by Bais, A, Sablatnig, R, Gu, J, Khawaja, YM, Usman, M, Hasan, GM and Iqbal, MT
Abstract:
This paper presents vision based self-localization of tiny autonomous mobile robots in a known but highly dynamic environment. The problem covers tracking the robot position with an initial estimate to global self-localization. The algorithm enables the robot to find its initial position and to verify its location during every movement. The global position of the robot is estimated using trilateration based techniques whenever distinct landmark features are extracted. Distance measurements are used as they require fewer landmarks compared to methods using angle measurements. However, the minimum required features for global position estimation are not available throughout the entire state space. Therefore, the robot position is tracked once a global position estimate is available. Extended Kalman filter is used to fuse information from multiple heterogeneous sensors. Simulation results show that the new method that combines the global position estimation with tracking results in significant performance gain. © 2008 Springer-Verlag Berlin Heidelberg.
Reference:
Stereo vision based self-localization of autonomous mobile robots (Bais, A, Sablatnig, R, Gu, J, Khawaja, YM, Usman, M, Hasan, GM and Iqbal, MT), In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 4931 LNCS, 2008.
Bibtex Entry:
@inproceedings{bais2008stereorobots,
author = "Bais, A and Sablatnig, R and Gu, J and Khawaja, YM and Usman, M and Hasan, GM and Iqbal, MT",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "367--380",
title = "Stereo vision based self-localization of autonomous mobile robots",
volume = "4931 LNCS",
year = "2008",
abstract = "This paper presents vision based self-localization of tiny autonomous mobile robots in a known but highly dynamic environment. The problem covers tracking the robot position with an initial estimate to global self-localization. The algorithm enables the robot to find its initial position and to verify its location during every movement. The global position of the robot is estimated using trilateration based techniques whenever distinct landmark features are extracted. Distance measurements are used as they require fewer landmarks compared to methods using angle measurements. However, the minimum required features for global position estimation are not available throughout the entire state space. Therefore, the robot position is tracked once a global position estimate is available. Extended Kalman filter is used to fuse information from multiple heterogeneous sensors. Simulation results show that the new method that combines the global position estimation with tracking results in significant performance gain. © 2008 Springer-Verlag Berlin Heidelberg.",
doi = "10.1007/978-3-540-78157-8_28",
isbn = "3540781560",
isbn = "9783540781561",
issn = "0302-9743",
eissn = "1611-3349",
keyword = "Autonomous robots",
keyword = "Kalman filter",
keyword = "Self-localization",
keyword = "Soccer robots",
keyword = "Stereo vision",
language = "eng",
}