Direct Visual-Inertial Ego-Motion Estimation via Iterated Extended Kalman Filter
This improves reactive navigation for drones by enhancing speed and robustness, though it is incremental as it builds on existing filtering methods with a direct visual approach.
The paper tackles ego-motion estimation for Micro Aerial Vehicles by directly fusing IMU and monocular visual feedback using an Iterated Extended Kalman Filter, achieving comparable accuracy to state-of-the-art systems while being 15-30 times faster.
This letter proposes a reactive navigation strategy for recovering the altitude, translational velocity and orientation of Micro Aerial Vehicles. The main contribution lies in the direct and tight fusion of Inertial Measurement Unit (IMU) measurements with monocular feedback under an assumption of a single planar scene. An Iterated Extended Kalman Filter (IEKF) scheme is employed. The state prediction makes use of IMU readings while the state update relies directly on photometric feedback as measurements. Unlike feature-based methods, the photometric difference for the innovation term renders an inherent and robust data association process in a single step. The proposed approach is validated using real-world datasets. The results show that the proposed method offers better robustness, accuracy, and efficiency than a feature-based approach. Further investigation suggests that the accuracy of the flight velocity estimates from the proposed approach is comparable to those of two state-of-the-art Visual Inertial Systems (VINS) while the proposed framework is $\approx15-30$ times faster thanks to the omission of reconstruction and mapping.