NDLI: Entropy based feature selection scheme for real time simultaneous localization and map building

Content Provider	IEEE Xplore Digital Library
Author	Sen Zhang Lihua Xie Adams, M.D.
Copyright Year	2005
Description	Author affiliation: Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore (Sen Zhang; Lihua Xie; Adams, M.D.)
Abstract	We propose a novel entropy-based method for feature selection in order to reduce the computational burden for real time simultaneous localization and map building (SLAM) for mobile robot navigation. Our approach is based on information (entropy) theory together with a data association method to initialize new features into the map, match measurements to the map features, and remove out-of-date features. The selected features are optimum in the sense that fusion of measurements from those features with existing information would yield the most entropy reduction in estimating the robot location and the map features' locations. Our method has the advantage of selecting a suitable number of features by considering the computational constraint in real time implementations. Simulation results show that the proposed entropy based feature selection strategy is effective in dealing with the map scaling problem in SLAM.
Starting Page	1175
Ending Page	1180
File Size	384501
Page Count	6
File Format	PDF
ISBN	0780389123
DOI	10.1109/IROS.2005.1545054
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-08-02
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Simultaneous localization and mapping Mobile robots Vehicles Robot sensing systems Concurrent computing Computational efficiency Navigation Information entropy Yield estimation Computational modeling SLAM Entropy Feature Selection
Content Type	Text
Resource Type	Article

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