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Proceedings Paper

Current state of the art of vision based SLAM
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Paper Abstract

The ability of a robot to localise itself and simultaneously build a map of its environment (Simultaneous Localisation and Mapping or SLAM) is a fundamental characteristic required for autonomous operation of the robot. Vision Sensors are very attractive for application in SLAM because of their rich sensory output and cost effectiveness. Different issues are involved in the problem of vision based SLAM and many different approaches exist in order to solve these issues. This paper gives a classification of state-of-the-art vision based SLAM techniques in terms of (i) imaging systems used for performing SLAM which include single cameras, stereo pairs, multiple camera rigs and catadioptric sensors, (ii) features extracted from the environment in order to perform SLAM which include point features and line/edge features, (iii) initialisation of landmarks which can either be delayed or undelayed, (iv) SLAM techniques used which include Extended Kalman Filtering, Particle Filtering, biologically inspired techniques like RatSLAM, and other techniques like Local Bundle Adjustment, and (v) use of wheel odometry information. The paper also presents the implementation and analysis of stereo pair based EKF SLAM for synthetic data. Results prove the technique to work successfully in the presence of considerable amounts of sensor noise. We believe that state of the art presented in the paper can serve as a basis for future research in the area of vision based SLAM. It will permit further research in the area to be carried out in an efficient and application specific way.

Paper Details

Date Published: 2 February 2009
PDF: 12 pages
Proc. SPIE 7251, Image Processing: Machine Vision Applications II, 72510F (2 February 2009); doi: 10.1117/12.805839
Show Author Affiliations
Naveed Muhammad, Le2i, CNRS, IUT Le Creusot, Univ. de Bourgogne (France)
David Fofi, Le2i, CNRS, IUT Le Creusot, Univ. de Bourgogne (France)
Samia Ainouz, Le2i, CNRS, IUT Le Creusot, Univ. de Bourgogne (France)

Published in SPIE Proceedings Vol. 7251:
Image Processing: Machine Vision Applications II
Kurt S. Niel; David Fofi, Editor(s)

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