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Localization.md (1232B)

      1 # Localization
      2 
      3 **Source:** Probabilistic Robotics
      4 
      5 **Chapter:** 1
      6 
      7 **Definition:** Localization is the problem of determining the coordinates of a robot in an environment.
      8 
      9 Localization is a loop of move (lose information) and sense (gain information) steps, entered into with an initial belief.
     10 
     11 ## Approaches
     12 
     13 - [Histogram Filters](HistogramFilters.md)
     14 
     15 ## Related Ideas
     16 
     17 - [Convolution](Convolution.md)
     18     - Used during the sensing process of localization
     19 - [PriorProbability](PriorProbability.md)
     20     - Belief after movement or at the start, but before sensing
     21 - [PosteriorProbability](PosteriorProbability.md)
     22     - Belief after sensing
     23 - [Normalization](Normalization.md)
     24     - What we do to find the PMF of histogram filters after sensing and applying our convolution.
     25 - [LimitDistribution](LimitDistribution.md)
     26     - Distribution after infinite movements
     27 - [Bayes Theorem](BayesTheorem.md)
     28     - The essential idea behind the distribution calculation for the sense part of the main loop for histogram filters
     29 - [Total Probability Theorem](TotalProbabilityTheorem.md)
     30     - The probability of a robot being somewhere is the probability of each prior postition times the probability of the transition to the given position