Attitude Estimation of a Biologically Inspired Robotic Housefly via Multimodal Sensor Fusion

In this paper, we address sensor fusion for the attitude estimation of micromechanical aerial vehicles (MAVs), in particular a biologically inspired robotic housefly. First, a dynamic observer is proposed that estimates attitude based on kinematic data available from different and redundant bio-inspired sensors such as halteres, ocelli, gravitometers, magnetic compasses and light polarization compasses. In particular, following a geometric approach, the traditional structure of complementary filters, suitable for multiple sensor fusion, is specialized to the Lie group of rigid-body rotations SO(3) and almost-global asymptotic stability is proved. Then, the filter performance is experimentally tested via a 3-d.o.f. 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.