Paper Info
Title
Monte carlo localization using SIFT features
Abstract
The ability of finding its situation in a given environment is crucial for an autonomous agent. While navigating through a space, a mobile robot must be capable of finding its location in a map of the environment (i.e. its pose x, y, θ), otherwise, the robot will not be able to complete its task. This problem becomes specially challenging if the robot does not possess any external measure of its global position. Typically, dead-reckoning systems do fail in the estimation of robot's pose when working for long periods of time. In this paper we present a localization method based on the Monte Carlo algorithm. During the last decade this method has been extensively tested in the field of mobile Robotics, proving to be both robust and efficient. On the other hand, our approach takes advantage from the use of a vision sensor. In particular, we have chosen to use SIFT features as visual landmarks finding them suitable for the global localization of a mobile robot. We have succesfully tested our approach in a B21r mobile robot, achieving to globally localize the robot in few iterations. The technique is suitable for office-like environments and behaves correctly in the presence of people and moving objects.
Year
DOI
Venue
2005
10.1007/11492429_75
IbPRIA (1)
Keywords
Field
DocType
office-like environment,global localization,monte carlo algorithm,global position,mobile robot,monte carlo localization,sift feature,mobile robotics,autonomous agent,localization method,b21r mobile robot,dead reckoning
Computer vision,Computer science,Mobile agent,Dead reckoning,Artificial intelligence,Autonomous system (mathematics),Mobile robot navigation,Monte Carlo localization,Robot,Robotics,Mobile robot
Conference
Volume
ISSN
ISBN
3522
0302-9743
3-540-26153-2
Citations 
PageRank 
References 
7
0.52
9
Authors
5
Name
Order
Citations
PageRank
Arturo Gil126321.99
Óscar Reinoso218632.13
Asunción Vicente370.52
C. Fernández4358.76
Luis Payá59421.07