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


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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T1 - A novel method for mobile robot simultaneous localization and mapping
A1 - LI Mao-hai
A1 - HONG Bing-rong
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A1 - WEI Zhen-hua
J0 - Journal of Zhejiang University Science A
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DOI - 10.1631/jzus.2006.A0937

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


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