Global visual localization of mobile robots using kernel principal component analysis

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.