Autonomous state estimation using optic flow sensing

This work addresses the problem of state estimation for an autonomous flying vehicle using an optic flow approach motivated by the visuomotor system of insects. We investigate the minimum number of optic flow sensors and the sensor configuration necessary to have an observable system. The system dynamics linearized about a point are not observable, but a nonlinear observability analysis based on Lie derivatives indicates that the sensor measurements provide enough information for estimating the states of the system. In this work, we consider planar obstacles, such as walls. The results are demonstrated in simulation.