State Estimation Using Optical Flow from Parallax-Weighted Feature Tracking

Abstract

Computer vision presents an attractive sensor option for micro aerial vehicle (MAV) applications due to the payload and performance restrictions imposed by typical mission scenarios. Optical flow can be measured by tracking the perceived motion of feature points between successive image frames. This perceived featurepoint motion yields information regarding vehicle motion as described through geometric relationships. This paper presents an optimization-based approach to estimate aircraft angular rates and wind-axis angles using monocular vision. A bias in the optical-flow equations is lev eraged to decouple components resulting from angular and translational motion, respectively. Attempts to resolve the ambiguity introduced by the loss of depth information are avoided through this decoupling. Additionally, estimator performance is shown to rely on proper selection of feature points used for the estimation process. Parallax measurements are used to identify features that are most likely to yield accurate state estimates. The technique is then demonstrated through simulation.