Fast Robot Localization Approach Based on Manifold Regularization with Sparse Area Features

Background/Introduction Robot localization can be considered as a cognition process that takes place during a robot estimating metric coordinates with vision. It provides a natural method for revealing the true autonomy of robots. In this paper, a kernel principal component analysis (PCA)-regularized least-square algorithm for robot localization with uncalibrated monocular visual information is presented. Our system is the first to use a manifold regularization strategy in robot localization, which achieves real-time localization using a harmonic function.