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Localization and Mapping for Indoor Navigation: Survey
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Author(s): Heba Gaber (Ain-Shams University, Egypt), Mohamed Marey (Ain-Shams University, Egypt), Safaa Amin (Ain-Shams University, Egypt)and Mohamed F. Tolba (Ain-Shams University, Egypt)
Copyright: 2017
Pages: 25
Source title:
Handbook of Research on Machine Learning Innovations and Trends
Source Author(s)/Editor(s): Aboul Ella Hassanien (Cairo University, Egypt)and Tarek Gaber (Suez Canal University, Egypt)
DOI: 10.4018/978-1-5225-2229-4.ch007
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Abstract
Mapping and exploration for the purpose of navigation in unknown or partially unknown environments is a challenging problem, especially in indoor environments where GPS signals can't give the required accuracy. This chapter discusses the main aspects for designing a Simultaneous Localization and Mapping (SLAM) system architecture with the ability to function in situations where map information or current positions are initially unknown or partially unknown and where environment modifications are possible. Achieving this capability makes these systems significantly more autonomous and ideal for a large range of applications, especially indoor navigation for humans and for robotic missions. This chapter surveys the existing algorithms and technologies used for localization and mapping and highlights on using SLAM algorithms for indoor navigation. Also the proposed approach for the current research is presented.
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