Abstract : In this paper, we enhance appearance-based robot localization by using disparity maps. Disparity maps provide the same type of information as range based sensors (distance to objects) and thus, they are likely to be less sensitive to changes of illumination than plain images, that are the source of information generally used in appearance-based localization. The main drawback of disparity maps is that they can include very noisy depth values: points for which the algorithms can not determine reliable depth information. These noisy values have to be discarded resulting in missing values. The presence of missing values makes Principal Component Analysis (the standard method used to compress images in the appearance-based framework) unfeasible. We describe a novel Expectation-Maximization algorithm to determine the principal components of a data set including missing values and we apply it to disparity maps. The results we present show that disparity maps are a valid alternative to increase the robustness of appearance-based localization.
https://hal.inria.fr/inria-00321488
Contributeur : Jakob Verbeek
<>
Soumis le : mardi 8 mars 2011 - 15:07:07
Dernière modification le : lundi 25 septembre 2017 - 10:08:04
Document(s) archivé(s) le : jeudi 9 juin 2011 - 02:45:30
