IMU Dead-Reckoning Localization with RNN-IEKF Algorithm

Abstract

In complex urban environments, the Inertial Navigation System (INS) is important for navigating unmanned ground vehicles (UAVs) for its environment-independency and reliability of real-time localization. It is usually employed as the baseline in the case of other sensors failures, such as the GPS, Lidar, or Cameras. However, one problem for the INS is that its estimation error of localization accumulates over time, and thus the estimated trajectories of the UAVs continue to drift away from their ground truths. To solve this problem, this paper proposes an improved algorithm based on the Invariant Extended Kalman Filter (IEKF) for dead-reckoning of autonomous vehicles, which dynamically adjusts the process noise and the observation noise covariance matrixes through Attention mechanism and Recurrent Neural Network (RNN). The algorithm achieves more robust and accurate dead-reckoning localization in the experiments conducted on the KITTI dataset, reducing the translational error by about 45%compared to the baseline. © 2022 IEEE.