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


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

October 28, 2014
April 30, 2015
Online released:
July 20, 2015
July 20, 2015
mobile robots, map matching, part-model, common pattern discovery

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.

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Last updated on Mar. 28, 2017