GPS-INS Integration Algorithm for Autonomous Vehicle Using Loosely Coupled Integration Architecture

Abstract:

Inertial measurement unit (IMU) has been widely used for autonomous vehicles navigation. The accuracy of IMU specifies the performance of the inertial navigation system (INS).The errors in the INS are mainly due to the IMU inaccuracies, initial alignment, computational errors and approximations in the system equations. These errors are further integrated over time due to the dead-reckoning nature of the INS, which leads to unacceptable results. These errors need an accurate estimation for high precision navigation. INS is integrated with Global Positioning System (GPS) to estimate the errors and enhance the navigation capability of the INS. Linearized Kalman Filter (LKF) is proposed for estimating the errors in the low cost INS using Loosely Coupled integration approach, which is opted for its simplicity and robustness. Prediction part of the LKF is used during the GPS lag for errors estimation, which is found very effective for low cost sensors. The resulting GPS-INS integration algorithm is evaluated on simulated Autonomous vehicle trajectory, generated from 6-DOF model. The integrated system limits the attitude errors less than 0.1 deg and velocity errors of the order of 0.003 meter per second. Furthermore, it provides an optimal navigation solution than can be achieved from individual systems.

• Periodical: Applied Mechanics and Materials (Volume 390)
• Pages: 506-511
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.390.506
• Online since: August 2013
• Authors: Rashid Iqbal, Zhong Jian Li, Khan Badshah
• Keywords: Autonomous Vehicles, GPS, Inertial Navigation System (INS), Kalman Filtering, Loosely Coupled Approach

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