Odometry-driven inference to link multiple exemplars of a location

Lowry, Stephanie, Wyeth, Gordon, & Milford, Michael (2013) Odometry-driven inference to link multiple exemplars of a location. In Sugano, S. & Kaneko, M. (Eds.) Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems 2013, IEEE, Tokyo Big Sight, Tokyo, pp. 534-539.

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Abstract

A major challenge for robot localization and mapping systems is maintaining reliable operation in a changing environment. Vision-based systems in particular are susceptible to changes in illumination and weather, and the same location at another time of day may appear radically different to a system using a feature-based visual localization system. One approach for mapping changing environments is to create and maintain maps that contain multiple representations of each physical location in a topological framework or manifold. However, this requires the system to be able to correctly link two or more appearance representations to the same spatial location, even though the representations may appear quite dissimilar. This paper proposes a method of linking visual representations from the same location without requiring a visual match, thereby allowing vision-based localization systems to create multiple appearance representations of physical locations. The most likely position on the robot path is determined using particle filter methods based on dead reckoning data and recent visual loop closures. In order to avoid erroneous loop closures, the odometry-based inferences are only accepted when the inferred path's end point is confirmed as correct by the visual matching system. Algorithm performance is demonstrated using an indoor robot dataset and a large outdoor camera dataset.

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ID Code: 66357
Item Type: Conference Paper
Refereed: Yes
DOI: 10.1109/IROS.2013.6696403
ISSN: 2153-0858
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Funding:
Copyright Owner: Copyright 2013 IEEE
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Deposited On: 21 Jan 2014 00:45
Last Modified: 09 Apr 2014 12:20

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