Fusion of GPS, Compass, and Camera for Localization of an Intelligent Vehicle

Abstract

This paper proposes a fuzzy based sensor fusion algorithm for localization of an intelligent vehicle by correcting translational error of latitude and longitude in easily available maps such as Google Earth. Even though the chosen map possesses translational error, the proposed algorithm will automatically correct and compensate this error by using the integration of a Global Positioning System (GPS), a magnetic compass, and a CCD camera. To integrate these sensors, all the sensing data must be converted to the same reference. The GPS and the magnetic compass provide global data, whereas the camera provides local data. Since all sensors contain some uncertainties, the outputs of all sensors can be expressed by fuzzy numbers with triangular membership functions. All the fuzzy numbers are operated based on the alpha-cut closed interval properties. The proposed algorithm uses information from the GPS and the magnetic compass to calculate the global position of two selected pixels in two different segments, which represent road center line in the road images. Consequently, by knowing the global positions of the selected pixels, it is possible to calculate the horizontal and vertical deviations that the waypoints in the original map are to shift. The system performs efficiently on an unmarked road inside Asian Institute of Technology Thailand (AIT) campus.