scholarly journals Mobile Robot Localization and Mapping with Uncertainty using Scale-Invariant Visual Landmarks

2002 ◽  
Vol 21 (8) ◽  
pp. 735-758 ◽  
Author(s):  
Stephen Se ◽  
David Lowe ◽  
Jim Little
Keyword(s):  
Mobile Robot ◽  
Robot Localization ◽  
Scale Invariant ◽  
Visual Landmarks ◽  
Localization And Mapping ◽  
Mobile Robot Localization

Building the Panoramic Image for Mobile Robot Localization

2013 ◽  
Vol 365-366 ◽  
pp. 967-970 ◽  
Author(s):  
Vladimir Popov ◽  
Anna Gorbenko
Keyword(s):  
Mobile Robot ◽  
Robot Navigation ◽  
String Matching ◽  
Mobile Robot Navigation ◽  
Robot Localization ◽  
Panoramic Image ◽  
High Quality ◽  
Visual Landmarks ◽  
One Dimensional ◽  
Mobile Robot Localization

Visual landmarks are extensively used in contemporary robotics. There are a large number of different systems of visual landmarks. In particular, fingerprints give us unique identifiers for visually distinct locations by recovering statistically significant features. Therefore, fingerprints can be used as visual landmarks for mobile robot navigation. To create fingerprints we need one-dimensional color panoramas of high quality. In this paper, we consider a method for building the panoramic image using string matching algorithms. In particular, we propose the shortest common ordered supersequence problem.

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

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.

Sign in / Sign up

Export Citation Format

Share Document