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On-line Access: 2011-07-04

Received: 2010-07-19

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Journal of Zhejiang University SCIENCE C 2011 Vol.12 No.7 P.574-588


Map building for dynamic environments using grid vectors

Author(s):  Wen-fei WANG, Rong XIONG, Jian CHU

Affiliation(s):  State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   rxiong@iipc.zju.edu.cn

Key Words:  Grid vector, Line, Segments, Dynamic, Simultaneous localization and mapping (SLAM), Expectation maximization (EM)

Wen-fei WANG, Rong XIONG, Jian CHU. Map building for dynamic environments using grid vectors[J]. Journal of Zhejiang University Science C, 2011, 12(7): 574-588.

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%T Map building for dynamic environments using grid vectors
%A Wen-fei WANG
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%DOI 10.1631/jzus.C1000255

T1 - Map building for dynamic environments using grid vectors
A1 - Wen-fei WANG
A1 - Rong XIONG
A1 - Jian CHU
J0 - Journal of Zhejiang University Science C
VL - 12
IS - 7
SP - 574
EP - 588
%@ 1869-1951
Y1 - 2011
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1000255

This paper addresses the problem of creating a geometric map with a mobile robot in a dynamic indoor environment. To form an accurate model of the environment, we present a novel map representation called the ‘grid vector’, which combines each vector that represents a directed line segment with a slender occupancy grid map. A modified expectation maximization (EM) based approach is proposed to evaluate the dynamic objects and simultaneously estimate the robot path and the map of the environment. The probability of each grid vector is evaluated in the expectation step and then used to distinguish the vector into static and dynamic ones. The robot path and map are estimated in the maximization step with a graph-based simultaneous localization and mapping (SLAM) method. The representation we introduce provides advantages on making the SLAM method strictly statistic, reducing memory cost, identifying the dynamic objects, and improving the accuracy of the data associations. The SLAM algorithm we present is efficient in computation and convergence. Experiments on three different kinds of data sets show that our representation and algorithm can generate an accurate static map in a dynamic indoor environment.

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


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