Fusing a hyper-ellipsoid clustering Kohonen network with the Julier-Uhlmann-Kalman filter for autonomous mobile robot map building and tracking

2002 ◽  
Author(s):  
J.A. Janet ◽  
M.W. White ◽  
M.G. Kay ◽  
J.C. Sutton ◽  
J.J. Brickley
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Map Building ◽  
Autonomous Mobile Robot ◽  
Kohonen Network

Comparative Study on Autonomous Robot Trajectory Determination in an Unknown Indoor Environment

2014 ◽  
Vol 555 ◽  
pp. 327-333
Author(s):  
Teodora Gîrbacia
Keyword(s):  
Kalman Filter ◽  
Comparative Study ◽  
Mobile Robot ◽  
Particle Filter ◽  
Measurement Errors ◽  
Indoor Environment ◽  
Autonomous Mobile Robot ◽  
Robot Trajectory ◽  
Slam Algorithm ◽  
Closed Environment

In this paper is presented a comparative study between using extended Kalman filter and particle filter applied on SLAM algorithm for an autonomous mobile robot. The robot navigates through an unknown indoor environment in which are placed 80 landmarks and it creates the map of the environment. Because the sensors placed on the robots produce measurement errors it is necessary to use Bayesian filters as the Kalman filter or the particle filter. An application was implemented that shows the estimated measurement errors produced while using both filters in order to create the estimated map of the closed environment in which the autonomous mobile robot is navigating.

Dynamic Map Building for an Autonomous Mobile Robot

1992 ◽  
Vol 11 (4) ◽  
pp. 286-298 ◽  
Author(s):  
John J. Leonard ◽  
Hugh F. Durrant-Whyte ◽  
Ingemar J. Cox
Keyword(s):  
Mobile Robot ◽  
Map Building ◽  
Autonomous Mobile Robot ◽  
Dynamic Map
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