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JACIII Vol.19 No.4 pp. 523-531
doi: 10.20965/jaciii.2015.p0523
(2015)

Paper:

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Unsupervised Part-Based Scene Modeling for Map Matching

Kanji Tanaka and Shogo Hanada

Graduate School of Engineering, University of Fukui
3-9-1 Bunkyo, Fukui, Fukui 910-8507, Japan

Received:
October 28, 2014
Accepted:
April 30, 2015
Published:
July 20, 2015
Creative Commons License
Keywords:
mobile robots, map matching, part-model, common pattern discovery
Abstract
In exploring the 1-to-N map matching problem that exploits a compact map data description, we hope to improve map matching scalability used in robot vision tasks. We propose explicitly targeting fast succinct map matching, which consists of map matching subtasks alone. These tasks include offline map matching attempts to find compact part-based scene models that effectively explain individual maps by using fewer larger parts. These tasks also include online map matching to find correspondence between part-based maps efficiently. Our part-based scene modeling approach is unsupervised and uses common pattern discovery (CPD) between input and known reference maps. Results of our experiments, which use a publicly available radish dataset, confirm the effectiveness of our proposed approach.
Cite this article as:
K. Tanaka and S. Hanada, “Unsupervised Part-Based Scene Modeling for Map Matching,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.4, pp. 523-531, 2015.
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