Vehicle Localization with Global Probability Density Function for Road Navigation

This paper presents a novel approach of real-time vehicle´s localization (position and orientation) estimation. Fusion of GPS, gyroscope, speedometer and visual data is employed here to provide real time and accurate localization information. Global probability density function(PDF) is adopted to be the blending factor instead of general Kalman gain, which allows our approach to be robust and accurate for most of practical systematic problems, since the basic measurements from GPS may cause data drift or large infrequent data jumps during the fusion processing. Combining with visual data for lane shape recognition and tracking, our approach can provide as accurate as 3 to 5 meters RMS location accuracy at about 30 Hz, with less then 35 ms delay. This approach has been adapted to the direct visual navigation system in VICNAS.