Notice
2.7. Grid Localization: an example in 1D
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Descriptif
Now that we have theequations of the Bayes filter, we need a method in order to implementin real cases these equations.
So, in the following, I want todiscuss two methods, which are commonly adopted by the MobileRobotics Community and, these, if you want, correspond to twoextreme solutions because one is a fully numerical and itis based on a grid and, for the case of localization, isknown as the grid-localization approach – and theother one is a fully analytical and it isknown as a the Kalman filter.
So, now, in this video, wediscuss the first method, which is numerical, and we do thisby referring to a simple case – a trivial case, aone-dimensional case – and this will allow not only to understand thesemethods – how it works – but also to better understandthe behavior of the Bayes filter.
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MartinelliAgostinoIn this video, we want to discuss the case of a wheel encoders in 2D, and in particular the case of a robot equipped with a differential drive which is very popular in mobile robotics.
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