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