Abstract
Improvement of dead reckoning accuracy is essential for robotic localization system and has been intensively studied. However, existing solutions cannot provide accurate positioning when a robot suffers from changing dynamics such as wheel slip. In this paper, we propose an interacting multiple model (IMM) framework to detect and compensate for wheel slip. Firstly, two different types of extended Kalman filter (EKF) are designed to consider both no-slip and slip dynamics of mobile robots. Then a support vector machine (SVM) for slip estimation is constructed using real world training data. The trained model is utilized along with the two EKFs in the IMM framework. The approach is evaluated with experiments and the results show that the proposed approach improves positioning and slip compensation compared to the conventional approach.
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© 2013 Springer-Verlag Berlin Heidelberg
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Jung, J., Lee, HK., Myung, H. (2013). Slip Compensation of Mobile Robots Using SVM and IMM. In: Kim, JH., Matson, E., Myung, H., Xu, P. (eds) Robot Intelligence Technology and Applications 2012. Advances in Intelligent Systems and Computing, vol 208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37374-9_1
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DOI: https://doi.org/10.1007/978-3-642-37374-9_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37373-2
Online ISBN: 978-3-642-37374-9
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