems 15 (pp. 1319-1326). Cambridge, MA, USA: MIT Press.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-DB45-0

Abstract

The tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during self-motion. In this study, we examine whether a simplified linear model of these neurons can be used to estimate self-motion from the optic flow. We present a theory for
the construction of an estimator consisting of a linear combination of optic flow vectors that incorporates prior knowledge both about the distance distribution of the environment, and about the noise and self-motion statistics of the sensor. The estimator is tested on a gantry carrying an omnidirectional vision sensor. The experiments show
that the proposed approach leads to accurate and robust estimates of rotation rates, whereas translation estimates turn out to be less reliable.