Multimodal sensor fusion for attitude estimation of micromechanical flying insects: A geometric approach

In this paper, we study sensor fusion for the attitude estimation of micro aerial vehicles (MAVs), in particular mechanical flying insects. First, following a geometric approach, a dynamic observer is proposed which estimates attitude based on kinematic data available from different and redundant bio-inspired sensors such as halteres, ocelli, gravitometers, magnetic compass and light polarization compass. In particular, the traditional structure of complementary filters, suitable for multiple sensor fusion, is specialized to the Lie group of rigid body rotations SO(3). Then, a numerical implementation of the filter is provided for the specific case of inertial/magnetic navigation, i.e. when gravitometers, magnetometer and gyroscopes are available. Finally, the filter performance is experimentally tested via a 3 degrees-of-freedom robotic flapper and a custom-made set of inertial/magnetic sensors. Experimental results show good agreement, upon proper tuning of the filter, between the actual kinematics of the robotic flapper and the kinematics reconstructed from the inertial/magnetic sensors via the proposed filter.