Spatial-temporal Collaborative Sequential Monte Carlo for Mobile Robot Localization in Distributed Intelligent Environments


Share / Export Citation / Email / Print / Text size:

International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering , Engineering, Electrical & Electronic


eISSN: 1178-5608



VOLUME 5 , ISSUE 2 (June 2012) > List of articles

Spatial-temporal Collaborative Sequential Monte Carlo for Mobile Robot Localization in Distributed Intelligent Environments

Kun Qian * / Xudong Ma / Xian Zhong Dai / Fang Fang

Keywords : spatial-temporal collaboration, mobile robot, particle filter, distributed sensor network, localization and navigation.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 5, Issue 2, Pages 295-314, DOI:

License : (CC BY-NC-ND 4.0)

Received Date : 30-March-2012 / Accepted: 15-May-2012 / Published Online: 01-June-2012



In this paper, a spatial-temporal collaborative sequential Monte Carlo architecture for mobile robot localization is designed to well suites intelligent environment for service robotic system. A proposed algorithm, namely Distributed Proportional Allocation-Augmented Particle Filter (DPA-APF), resolves the sensor collaboration problem by the processes of augmented sampling, inter-node resampling, inner-node resampling and particle exchange. These procedures exploit data parallelism and pipelining of resampling operations and improve the scalability of distributed particle filters (PFs). Moreover, modified visual and laser sensor perception models are also addressed to guarantee reliable and accurate robot localization in dynamic scenarios that robot coexists with people. The proposed method is applied to a home-care robotic intelligent room with distributed smart nodes, and the experimental results validate the effectiveness of the proposed method, which is hopeful to reduce the gap that exists between PF theory and their implementation using networked hardware.

Content not available PDF Share



[1] J. Reijula, T. Rosendahl, K. Reijula, P. Roilas, H. Roilas, R. Sepponen, “New Method to Assess Service Quality in Care Homes for the Elderly”, International Journal on Smart Sensing and Intelligent Systems, Vol.3, No,1, pp.14-26, 2010.
[2] T. Sato, T. Harada, T. Mori, “Environment-type robot system Robotic Room featured by behavior media, behavior contents, and behavior adaptation”, IEEE/ASME Transactions on Mechatronics, Vol.9, No.3, pp.529-534, 2004.
[3] H. Hashimoto, “Intelligent interactive spaces - Integration of IT and robotics”, Workshop on Advanced Robotics and its Social Impacts. Nagoya, Japan: IEEE Computer Society, pp.85-90, 2005.
[4] H. Hongu, N. Y. Chong, M. Miyazaki, et al, “An Experimental Testbed for Knowledge Distributed Robot Systems”, Proc. 1st Int. Workshop on Networked Sensing Systems. Tokyo, Japan, pp.237-240, 2004.
[5] Y. Ha, J. Sohn, Y. Cho, et al, “A robotic service framework supporting automated integration of ubiquitous sensors and devices”. Information Sciences, Vol.177, No.3, pp.657-679, 2007.
[6] A. Saffiotti, M. Broxvall, M. Gritti, et al, “The PEIS-ecology project: Vision and results”, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. Nice, France: Inst. of Elec. and Elec. Eng. Computer Society, pp.2329-2335, 2008.
[7] J. H. Lee, K. Morioka, N. Ando, H. Hashimoto, “Cooperation of Distributed Intelligent Sensors in Intelligent Environment", IEEE/ASME Trans. on Mechatronics,Vol.9, No.3, pp.535-543, 2004
[8] K. Morioka, S. Kovacs, J. H. Lee, P. Korondi, “A Cooperative Object Tracking System with Fuzzy-Based Adaptive Camera Selection”, International Journal on Smart Sensing and Intelligent Systems, Vol.3, No,3, pp.338-358, 2010.
[9] A. Doucet, N. de Freitas, and N. Gordon, Eds., “Sequential Monte Carlo Methods in Practice”, Springer-Verlag, 2001.
[10] S. Thrun, D. Fox, W. Burgard, “Monte Carlo Localization with Mixture Proposal Distribution”, Proceedings AAAI National Conference on Artificial Intelligence, pp.859-865, 2000.
[11] S. Thrun, D. Fox, W. Burgard, F. Dellaert, “Robust Monte Carlo Localization for Mobile Robots”, Artifial Intelligence, Vol.28, pp.99-141, 2001.
[12] F. Zhao, J. Shin, J. Reich, “Information-driven dynamic sensor collaboration for tracking applications”, IEEE Signal Processing Magazine, Vol.19, pp.61-72, 2002.
[13] M. Rosencrantz, G. Gordon, S. Thrun, “Decentralized sensor fusion with distributed particle filters”, In: Proc. Conf. Uncertainty in Artificial Intelligence, Acapulco, Mexico, 2003.
[14] M. Bolic, P. M. Djuric, S. Hong, “Resampling Algorithms and Architectures for Distributed Particle Filters”, IEEE Trans. Signal Process, Vo.53, No.7, pp.2442-2450, 2004.
[15] M Coates, “Distributed Particle Filters for Sensor Networks”, In Proc. of 3nd Workshop on Information Processing in Sensor Networks, pp:99-107, 2004.
[16] H. Q. Liu, H. C. So, F. K. W. Chan, and K. W. K. Lui, “Distributed particle filter for target tracking in sensor networks”, Progress In Electromagnetics Research C, Vol.11, pp.171-182, 2009.
[17] N. J. Gordon, D. J. Salmond, A. Smith, “Novel-approach to nonlinear non-Gaussian Bayesian state estimation”, IEE Proceedings-F Radar and signal processing, Vol.140, No.2, pp.107-113, 1993.
[18] M. K. Pitt, N. Shephard, “Filtering via Simulation: Auxiliary Particle Filters”, Journal of the American Statistical Association, Vol.94, No.446, pp.590-599, 1999.
[19] N. A. Vlassis, B. Terwijn, B. Kröse, “Auxiliary Particle Filter Robot Localization from High-Dimensional Sensor Observations”, Proceedings of the 2002 IEEE International Conference on Robotics and Automation, Washington, DC, USA, pp.7-12, 2002.
[20] S. Thrun, W. Burgard, D. Fox. “Probabilistic Robotics”, London: MIT Press, 2005.
[21] D. Crisan, A. Doucet, “A survey of convergence results on particle filtering methods for practitioners”, IEEE Trans. Signal Processing, Vol.50, No.3, pp.736-746, 2002.
[22] X. D. Ma, X. Z. Dai and W. Shang, “Vision-based Extended Monte Carlo Localization for Mobile Robot”, Proceedings of the IEEE International Conference on Mechatronics and Automation, pp:1831-1836, 2005.
[23] K. Qian, X. D. Ma, X. Z. Dai and F. Fang, “Socially Acceptable Pre-collision Safety Strategies for Human-Compliant Navigation of Service Robots”, Advanced Robotics, Vol.24, No.13, pp.1813-1840, 2010.