Abstract
Mobile robots must have some effective method for determining their position in the working environment in order to operate appropriately. In this paper, precise outdoor positioning of a vehicle is achieved by continuously fusing odometry with wide-area Differential Global Positioning System (DGPS) data through a Kalman filter. The propagation and reduction of spatial uncertainty is computed together with the mobile robot's coordinates and heading. Two distinct sources of differential corrections have been applied to autonomous and teleoperated navigation of the Aurora mobile robot in a road network. Path teaching and path tracking have been successfully tested in several experiments over a relatively flat surface.