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What is Simultaneous Localization And Mapping (SLAM)

Handbook of Research on Fostering Student Engagement With Instructional Technology in Higher Education
A system originating from robotics and computer vision, SLAM is the procedure by which a computer scans an environment and constructs a digital map of the area. This has become a standard for anchoring augmented reality content within the real world physical spaces. This is the process ARKit apps undertake to detect surfaces.
Published in Chapter:
Higher Education Teaching and Learning With Augmented Reality
Janet L. Holland (Emporia State University, USA), Sungwoong Lee (University of West Georgia, USA), Mohammad Daouk (Cerner Corporation, USA), and Daniel A. Agbaji (Emporia State University, USA)
DOI: 10.4018/978-1-7998-0119-1.ch013
This chapter is based on a review of the literature, initial lab examinations, and experiences teaching university undergraduate pre-service teachers and master degree students in Instructional Design and Technology. The authors analyzed the literature, benefits, drawbacks, experiences, and educational implications of integrating augmented reality in higher education to prepare students for eventual workplace success. Using augmented reality, three-dimensional interactive digital imaging provides an immersive, engaging learning environment to interact with content in new ways not previously possible. The 3D models can impart significant content information by viewing digital objects from any angle, sometimes peeling back the layers, all in real time. In addition, they consider the educational implications for integrating and evaluating augmented reality.
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A Survey of People Localization Techniques Utilizing Mobile Phones
A technique for localizing a person or object in an area and simultaneously building a map of the area by processing sensor reading data. Usually, the map is built and adjusted incrementally during multiple visits to the area, and localization accuracy improves during the map building process.
Full Text Chapter Download: US $37.50 Add to Cart
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