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

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Abstract

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.

Methods

The core idea is to incorporate labelled and unlabelled observation data in offline training to generate a regression model smoothed by the intrinsic manifold embedded in area feature vectors. The harmonic function is employed to solve the online localization of new observations. Our key contributions include semi-supervised learning techniques for robot localization, the discovery and use of the visual manifold learned by kernel PCA and some solutions for simultaneous parameter selection. This simultaneous localization and mapping (SLAM) system combines dimension reduction methods, manifold regularization techniques and parameter selection to provide a paradigm of SLAM having self-contained theoretical foundations.

Results and Conclusions

In extensive experiments, we evaluate the localization errors from the perspective of reducing implementation and application difficulties in feature selection and magnitude ratio determination of labelled and unlabelled data. Then, a nonlinear optimization algorithm is adopted for simultaneous parameter selection. Our online localization algorithm outperformed the state-of-the-art appearance-based SLAM algorithms at a processing rate of 30 Hz for new data on a standard PC with a camera.

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Notes

  1. The observation matrix is mean centred and the Gaussian RBF kernel function is \(K({\text{obv}}_i,{\text{obv}}_j)=\exp (-\sigma \Vert {\text{obv}}_i-{\text{obv}}_j\Vert ^2)\), where  \(\sigma =200\).

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Acknowledgments

This work was supported by the Scientific Research Plan Projects for Higher Schools in Hebei Province (Grant No. QN2015338), the National Natural Science Foundation of China (Grant No. 61105083), the Program for New Century Excellent Talents in University (Grant No. NCET-11-0634), the Program of the Co-Construction with the Beijing Municipality of China (Program No. GJ2013005), the High Tech Research and Development (863) Program of China (Grant No. 2006AA04Z207), the Research Fund for Doctoral Programs of Higher Education of China (Grant No. 20060006018), the International Cooperation Program of Science and Technology of China (Grant No. 2007DFA11530) and the National Natural Science Foundation of China (Grant No. 60875072).

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Authors and Affiliations

  1. School of Control and Computer Engineering, North China Electric Power University, 102206, Beijing, People’s Republic of China

    Hua Wu, Chang-An Liu & Guo-Tian Yang

  2. Department of Disaster Prevention Apparatus, Institute of Disaster Prevention, Langfang, Hebei, People’s Republic of China

    Yan-Xiong Wu

  3. School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, People’s Republic of China

    Shi-Yin Qin

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Hua Wu, Yan-Xiong Wu, Chang-An Liu, Guo-Tian Yang and Shi-Yin Qin declare that they have no conflict of interest.

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Wu, H., Wu, YX., Liu, CA. et al. Fast Robot Localization Approach Based on Manifold Regularization with Sparse Area Features. Cogn Comput 8, 856–876 (2016). https://doi.org/10.1007/s12559-016-9427-3

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