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Multilevel classification scheme for AGV perception
Muhammad Naeem1; Sohail Asghar1; Shahzad Rafiq Irfan1; Simon Fong2
2010-12-01
Conference Name2010 6th International Conference on Advanced Information Management and Service (IMS)
Source PublicationProc. - 6th Intl. Conference on Advanced Information Management and Service, IMS2010, with ICMIA2010 - 2nd International Conference on Data Mining and Intelligent Information Technology Applications
Pages485-489
Conference Date30 Nov.-2 Dec. 2010
Conference PlaceSeoul, South Korea
Abstract

An Autonomous Ground Vehicle (AGV) should be capable of self-navigating through various terrains based on priori data as well as self-configuring and optimizing its motion on the basis of sensed data. Research has been in progress in this domain to improve terrain perception for planning, execution, and control of desired motion of an AGV. There involve certain processes to achieve these goals. During the perception phase multiple classification techniques such as Bayesian Inference, K-Mean clustering, Artificial Neural Network and many others are used depending on underlying sensing technology for example LADAR and RGB Camera. This paper proposes a multilevel classification scheme for terrain identification and obstacle detection to improve self-organization according to the known terrain type. As a result the computation cost is reduced because of the use of multiple sensors.

KeywordAutonomous Ground Vehicle Classification
URLView the original
Language英语
Fulltext Access
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Center of Research in Data Engineering (CORDE), Mohammad Ali Jinnah University, Islamabad, Pakistan
2.Faculty of Science and Technology, University of Macau, China
Recommended Citation
GB/T 7714
Muhammad Naeem,Sohail Asghar,Shahzad Rafiq Irfan,et al. Multilevel classification scheme for AGV perception[C],2010:485-489.
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