Nonlinear attitude estimation using active vision and inertial measurements

In this paper we consider the problem of estimating the attitude of a rigid body equipped with a triad of rate gyros and a pan and tilt camera. The nonlinear attitude observer integrates angular velocity measurements from rate gyros, with images of a planar scene provided by the camera. By exploiting directly sensor information, i) a stabilizing feedback law is introduced and exponential convergence to the origin of the estimation errors is shown; ii) an active vision system is proposed that relies on an image-based exponentially input-to-state stable (ISS) control law for the camera pan and tilt angular rates to keep the features in the image plane. The discrete time implementation of the observer makes use of recent results in geometric numeric integration to preserve the rotation matrix properties. Simulated and experimental results demonstrate the effectiveness and applicability of the proposed solution.