Implementation of a kalman filter in positioning for autonomous vehicles, and its sensitivity to the process parameters

Modern autonomous vehicles are using more than one method for performing the positioning task. The most common positioning methods for indoor vehicles are odometry for relative positioning and triangulation for absolute positioning. In many cases a Kalman filter is required to merge the data from the positioning systems and determines the vehicle position based on error analysis of the measurements and calculation procedures. A Kalman filter is particularly advantageous for "onthe-fly" positioning, which is performed while the vehicle is in motion. This paper presents the implementation of a Kalman filter in "ROBI" - an AGV for material handling in a manufacturing environment. The performance of the filter in estimating the position of the AGV and the effect of motion parameters (speed, path curvature, beacon layout etc.) on filter accuracy are shown.