An information-theoretic approach to autonomous navigation and guidance of an uninhabited aerial vehicle in unknown environments

This paper presents an intelligent, on-line guidance scheme for maximising vehicle navigation and snap information for an uninhabited aerial vehicle while operating over unknown terrain. The tasks of localisation and mapping are performed concurrently using the well known extended Kalman filter implementation of the simultaneous localisation and mapping (SLAM) algorithm. A guidance scheme is formulated using vehicle actions in order to maximise the quality of the resulting vehicle pose estimates and environment map by maximising the entropic information in vehicle and map estimates. Results are presented using a high-fidelity six-degree of freedom simulation of an aerial vehicle using inertial navigation and a range/hearing sensor.