Vision-Based State Estimation for Uninhabited Aerial Vehicles

Abstract

This paper presents an extension of the previous work by the authors on a vision-based state estimation algorithm for uninhabited aerial vehicles (UAVs) using the implicit extended Kalman filter (IEKF) and the coplanarity (or epipolar) constraint. The coplanarity constraint, a well-known property in the Structure from Motion (SFM) field, has advantages for this application in that the feature point locations in three dimensional spaces do not have to be known and tracked and that feature points can be discarded and acquired as necessary. This reduces the computational load which is important for real time applications such as aircraft control. Advantages of the IEKF are that, in principle, the current estimate uses all previous information, not just the current observations, and that the estimate will propagate forward in an orderly fashion in the case of interrupted or reduced measurements. The dynamics of the aircraft are included in the process model which improves the observability of the states and resolves the SFM scale factor ambiguity. A disadvantage is that the estimator is specific to the particular UAV model. The algorithm was implemented in a numerical simulation and exhibited significant biases in the state estimates. The bias problems were eliminated by zeroing out the small velocity dependent terms in the measurement matrix. The modified estimator was then employed in a UAV simulation to provide feedback to a simple autopilot. The simulation demonstrated that the state estimates provided were sufficiently accurate to allow control of the simulated UAV through successful waypoint navigation.