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Journal of Zhejiang University SCIENCE A 2006 Vol.7 No.6 P.937~944

http://doi.org/10.1631/jzus.2006.A0937


A novel method for mobile robot simultaneous localization and mapping


Author(s):  LI Mao-hai, HONG Bing-rong, LUO Rong-hua, WEI Zhen-hua

Affiliation(s):  School of Computer Science, Harbin Institute of Technology, Harbin 150001, China

Corresponding email(s):   limaohai@163.com

Key Words:  Mobile robot, Rao-Blackwellized particle filter (RBPF), Monocular vision, Simultaneous localization and mapping (SLAM)


LI Mao-hai, HONG Bing-rong, LUO Rong-hua, WEI Zhen-hua. A novel method for mobile robot simultaneous localization and mapping[J]. Journal of Zhejiang University Science A, 2006, 7(6): 937~944.

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author="LI Mao-hai, HONG Bing-rong, LUO Rong-hua, WEI Zhen-hua",
journal="Journal of Zhejiang University Science A",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2006.A0937"
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%A WEI Zhen-hua
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%D 2006
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.A0937

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T1 - A novel method for mobile robot simultaneous localization and mapping
A1 - LI Mao-hai
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A1 - WEI Zhen-hua
J0 - Journal of Zhejiang University Science A
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SP - 937
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%@ 1673-565X
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2006.A0937


Abstract: 
A novel mobile robot simultaneous localization and mapping (SLAM) method is implemented by using the rao-Blackwellized particle filter (RBPF) for monocular vision-based autonomous robot in unknown indoor environment. The particle filter combined with unscented Kalman filter (UKF) for extending the path posterior by sampling new poses integrating the current observation. Landmark position estimation and update is implemented through UKF. Furthermore, the number of resampling steps is determined adaptively, which greatly reduces the particle depletion problem. Monocular CCD camera mounted on the robot tracks the 3D natural point landmarks structured with matching image feature pairs extracted through Scale Invariant Feature Transform (SIFT). The matching for multi-dimension SIFT features which are highly distinctive due to a special descriptor is implemented with a KD-Tree. Experiments on the robot Pioneer3 showed that our method is very precise and stable.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1] Davison, A.J., 2003. Real-Time Simultaneous Localisation and Mapping with a Single Camera. Proc. of the Ninth Int. Conf. on Computer Vision ICCV’03. Nice, France, p.1403-1410.

[2] Doucet, A., de Freitas, J., Murphy, K., Russel, S., 2000. Rao-Blackwellized Partcile Filtering for Dynamic Bayesian Networks. Proc. of Conf. on Uncertainty in Artificial Intelligence (UAI). California, USA.

[3] Kortenkamp, D., Bonasso, R.P., Murphy, R., 1998. AI-based Mobile Robots: Case Studies of Successful Robot Systems. MIT Press, Cambridge.

[4] Liu, J.S., Chen, R., 1998. Sequential Monte Carlo methods for dynamical systems. J. Amer. Statist. Assoc., 93(443):1032-1044.

[5] Lowe, D., 2004. Distinctive image features from scale-invariant keypoints. Int. J. of Computer Vision, 60(2):91-110.

[6] Ma, S.D., Zhang, Z.Y., 1998. Computer Vision—Computational Theories and Algorithm. Science Press, Beijing, p.52-79 (in Chinese).

[7] Merwe, R., Doucet, A., Freitas N., Wan, E., 2000. The Unscented Particle Filter. Technical Report CUED/ FINFENG/TR 380. Cambridge University.

[8] Montemerlo, M., Thrun, S., 2003. Simultaneous Localization and Mapping with Unknown DATA Association Using FastSLAM. Proc. IEEE Int. Conf. Robotics and Automation (ICRA). Taipei, China, p.1985-1991.

[9] Moore, A.W., 1991. An Introductory Tutorial on KD-Trees. Technical Report No. 209. Computer Laboratory, Carnegie Mellon University, Pittsburgh, Cambridge.

[10] Murphy, K., Russell, S., 2001. Rao-Blackwellized Particle Filtering for Dynamic Bayesian Networks. In: Doucet, A., Freitas, N., Gordon, N. (Eds.), Sequential Monte Carlo Methods in Practice. Springer-Verlag, p.499-515.

[11] Sim, R., Elinas, P., Griffin, M., Little, J., 2005. Vision-Based SLAM Using the Rao-Blackwellized Particle Filter. IJCAI Workshop on Reasoning with Uncertainty in Robotics (RUR). Edinburgh, Scotland.

[12] Stachniss, C., Grisetti, G., Burgard, W., 2005. Recovering Particle Diversity in a Rao-Blackwellized Particle Filter for SLAM after Actively Closing Loops. Proc. the IEEE Int. Conf. on Robotics and Automation (ICRA). Barcelona, Spain, p.667-672.

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