A new approach using fractal based vision is presented to solve the problem of mobile robot navigation in outdoor environments. Mobile robots rely on landmarks such as mile markers and street signs for information on global position and local traffic conditions. Due to the motion of the robot, the location, size and orientation of the landmarks are varying. Also, other objects in the scene might partially occlude the landmark. Thus, a robust recognition system is required to recognize the landmarks that may be distorted by a combination of these effects. A new fractal model called incremental fractional Brownian motion (BM) model, is developed to locate these landmarks. A new neural network architecture, reconfigurable neural network (RNN), is developed to recognize the landmarks. The fractal model is shown to be invariant to changes in intensity of incident light. The landmark candidate regions detected by the ifBM model are analyzed by the RNN. New learning rules based on update normalization are developed to decrease learning time and increase system stability. The network also has the ability to learn new patterns with minimal retraining time. The network is tested with images of actual street signs that were distorted by scale changes, rotations and occlusions.
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