CLC number: TP24
On-line Access:
Received: 2005-10-25
Revision Accepted: 2006-01-18
Crosschecked: 0000-00-00
Cited: 1
Clicked: 6280
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.
@article{title="A novel method for mobile robot simultaneous localization and mapping",
author="LI Mao-hai, HONG Bing-rong, LUO Rong-hua, WEI Zhen-hua",
journal="Journal of Zhejiang University Science A",
volume="7",
number="6",
pages="937-944",
year="2006",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2006.A0937"
}
%0 Journal Article
%T A novel method for mobile robot simultaneous localization and mapping
%A LI Mao-hai
%A HONG Bing-rong
%A LUO Rong-hua
%A WEI Zhen-hua
%J Journal of Zhejiang University SCIENCE A
%V 7
%N 6
%P 937-944
%@ 1673-565X
%D 2006
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.A0937
TY - JOUR
T1 - A novel method for mobile robot simultaneous localization and mapping
A1 - LI Mao-hai
A1 - HONG Bing-rong
A1 - LUO Rong-hua
A1 - WEI Zhen-hua
J0 - Journal of Zhejiang University Science A
VL - 7
IS - 6
SP - 937
EP - 944
%@ 1673-565X
Y1 - 2006
PB - Zhejiang University Press & Springer
ER -
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.
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