Decentralized Geolocation and Bias Estimation for Uninhabited Aerial Vehicles with Articulating Cameras

Abstract

The cooperative geolocation of a point of interest using multiple uninhabited aerial vehicles with articulating camera sensors is addressed, where there are non-zero-mean errors (biases) in the estimate of the uninhabited aerial vehicle state. The proposed approach is to use the onboard navigation solution in the estimator and, further, to consider biases across all uninhabited aerial vehicles and to jointly estimate both the biases and the unknown point-of-interest location. Furthermore, a decentralized solution is presented that uses marginalization of the biases, thus allowing the uninhabited aerial vehicles to share only information about the point of interest and model only their local biases. This decentralized approach saves significant computation and scales well with the number of uninhabited aerial vehicles. Real flight-test data and hardware-in-the-loop simulations are used to demonstrate the improvement in geolocation with bias estimation, as well as the effectiveness of the new decentralized point of interest and bias estimation algorithm, for both stationary and moving points of interest.