Improved State Propagation through AI-based Pre-processing and Down-sampling of High-Speed Inertial Data

We present a novel approach to improve 6 degree-of-freedom state propagation for unmanned aerial vehicles in a classical filter through pre-processing of high-speed inertial data with AI algorithms. We evaluate both an LSTM-based approach as well as a Transformer encoder architecture. Both algorithms take as input short sequences of fixed length N of high-rate inertial data provided by an inertial measurement unit (IMU) and are trained to predict in turn one pre-processed IMU sample that minimizes the state propagation error of a classical filter across M sequences. This setup allows us to provide sufficient temporal history to the networks for good performance while maintaining a high propagation rate of pre-processed IMU samples important for later deployment on real-world systems. In addition, our network architectures are formulated to directly accept input data at variable rates thus minimizing necessary data preprocessing. The results indicate that the LSTM based architecture outperforms the Transformer encoder architecture and significantly improves the propagation error even for long IMU propagation times.