Position estimation of autonomous guided vehicle - Goh, Ching Tard. (2008)
This thesis presents the development and application of a sensor fusion algorithm in positioning. The theoretical background behind the algorithm is based on the extended Kalman filter. By merging information from different sensors such as the Differential Global Positioning System (DGPS), rate gyroscope and odometers, the filter is able to predict optimally the position and orientation of a 2-wheel steerable vehicle. In the filter, an enhanced kinematic process or vehicle model that accounts for the side slips experienced at the vehicle wheels is employed. These slip parameters that conform to the angles between the actual translated and pointed directions of the vehicle tires can affect the accuracy and consistency of the estimation system. Comparison between the enhanced model and another (without the slip consideration and based on pure kinematics) indicates improvements in the estimations as well as the orientation rate innovations with the slip compensation.