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Abstract

Tangential neurons in the fly brain are sensitive to the typical optic
flow patterns generated during egomotion. In this study, we examine
whether a simplified linear model based on the organization principles
in tangential neurons can be used to estimate egomotion 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 egomotion 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 are of reasonable quality, albeit less
reliable.