A Vision-inertial Autonomous Localization Method for UAVs with Satellite Assistance
Autonomous localization of UAVs in complex environments is an important prerequisite for the intelligence and application of UAVs,but it also faces various challenges,such as sensor noise,dynamic initialization.To solve these problems,a satellite assisted vision-inertial autonomous localization method for UAVs is proposed,which realizes the real-time estimation of the UAVs' position,velocity,attitude and bias and other states.A closed-form solution model based on IMU pre-integration is adopted,which achieves dynamic initialization of the visual-inertial navigation system(VINS),effec-tively reducing the computation and initialization time.The GPS-IMU time offset is modeled,and online calibration of the external time offset is performed,which solve the time delay of the global pose measurement information,and realize the asynchronous update of the global pose information.Outdoor flight experiments are conducted on a monocular inertial navi-gation sensor and a quadcopter UAV experimental platform,which verify the effectiveness and robustness of the proposed method.The experiment results show that the positioning accuracy of the proposed method in complex environments with in-termittent GPS signals reaches 2.2 m,significantly better than the traditional VINS method of 4.8 m,reflecting the effi-ciency and robustness of the proposed method.It provides an innovative solution for the autonomous localization of UAVs in complex environments,and opens up new fields for the intelligence and application of UAVs.
UAV state estimationvisual-inertial navigation system(VINS)self-localization in unknown environmentsmulti-source heterogeneous sensors
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