Unsupervised monocular depth estimation algorithm integrated with IMU

In order to solve the problems of unsupervised monocular depth estimation algorithm output scale blur, inconsistent depth estimation scale between frames, poor depth estimation accuracy under strong light and simple-texture scenes, this article combines the Extended Kalman Filter (EKF) with IMU attitude information, which can provide information about real scale constraints for the training phase of the monocular depth estimation network. The minimization of the IMU photometric loss function and the cross-sensor photometric consistency loss function are introduced to solve the scale ambiguity problem of the unsupervised monocular depth estimation algorithm and ensures that the scale of depth estimates between frames is consistent. Based on the IMU's insensitivity to light, the algorithm also improves the accuracy of depth estimation in outdoor strong light scenes.