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

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-motionfrom the optic flow. We present a theory for the construction ofan estimator consisting of a linear combination of optic flow vectors that incorporates prior knowledge both about the distance distribution ofthe environment, and about the noise and self-motion statistics of the sensor. The estimator is tested on a gantry carrying an omnidirectional visionsensor. The experiments show that the proposed approach leads to accurate and robust estimates of rotation rates, whereas translation estimatesturn out to be less reliable.