An indoor positioning method of unmanned aerial vehicle (UAV) based on improved multi-state constraint Kalman filter (MSCKF) was proposed aiming at the problem that the indoor positioning of UAV is prone to drift. A high robustness and low delay detection method was proposed under the framework of MSCKF. The pose of UAV was calculated with the help of the known positions of the mark points in world coordinate system. Then inertial measurement unit (IMU) data and monocular vision data fusion and UAV pose correction were realized. The proposed positioning method was tested. The simulation results show that the positioning error of the proposed method was within 0.266 m, and the positioning accuracy was improved by more than 54.6% compared to OpenVins and LARVIO.
Si-peng WANG,Chang-ping DU,Guang-hua SONG,Yao ZHENG. Indoor positioning method of UAV based on improved MSCKF algorithm. Journal of ZheJiang University (Engineering Science), 2022, 56(4): 711-717.
Fig.3Performance test environment of positioning algorithm
Fig.4Positioning results of trajectory 1 and trajectory 2
Fig.5Error comparision of trajectory 1
算法
误差/m
轨迹1
轨迹2
本文算法
0.226
0.266
OpenVins
0.498
0.644
LARVIO
0.575
—
Tab.1Positioning error of three algorithms for two trajectories
Fig.6Error comparision of trajectory 2
算法
td/ms
本文算法
7.92
OpenVins
2.67
文献[13]算法
>22.22
Tab.2Real-time analysis of positioning algorithms
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