TY - GEN
T1 - Vision-Based Global Positioning System Using Improved GMS Algorithm for a UAV
AU - Xin, Long
AU - He, Xinhua
AU - Cui, Xin
AU - Liu, Ziyu
N1 - Publisher Copyright:
© 2023, Beijing HIWING Sci. and Tech. Info Inst.
PY - 2023
Y1 - 2023
N2 - Fast and accurate localization for unmanned aerial vehicle (UAV) navigation could avoid hazards when GPS is unavailable. A vision-based global positioning system is developed for UAV by means of satellite images from Google Map as reference. In this system, an ultra-robust and fast feature correspondence algorithm, called Grid-based motion statistics (GMS), is utilized for scene matching. GMS’s performance is comparable to the techniques many orders of magnitude slower, and it maintains high speed as the fast algorithms. A Least Median Square improved GMS algorithm (LMedS-GMS) is developed which is employed to solve the matching failure of the original GMS in challenging scenarios. Moreover, a robust filtering localization approach combining the random sample consensus algorithm (RANSAC) and the proposed LMedS-GMS is designed for locating the position of an UAV. Finally, a vision-based global positioning architecture is proposed using this method with the altitude and direction information from the airborne sensors. Experimental results based on offline data demonstrate that the proposed algorithm is superior to state-of-the-art methods in the accuracy and real-time performance.
AB - Fast and accurate localization for unmanned aerial vehicle (UAV) navigation could avoid hazards when GPS is unavailable. A vision-based global positioning system is developed for UAV by means of satellite images from Google Map as reference. In this system, an ultra-robust and fast feature correspondence algorithm, called Grid-based motion statistics (GMS), is utilized for scene matching. GMS’s performance is comparable to the techniques many orders of magnitude slower, and it maintains high speed as the fast algorithms. A Least Median Square improved GMS algorithm (LMedS-GMS) is developed which is employed to solve the matching failure of the original GMS in challenging scenarios. Moreover, a robust filtering localization approach combining the random sample consensus algorithm (RANSAC) and the proposed LMedS-GMS is designed for locating the position of an UAV. Finally, a vision-based global positioning architecture is proposed using this method with the altitude and direction information from the airborne sensors. Experimental results based on offline data demonstrate that the proposed algorithm is superior to state-of-the-art methods in the accuracy and real-time performance.
KW - Grid-based motion statistics (GMS)
KW - Unmanned aerial vehicles (UAVs)
KW - Vision-based positioning
UR - https://www.scopus.com/pages/publications/85151063853
U2 - 10.1007/978-981-99-0479-2_89
DO - 10.1007/978-981-99-0479-2_89
M3 - 会议稿件
AN - SCOPUS:85151063853
SN - 9789819904785
T3 - Lecture Notes in Electrical Engineering
SP - 981
EP - 993
BT - Proceedings of 2022 International Conference on Autonomous Unmanned Systems, ICAUS 2022
A2 - Fu, Wenxing
A2 - Gu, Mancang
A2 - Niu, Yifeng
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Autonomous Unmanned Systems, ICAUS 2022
Y2 - 23 September 2022 through 25 September 2022
ER -