Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)
Matthias Franz, Javaan Chahl
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 esti- mate self-motion from the optic ﬂow. We present a theory for the con- struction of an estimator consisting of a linear combination of optic ﬂow vectors that incorporates prior knowledge both about the distance distri- bution of the environment, and about the noise and self-motion statistics of the sensor. The estimator is tested on a gantry carrying an omnidirec- tional vision sensor. The experiments show that the proposed approach leads to accurate and robust estimates of rotation rates, whereas transla- tion estimates turn out to be less reliable.