Autonomous Navigation in Three-Dimensional Urban Environments Using Wide-Field Integration of Optic Flow

Abstract

In this paper, a control theoretic framework is introduced to analyze an information extraction approach from patterns of optic flow based on analogs to wide-field motion-sensitive interneurons in the insect visuomotor system. An algebraic model of optic flow is developed, based on a parameterization of three-dimensional urban environments. It is shown that estimates of proximity and speed, relative to these environments, can be extracted using weighted summations of the instantaneous patterns of optic flow. Small perturbation techniques are then applied to link weighting patterns to outputs, which are applied as feedback to facilitate stability augmentation and perform local obstacle avoidance and terrain following. Additive noise and environment uncertainties are incorporated into an offline procedure for determination of optimal weighting functions. Stability is proven via local asymptotic analysis and the resulting approach demonstrated in simulation using a micro helicopter in a three-dimensional urbanlike environment.