Research Article
A Multi-source Fused Location Estimation Method for UAV Based on Machine Vision and Strapdown Inertial Navigation
@INPROCEEDINGS{10.1007/978-3-030-69066-3_24, author={Jiapeng Li and Shuo Shi and Xuemai Gu}, title={A Multi-source Fused Location Estimation Method for UAV Based on Machine Vision and Strapdown Inertial Navigation}, proceedings={Artificial Intelligence for Communications and Networks. Second EAI International Conference, AICON 2020, Virtual Event, December 19-20, 2020, Proceedings}, proceedings_a={AICON}, year={2021}, month={7}, keywords={Unmanned aerial vehicle Strapdown inertial navigation system Multi-Source information fusion}, doi={10.1007/978-3-030-69066-3_24} }- Jiapeng Li
Shuo Shi
Xuemai Gu
Year: 2021
A Multi-source Fused Location Estimation Method for UAV Based on Machine Vision and Strapdown Inertial Navigation
AICON
Springer
DOI: 10.1007/978-3-030-69066-3_24
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.