Visual Confined-Space Navigation Using an Efficient Learned Bilinear Optic Flow Approximation for Insect-scale Robots

Abstract

Visual navigation for insect-scale robots is very challenging because in such a small scale, the size, weight, and power (SWaP) constraints do not appear to permit visual navigation techniques such as SLAM (Simultaneous Localization and Mapping) because they are likely to be too power-hungry. We propose to use a biology-inspired approach, which we term the bilinear optic flow approximation, that is more computationally efficient. We build on previous work that has shown that the bilinear approximation can be used for visual servoing. Here, we show that a bilinear approximator can be learned that is able to stabilize the heading of a robot while performing continuous forward motion in a corridor-shaped environment. This is a necessary capability for confined-space navigation that insect-sized robots are likely to perform. In this work, we describe the underlining methodology of the method and built a 2D visual simulation environment and omnidirectional camera model to validate our results.