NDLI: Decision fusion of global and local image features for Markov localization

Content Provider	IEEE Xplore Digital Library
Author	Zeng-Shun Zhao
Copyright Year	2009
Description	Author affiliation: College of Information and Electrical Engineering, Shandong University of Science and Technology, Qingdao 266510, China (Zeng-Shun Zhao)
Abstract	This paper addresses a major problem in the context of visual robot localization. Vision-based localization easily leads to ambiguities in large-scale environments. A probabilistic method is proposed for mobile robots to recognize scenes for topological localization. Appearance-based scene classes are automatically learned from composite features which combine global and local image features extracted from sets of training images. A modified Scale Invariant Feature Transform (SIFT) feature descriptor, which integrates color with local structure, is used as local features to disambiguate the identification of features easily confused. The environment is defined as a topological graph where each node corresponds to a place and edges are paths connecting one node with another. In the course of traveling, each detected interest point vote for the most likely location, and the correct location is the one getting the largest number of votes. In the case of perceptual aliasing, a Hidden Markov Model (HMM) is used to increase the robustness of location recognition. Experimental results show that application of the proposed feature and decision fusion can largely reduce wrong matches and the proposed method is effective.
Starting Page	32
Ending Page	37
File Size	275686
Page Count	6
File Format	PDF
ISBN	9781424437283
DOI	10.1109/ICWAPR.2009.5207436
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-07-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Markov Localization Image edge detection Mobile robots Decision Fusion Visual Feature Voting Layout Hidden Markov models Feature extraction Robot localization Robustness Large-scale systems Hidden Markov Model Joining processes
Content Type	Text
Resource Type	Article

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