Indoor Vehicular Navigation using IMU and LiDAR with EKF Parameters Optimization using Grey Wolf Algorithm

Nowadays, vehicles must be able to localize themselves in all environments, including urban areas and indoor environments where the performance of Global Navigation Satellite Systems (GNSS) may be reduced. In the studies conducted so far in urban environments, an inertial measurement unit (IMU) and an extended Kalman filter (EKF) have been presented. For indoor environments, a light and range detection system (LiDAR) has been developed, which was more accurate than previous methods. However, the accuracy of diagnosis should be improved by providing newer methods. Therefore, in this article, in order to increase the accuracy of the position error of the internal navigation system, an integrated developed IMU/LiDAR Kalman filter was used, and then by optimizing the parameters of the Kalman filter and the gray wolf meta-heuristic algorithm, an attempt was made to reduce the position error. In order to check the performance of the presented method, the results before using the optimization algorithm and after the optimization were evaluated using three different paths. The obtained results, including position, speed, and direction show that the accuracy of the results increases by using the gray wolf algorithm compared to the conventional model.