Please use this identifier to cite or link to this item:
Title: An entropy optimization strategy for simultaneous localization and mapping
Authors: Liu, Y
Ren, XM
Rad, AB
Zhang, XZ
Wong, YK
Keywords: Autonomous mobile robots
Issue Date: 2010
Publisher: Springer
Source: Journal of intelligent and robotic systems: theory and applications, 2010, v. 60, no. 3-4, p. 435-455 How to cite?
Journal: Journal of Intelligent and Robotic Systems: Theory and Applications 
Abstract: We present a novel algorithm for simultaneous localization and mapping via application of entropy on construction of segment-based maps. Entropy has been incorporated in SLAM to enhance its sensitivity and robustness in presence of non-Gaussian uncertainties and disturbances. The kernel density estimator is employed to approximate the probability appearance of samples directly from sensor data. An entropy based robust estimator is then designed to extract reliable parameters of the line segment from the environment. Rao-Blackwellized particle filter is also adopted to estimate the pose of the robot and update the map simultaneously. Simulations and experiments results validate the effectiveness and accuracy of the proposed approach.
DOI: 10.1007/s10846-010-9426-7
Appears in Collections:Journal/Magazine Article

View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

Last Week
Last month
Citations as of Aug 13, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.