UAV Visual-Inertial Dynamics (VI-D) Odometry using Unscented Kalman Filter

Different approaches to Visual-Inertial odometry(VIO) has been presented in literature. However, few research works exploits the quadrotor translational and rotational dynamics and the known thrust and torque inputs. Additional information from the dynamics with known control inputs improves the state estimation. This paper is focused on the estimation of the quadrotor UAV 6 DOF pose by fusing the information from the dynamics of the quadrotor coupled with visual-inertial measurements using an Unscented Kalman Filter. The thrust and torque inputs drives the prediction model while the VIO system provides 6 DOF pose, velocity and unbiased angular velocity of the vehicle. The results shows that our approach improves the state estimates by about 2-5% in translation and 37% in rotation.