A Procedure to make the Probabilistic Odometry Motion Model of an Autonomous Wheelchair

We present a new and completely probabilistic approach to extract the unknown motion model of an autonomous wheelchair. This method is, however, general and can be applied to any mobile robot. Particle filter based methods are very popular for localization and navigation of mobile robots. These methods use motion model for the prediction of robot states when a control action is taken. Well theoretical foundation has been established but only few applications exist. Mostly this model is assumed arbitrarily and fails during the implementation phase. We make the model using probabilistic procedure after getting the real data. The inferences are efficiently made by two table lookup commands during implementation of the particle filter. This can also be used in a hierarchical fashion. The model is validated and experimental results are shown