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Paper WeD08.4

Liu, Ming (ETH Zurich), Siegwart, Roland (ETH Zurich)

DP-FACT: Towards Topological Mapping and Scene Recognition with Color for Omnidirectional Camera

Scheduled for presentation during the Regular Session "Visual Learning" (WeD08), Wednesday, May 16, 2012, 17:15−17:30, Meeting Room 8 (Wacipi)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on December 17, 2017

Keywords Localization, Omnidirectional Vision

Abstract

Topological mapping and scene recognition problems are still challenging, especially for online realtime vision-based applications. We develop a hierarchical probabilistic model to tackle them using color information. This work is stimulated by our previous work [1] which defined a lightweight descriptor using color and geometry information from segmented panoramic images. Our novel model uses a Dirichlet Process Mixture Model to combine color and geometry features which are extracted from omnidirectional images. The inference of the model is based on an approximation of conditional probabilities of observations given estimated models. It allows online inference of the mixture model in real-time (at 50Hz), which outperforms other existing approaches. A real experiment is carried out on a mobile robot equipped with an omnidirectional camera. The results show the competence against the state-of-art.

 

 

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