2A1-O16 Autonomous running method for obtaining environment map information by using a wheeled mobile robot(Localization and Mapping)

2011 ◽  
Vol 2011 (0) ◽  
pp. _2A1-O16_1-_2A1-O16_2
Author(s):  
Yosuke FURUKAWA ◽  
Ryosuke IKEZAWA ◽  
Yusuke OSAKI ◽  
Hiroshi AN
Keyword(s):  
Mobile Robot ◽  
Wheeled Mobile Robot ◽  
Robot Localization ◽  
Environment Map ◽  
Localization And Mapping ◽  
Mobile Robot Localization

Effects of Sensory Precision on Mobile Robot Localization and Mapping

2014 ◽  
pp. 433-446 ◽  
Author(s):  
John G. Rogers ◽  
Alexander J. B. Trevor ◽  
Carlos Nieto-Granda ◽  
Alex Cunningham ◽  
Manohar Paluri ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Robot Localization ◽  
Localization And Mapping ◽  
Mobile Robot Localization

A RULE BASED DATA FUSION SCHEME FOR WHEELED MOBILE ROBOT LOCALIZATION

2006 ◽  
Vol 39 (16) ◽  
pp. 867-872
Author(s):  
Holger Blume ◽  
Frank Abelbeck ◽  
Bodo Heimann
Keyword(s):  
Data Fusion ◽  
Mobile Robot ◽  
Wheeled Mobile Robot ◽  
Robot Localization ◽  
Rule Based ◽  
Mobile Robot Localization ◽  
Fusion Scheme

Mobile Robot Localization with Recursive Bayesian Filters

2013 ◽  
pp. 203-252
Keyword(s):  
Mobile Robot ◽  
Graphical Model ◽  
Dynamic Bayesian Network ◽  
Robot Localization ◽  
Practical Applications ◽  
Recursive Filters ◽  
Localization And Mapping ◽  
The Family ◽  
Mobile Robot Localization ◽  
Probabilistic Solution

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.

1P1-H08 Research of the laying environment and rule for the mobile robot(Localization and Mapping (2))

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