Neuromorphic Translational Ego-motion Estimation using Log-Polar Motion Energy

We describe a neuromorphic hardware system for detecting patterns of expanding visual motion due to forward translation, which can be used for self-motion estimation. The system consists of a set of motion-energy filters, modeling the functional properties of direction tuned neurons in primary visual cortex. We apply the filters to log-polar sampled images to facilitate the construction of filters tuned to radial and angular directions of motion. We then describe how their outputs can be used to estimate direction of heading. We implemented this algorithm in a hardware system, which captures images from a camera and performs the required computations at 30 frames per second for images with 352 by 288 resolution.