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Journal of Robotics
Volume 2011, Article ID 257852, 12 pages
http://dx.doi.org/10.1155/2011/257852
Research Article

A Light-and-Fast SLAM Algorithm for Robots in Indoor Environments Using Line Segment Map

1Department of Electrical Engineering, National Tsing-Hua University 101, Section 2, Kuang-Fu Road, Hsinchu 30013, Taiwan
2Department of Electrical Engineering, National Tsing-Hua University 720R, EECS Bldg, 101, Section 2, Kuang-Fu Road, Hsinchu 30013, Taiwan
3Department of Electrical Engineering, National Tsing-Hua University 818, EECS Bldg, 101, Section 2, Kuang-Fu Road, Hsinchu 30013, Taiwan

Received 19 January 2011; Accepted 15 March 2011

Academic Editor: Heinz Wörn

Copyright © 2011 Bor-Woei Kuo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Simultaneous Localization and Mapping (SLAM) is an important technique for robotic system navigation. Due to the high complexity of the algorithm, SLAM usually needs long computational time or large amount of memory to achieve accurate results. In this paper, we present a lightweight Rao-Blackwellized particle filter- (RBPF-) based SLAM algorithm for indoor environments, which uses line segments extracted from the laser range finder as the fundamental map structure so as to reduce the memory usage. Since most major structures of indoor environments are usually orthogonal to each other, we can also efficiently increase the accuracy and reduce the complexity of our algorithm by exploiting this orthogonal property of line segments, that is, we treat line segments that are parallel or perpendicular to each other in a special way when calculating the importance weight of each particle. Experimental results shows that our work is capable of drawing maps in complex indoor environments, needing only very low amount of memory and much less computational time as compared to other grid map-based RBPF SLAM algorithms.