The Possibilistic Filter: An Alternative Approach for State Estimation
| Authors: | Matia Fernando, Universidad Politecnica de Madrid, Spain
Jimenez Agustin, Universidad Politecnica de Madrid, Spain
Rodriguez-Losada Diego, Universidad Politecnica de Madrid, Spain
Galan Ramon, Universidad Politecnica de Madrid, Spain
Al-Hadithi Basil M., Universidad Alfonso X El Sabio, Spain |
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| Topic: | 4.3 Robotics |
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| Session: | Positioning and Estimation |
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| Keywords: | State estimation, Kalman filter, Fuzzy logic, Possibility theory, Mobile robotics |
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Abstract
A new method to implement fuzzy Kalman filters is introduced in this paper. This has special application in fields where inaccurate models or sensors are involved, such as in mobile robotics. The innovation consists in using possibility distributions, instead of gaussian distributions. The main advantage of this approach is that uncertainty is not needed to be symmetric, while a region of possible solutions is allowed. The contribution of this work also includes a method to propagate uncertainty through both the process and the observation models. This one is based on quantifying uncertainty as trapezoidal possibility distributions. Finally, an example of a mobile robot during a localization process using landmarks is shown.
