DI-EME: Deep Inertial Ego-Motion Estimation for Autonomous Underwater Vehicle

Inertial navigation systems (INSs) are a topical solution in underwater navigation. Although appealing due to their ability to estimate pose without external information, INS suffer from compounding position errors due to bias and random noise. In general, INSs require the assistance of other positioning devices to achieve satisfactory positioning results. To solve these problems, this paper proposes an ego-motion estimation framework with an inertial measurement unit (IMUs) and magnetic compass based on the deep learning theory. The main idea is to estimate the displacement of vehicles from the IMU data in the time window and combine this with magnetic compass headings to reconstruct the trajectories of the vehicles. The pre-integration technology is used to process raw IMU data, which mathematically separates the dependence of traditional inertial algorithms based on the initial value. Then convolutional neural networks (CNN) and attention hybrid networks are used to estimate the displacement of vehicles. In addition, the framework leverages the backpropagation neural network to fuse the magnetic heading and IMU measurements to obtain an accurate heading. Compared with other deep learning methods, the proposed method reduces computational complexity and improves position accuracy. Eventually, the accuracy of the proposed method is verified in the sea trail. The results show that the maximum value of absolute trajectory errors accounts for 12.8% of the distance in severe sea conditions and 6.38% in usual sea conditions.