A sensor fusion approach for localization with cumulative error elimination

Abstract

This paper describes a robust approach which improves the precision of vehicle localization in complex urban environments by fusing data from GPS, gyroscope and velocity sensors. In this method, we apply Kalman filter to estimate the position of the vehicle. Compared with other fusion based localization approaches, we process the data in a public coordinate system, called Earth Centred Earth Fixed (ECEF) coordinates and eliminate the cumulative error by its statistics characteristics. The contribution is that it not only provides a sensor fusion framework to estimate the position of the vehicle, but also gives a mathematical solution to eliminate the cumulative error stems from the relative pose measurements (provided by the gyroscope and velocity sensors). The experiments exhibit the reliability and the feasibility of our approach in large scale environment.