Guest editorial: Special issue on characterizing mobile robot localization and mapping

2009 ◽  
Vol 27 (4) ◽  
pp. 309-311 ◽  
Author(s):  
Raj Madhavan ◽  
Chris Scrapper ◽  
Alex Kleiner
Author(s):  
John G. Rogers ◽  
Alexander J. B. Trevor ◽  
Carlos Nieto-Granda ◽  
Alex Cunningham ◽  
Manohar Paluri ◽  
...  

In this last chapter of the second section, the authors present probabilistic solutions to mobile robot localization that bring together the recursive filters introduced in chapter 4 and all the components and models already discussed in the preceding chapters. It presents the general, Bayesian framework for a probabilistic solution to localization and mapping. The problem is formally described as a graphical model (in particular a dynamic Bayesian network), and the characteristics that can be exploited to approach it efficiently are elaborated. Among parametric Bayesian estimators, the family of the Kalman filters is introduced with examples and practical applications. Then, the more modern non-parametric filters, mainly particle filters, are explained. Due to the diversity of filters available for localization, comparative tables are included.


2013 ◽  
Vol 2013 (0) ◽  
pp. _1P1-H08_1-_1P1-H08_3
Author(s):  
Satoshi ASHIZAWA ◽  
Ryunosuke IWATA ◽  
Michio YAMASHITA ◽  
Tomoya OOWAKI ◽  
Takeo OOMICHI

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