This paper describes the theoretical development and experimental implementation of a complete navigation procedure for use in an autonomous mobile robot for structured environments. Estimates of the vehicle’s position and orientation are based on the rapid observation of visual cues located at discrete positions within the environment. The extended Kalman filter is used to combine these visual observations with sensed wheel rotations to produce optimal estimates continuously. The complete estimation procedure, as well as the control algorithm, has been developed to be time independent. Rather than time, a naturally suitable quantity involving wheel rotations is used as the independent variable. One consequence of this choice is that the vehicle speed can be specified independently of the estimation and control algorithms. Reference paths are "taught" by manually leading the vehicle through the desired path in a manner similar to the teaching of industrial holonomic robots. Estimates produced by the extended Kalman filter during this teaching session are then used to represent the geometry of the path. The tracking of taught reference paths is accomplished by controlling the position and orientation of the vehicle relative to the reference path. Position estimates have been determined to be accurate to within one inch. Also, the reference paths are tracked precisely such that the position error typically does not exceed one inch. Time-independence path tracking has necessitated the development of a novel, geometry-based means for advancing along the reference path. Also novel is a means to accommodate the finite time interval which separates the instant at which images are acquired from the instant as which the processed image information becomes available to update the estimates.