Efficient Model-Aided Visual-Inertial Ego-Motion Estimation for Multirotor MAVs

Abstract

When deployed onboard micro air vehicles (MAVs) with limited processing power, visual ego-motion estimation solutions face an efficiency-accuracy trade-off. This paper proposes an aerodynamic-model-aided approach that emphasizes time efficiency over estimation accuracy. A linear drag force model of propellers guarantees bounded estimation errors in the velocity components orthogonal to the shafts of propellers and the attitude relative to the gravity direction. Feature point correspondences are extracted from the monocular image stream to compute the relative heading angle and translational direction, which is fused with inertial measurements by an extended Kalman filter (EKF) in a loosely coupled manner. The proposed approach shows balanced performance in accuracy and efficiency. It also has robustness to situations where vision information becomes unavailable.