Aiming at the problem that the MEMS-IMU bias of unmanned aerial vehicle(UAV)cannot be accurately estimated and compensated in the scenario of satellite signal rejection and vision system degradation,which results in the rapid divergence of navigation error,a bias online calibration method based on improved long short-term memory(LSTM)network is proposed.Firstly,to solve the problem of IMU bias nonlinearity and poor training effect of traditional recurrent neural network,a sequence to sequence LSTM and teacher forcing mechanism is added to improve network feature learning ability.Then,the trained network is used to calibrate MEMS-IMU bias online,and the compensated IMU measurement is optimized jointly with the visual information to ensure the navigation and positioning accuracy in the navigation process.The experimental results show that compared with the traditional LSTM method,the absolute position error of the proposed method is reduced by 6.5%in the pure inertial navigation experiment.The visual inertial integrated navigation experiments is conducted under the EUROC data set subsequence,and the absolute position error of the proposed method is reduced by 15%on average compared with the traditional LSTM method.
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