A New Combined Vision Technique for Micro Aerial Vehicle Pose Estimation

Abstract

In this work, a new combined vision technique (CVT) is proposed, comprehensively developed, and experimentally tested for stable, precise unmanned micro aerial vehicle (MAV) pose estimation. The CVT combines two measurement methods (multi- and mono-view) based on different constraint conditions. These constraints are considered simultaneously by the particle filter framework to improve the accuracy of visual positioning. The framework, which is driven by an onboard inertial module, takes the positioning results from the visual system as measurements and updates the vehicle state. Moreover, experimental testing and data analysis have been carried out to verify the proposed algorithm, including multi-camera configuration, design and assembly of MAV systems, and the marker detection and matching between different views. Our results indicated that the combined vision technique is very attractive for high-performance MAV pose estimation.