Paper Info
Title
A Qualitative-Probabilistic Approach to Autonomous Mobile Robot Self Localisation and Self Vision Calibration.
Abstract
Typically, the spatial features of a robot's environment are specified using metric coordinates, and well-known mobile robot localisation techniques are used to track the exact robot position. In this paper, a qualitative-probabilistic approach is proposed to address the problem of mobile robot localisation. This approach combines a recently proposed logic theory called Perceptual Qualitative Reasoning about Shadows (PQRS) with a Bayesian filter. The approach herein proposed was systematically evaluated through experiments using a mobile robot in a real environment, where the sequential prediction and measurement steps of the Bayesian filter are used to both self-localisation and self-calibration of the robot's vision system from the observation of object's and their shadows. The results demonstrate that the qualitative-probabilistic approach effectively improves the accuracy of robot localisation, keeping the vision system well calibrated so that shadows can be properly detected.
Year
DOI
Venue
2013
10.1109/BRACIS.2013.34
BRACIS
Keywords
Field
DocType
self vision calibration,bayesian filter,mobile robot localisation,well-known mobile robot localisation,vision system,robot localisation,mobile robot,autonomous mobile robot self,qualitative-probabilistic approach,real environment,approach herein,exact robot position
Computer science,Artificial intelligence,Probabilistic logic,Bayesian filtering,Calibration,Mobile robot,Machine learning
Conference
Citations 
PageRank 
References 
1
0.38
0
Authors
7
Name
Order
Citations
PageRank
Valquiria Fenelon Pereira110.38
Fabio G. Cozman21200172.21
Paulo E. Santos313120.29
Murilo Fernandes Martins4192.48
Fenelon Pereira, V.510.38
Gagliardi Cozman, F.610.38
Fernandes Martins, M.710.38