Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/20863
Title: Entropy based robust estimator and its application to line-based mapping
Authors: Liu, Y
Zhang, X
Rad, AB
Ren, X
Wong, YK
Keywords: Entropy
Kernel density estimation
Line segment mapping
Robust regression
Issue Date: 2010
Publisher: Elsevier Science Bv
Source: Robotics and autonomous systems, 2010, v. 58, no. 5, p. 566-573 How to cite?
Journal: Robotics and Autonomous Systems 
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.
URI: http://hdl.handle.net/10397/20863
DOI: 10.1016/j.robot.2010.01.002
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