Using Temporal Consistency to Improve Robot Localisation
Symbolic reasoning has rarely been applied to filter sensor information; and for data fusion, probabilistic models are favoured over reasoning with logic models. However, we show that in the fast dynamic environment of robotic soccer, Plausible Logic can be used effectively to deploy non-monotonic reasoning. We show this is also possible within the frame rate of vision in the (not so powerful) hardware of the AIBO ERS-7 used in the legged league. The non-monotonic reasoning with Plausible Logic not only has algorithmic completion guarantees but we show that it effectively filters the visual input for improved robot localisation. Moreover, we show that reasoning using Plausible Logic is not restricted to the traditional value domain of discerning about objects in one frame. We present a model to draw conclusions over consecutive frames and illustrate that adding temporal rules can further enhance the reliability of localisation.
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