Entropy based robust estimator and its application to line-based mapping

Authors:
Yan Liu;Xinzheng Zhang;Ahmad B. Rad;Xuemei Ren;Yiu-Kwong Wong
Affiliations:
Department of Automatic Control, Beijing Institute of Technology, Beijing, 100081, China;Department of Electrical Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;School of Engineering Science, Simon Fraser University Surrey, Canada;Department of Automatic Control, Beijing Institute of Technology, Beijing, 100081, China;Department of Electrical Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Venue:
Robotics and Autonomous Systems
Year:
2010

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Abstract

This paper presents a robust mapping algorithm for an application in autonomous robots. The method is inspired by the notion of entropy from information theory. A kernel density estimator is adopted to estimate the appearance probability of samples directly from the data. An Entropy Based Robust (EBR) estimator is then designed that selects the most reliable inliers of the line segments. The inliers maintained by the entropy filter are those samples that carry more information. Hence, the parameters extracted from EBR estimator are accurate and robust to the outliers. The performance of the EBR estimator is illustrated by comparing the results with the performance of three other estimators via simulated and real data.