2015
Genetic Bayesian ARAM for Simultaneous Localization and Hybrid Map Building
2015 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI)
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- First page
- 275
- Last page
- 279
- Language
- English
- Publishing type
- Research paper (international conference proceedings)
- DOI
- 10.1109/SSCI.2015.48
- Publisher
- IEEE
This paper presents a new framework for mobile robot to perform localization and build topological-metric hybrid map simultaneously. The proposed framework termed as Genetic Bayesian ARAM consists of two main components: 1) Steady state genetic algorithm (SSGA) for self-localization and occupancy grid map building and 2) Bayesian Adaptive Resonance Associative Memory (ARAM) for topological map building. The proposed method is validated using a mobile robot. Result show that Genetic Bayesian ARAM capable of generate hybrid map online and perform localization simultaneously.
- Link information
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- DOI
- https://doi.org/10.1109/SSCI.2015.48
- DBLP
- https://dblp.uni-trier.de/rec/conf/ssci/ChinLKT15
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000380431500038&DestApp=WOS_CPL
- URL
- http://dblp.uni-trier.de/db/conf/ssci/ssci2015.html#conf/ssci/ChinLKT15
- ID information
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- DOI : 10.1109/SSCI.2015.48
- DBLP ID : conf/ssci/ChinLKT15
- Web of Science ID : WOS:000380431500038