Artificial vision can be used in many environments such as indoor, outdoor, space, and even in underwater contexts. Most of times, vision based localization requires complex algorithms and hardware resources when related to general environment features. However, the use of simple landmarks can reduce dramatically the cost and the complexity of the recognition system. In typical indoor environments, in particular in offices, ceiling lamps are all of the same type and are placed in a quite regular way. Moreover, they can be easily seen, as generally no obstacles can exist between them and the robot vision system. These peculiarities motivated a study on the possibility of implementing a very low cost localization procedure using a standard onboard webcam. A Kalman filtering approach has been used to fuse vision and odometric data for position estimation, and a navigation architecture, based on a real-time Linux kernel, has been set up to show its reliability and flexibility.