Abstract
In recent years, unmanned aerial vehicle (UAV) technology has been widely used in industry, agriculture, military and other fields, and its positioning problem has been a research hotspot in this field. To solve the problem of invalidation of integrated navigation of global positioning system (GPS) and strapdown inertial navigation system (SINS) in indoor and other areas, this paper presents a multi-source information fusion location algorithm based on machine vision positioning and SINS. Based on image coordinate system (ICS), body coordinate system (BCS) and navigation coordinate system (NCS), combined with AprilTags recognition and positioning technology, this paper builds NCS with AprilTags array to get the position observation of UAV. Based on the idea of multi-source information fusion, this paper applied third-order fused complementary filter algorithm, which combines with the SINS to obtain accurate three-axis speed and position estimation. Finally, the reliability is verified by the test of the UAV experimental platform.
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Li, J., Shi, S., Gu, X. (2021). A Multi-source Fused Location Estimation Method for UAV Based on Machine Vision and Strapdown Inertial Navigation. In: Shi, S., Ye, L., Zhang, Y. (eds) Artificial Intelligence for Communications and Networks. AICON 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 356. Springer, Cham. https://doi.org/10.1007/978-3-030-69066-3_24
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DOI: https://doi.org/10.1007/978-3-030-69066-3_24
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