Hashem Tamimi and Andreas Zell

Vision based Localization of Mobile Robots using Kernel approaches

IROS 2004, Sendai, Japan, September 28 - October 2, 2004


The aim of this article is to present the potential of Kernel Principal Component Analysis (Kernel PCA) in the field of vision based robot localization. Using Kernel PCA we can extract features from the visual scene of a mobile robot. The analysis is applied only to local features so as to guarantee better computational performance as well as translation invariance. Compared with the classical Principal Component Analysis (PCA), Kernel PCA results show superiority in localization and robustness in presence of noisy scenes. The key success of the kernel PCA is the use of fractional power polynomial kernels.


Paper: [pdf]


   AUTHOR =       "Hashem Tamimi and Andreas Zell",
   TITLE =        "Vision based Localization of Mobile Robots using Kernel approaches",
   BOOKTITLE =    "Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2004)",
   YEAR =         "2004",