Aiming at the problem of poor positioning accuracy of visual inertial odometry algorithm under complex conditions such as low light or darkness,a visual inertial odometry algorithm based on multi-state constraint Kalman filter(MSCKF)is proposed to adapt to low light environment.The image is processed by combining discrete cosine transform(DCT)homomorphic filtering and adaptive image equalization with limited contrast,and then the enhanced image is applied to MSCKF to compute the accurate initial pose estimation.Finally,the feature reprojection error is calculated based on the multi-state observation constraint strategy to update the system state.The algorithm is tested on the public dataset EuRoC.The results show that the accuracy of the algo-rithm is improved compared to the original algorithm in both normal illumination and low illumi-nation scenarios.The maximum root mean square error is reduced from 1.778 m to 0.249 m,and the average root mean square error is reduced by 57.7％.
维普
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