Analysis of Three Different Kalman Filter Implementations for Agricultural Vehicle Positioning

Abstract

Conventional positioning techniques based on GPS receivers are not accurate enough to be used with autonomous guidance systems. High accuracy GPS receivers can be employed, but the cost of the system would be very high. The alternative solution presented in this article is to combine the data provided by different positioning sensors using a Kalman filter. The described procedure also uses an odometric estimation of the mobile position, based on the kinematic model of the agricultural vehicle. Three different implementations of the Kalman filter are described, using different sensor combinations but based on the same vehicle model.