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IEEE Int. Conference on Robotics and Automation, Anchorage, Alaska, May 2010

This research was conducted at the Fordham University Robotics and Computer Vision Lab. For more information about graduate programs in Computer Science, see http://www.cis.fordham.edu/graduate.html, and the Fordham University Graduate School of Arts and Sciences, see http://www.fordham.edu/gsas.

Disciplines

Computer Engineering | Robotics

Abstract

A team of robots cooperating to quickly produce a map needs to share landmark information between members so that the local maps can be accurately merged. However, the appearance of landmarks as seen by members of the team can change dramatically due to the phenomenon of occlusion.

We have previously presented an approach to landmark representation using Terrain Spatiograms – an extension to image spatiograms in which the spatial information relates to the scene rather than the image. Because this representation preserves depth structure, it is possible to identify and filter potential occlusions.

We present an approach to identifying and filtering occlusions using terrain spatiograms, and report experimental results on 20 landmark datasets for varying states of occlusion. We show that occlusion can be detected and filtered, resulting in improved landmark matching scores.

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