Landmark Recognition for Autonomous Navigation Using Odometric Information and a Network of Perceptrons

In this paper two methods for the detection and recognition of landmarks to be used in topological modeling for autonomous mobile robots are presented. The first method is based on odometric information and the distance between the estimated position of the robot and the already existing landmarks. Due to significant errors arising in the robot's position measurements, the distance-based recognition method performs quite poorly. For such reason a much more robust method, which is based on a neural network formed by perceptrons as the basic neural unit is proposed. Apart from performing very satisfactorily in the detection and recognition of landmarks, the simplicity of the selected ANN architecture makes its implementation very attractive from the computational standpoint and guarantees its application to real-time autonomous navigation.