Abstract
This research is a preliminary research for real time autonomous robot in unknown incremental dynamical environment. A general method for mapping incremental dynamic environment using Multiple Target tracking (MTT) was proposed in this research. Rao-Blackwellized Particle Filter (RBPF) was used for the multiple moving obstacles tracking problem. Firstly data association problem was solved via Multiple Hypothesis Tracking (MHT) data association by a new method. The new MHT method can use extra information except using only position of targets. Particle Filter is used in the method. Each particle is assumed as an obstacle map. Then tracking problem for each obstacle in the particle is solved by Extended Kalman Filter (EKF). Finally the particle which has highest weight is assumed as the dynamic map. Additionally a new resampling method was proposed in this research. The algorithm can cope with new obstacles and false detection according to the pure particle filter. Obstacles are assumed as human in this research hence their velocities are determined randomly up to human walking speed. Furthermore the robot moves approximately at human walking speed. A graphical user interface program was constituted in MATLAB so different states are surveyed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Wang, C.: Simultaneous localization, mapping and moving object tracking. PhD Thesis, Robotics Institute, Carnegie Mellon University, CMU-RI-TR-04-23 (April 2004); Tapia, Intille, S. Larson, K.: Activity recognition in the home using simple and ubiquitous sensors. Lecture Notes in Computer Science, Int. Conf. on Pervasive Computing, vol. 3001, pp. 158–175
Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. The MIT Press, Cambridge (2005)
Reid, D.B.: An algorithm for tracking multiple targets. IEEE Trans. on Automatic Control 24(6) (December 1979)
Cox, I.J., Hingorani, S.L.: An efficient implemenation of Reid’s multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking. IEEE Trans. on Pattern Analysis and Machine intelligence 18(2) (February 1996)
Schulz, D., Burgard, W., Fox, D., Cremers, A.B.: Tracking Multiple Moving Targets with a Mobile Robot using Particle Filters and Statistical Data Association. In: Proc. of the 2001 IEEE International Conference on Robotics & Automation (ICRA 2001), Seoul, Korea (2001)
Fortmann, T.E., Bar-Shalom, Y., Scheffe, M.: Sonar tracking of multiple targets using joint probabilistic data association. IEEE Journal of Oceanic Engineering OE-8, 173–184 (1983)
Sarkka, S., Vehtari, A., Lampinen, J.: Rao-blackwellizedmontecarlo data association for multiple target tracking. In: Svensson, P., Schubert, J. (eds.) Proceedings of the Seventh International Conference on Information Fusion, Mountain View, CA, vol. 1, pp. 583–590 (2004)
Gordon, N., Clapp, T.: A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking, M. SanjeevArulampalam, Simon Maskell. IEEE Transactions on Signal Processing 50(2) (February 2002)
Miller, I., Campbell, M.: Rao-Blackwellized Particle Filtering for Mapping Dynamic Environments. In: 2007 IEEE International Conference on Robotics and Automation, Roma, Italy, April 10-14 (2007)
Browning, R.C., Baker, E.A., Herron, J.A., Kram, R.: Effects of obesity and sex on the energetic cost and preferred speed of walking. Journal of Applied Physiology 100(2), 390–398 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Öner, A. (2013). Mapping of Incremental Dynamic Environment Using Rao-Blackwellized Particle Filter. In: Lee, S., Cho, H., Yoon, KJ., Lee, J. (eds) Intelligent Autonomous Systems 12. Advances in Intelligent Systems and Computing, vol 193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33926-4_68
Download citation
DOI: https://doi.org/10.1007/978-3-642-33926-4_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33925-7
Online ISBN: 978-3-642-33926-4
eBook Packages: EngineeringEngineering (R0)