Effect of spatial sampling on pattern noise in insect-based motion detection

Insects perform highly complicated navigational tasks even though their visual system is relatively simple. The main idea of work in this area is to study the visual system of insects and to incorporate algorithms used by them in electronic circuits to produce low power, computationally simple, highly efficient, robust devices capable of accurate motion detection and velocity estimation. The Reichardt correlator model is one of the earliest and the most prominent biologically inspired models of motion detection developed by Hassentein and Reichardt in 1956. In an attempt to get accurate estimates of yaw velocity using an elaborated Reichardt correlator, we have investigated the effect of pattern noise (deviation of the correlator output resulting from the structure of the visual scene) on the correlator response. We have tested different sampling methods here and it is found that a circular sampled array of elementary motion detectors (EMDs) reduces pattern noise effectively compared to an array of rectangular or randomly selected EMDs for measuring rotational motion

Insect-Inspired Visual Self-Motion Estimation

Strübbe S. Insect-Inspired Visual Self-Motion Estimation. Bielefeld: Universität Bielefeld; 2019

Insect-Inspired Estimation of Self-Motion

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 optimal linear estimator incorporating prior knowledge about the environment. The optimal 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

Insect-Inspired Estimation of Self-Motion

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 optimal linear estimator incorporating prior knowledge about the enviroment. The optimal 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