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Massachusetts Institute of Technology

Quadrasense : immersive UAV-based cross-reality environmental sensor networks

Abstract

dc:description.abstract

Quadrasense explores futuristic applications in environmental sensing by integrating ideas of cross-reality with semi-autonomous sensor-aware vehicles. The cross-reality principals of telepresence, augmented reality, and virtual reality are enabled through an Unnamed-Aerial-Vehicle, a specialized imaging system, a Head-Mounted-Display, a video game engine and a commodity computer. Users may move between any of the three modes of interaction, in real-time, through a singular visual interface. Utilizing an environment built with video game technology, a system was developed that can track and move a UAV in the physical world, towards goals of sensing, exploration and visualization. This application expands on the use of video games engines for simulation by directly joining the virtual and real worlds.

Degree

thesis:*
Department dc:contributor.department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2015

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Ramasubramanian, Vasant
Advisor dc:contributor.advisor
  • Joseph A. Paradiso.

Subjects

dc:subject × 1

Rights

dc:rights
Statement dc:rights
  • M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/1721.1/101827
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/101827

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Ramasubramanian, Vasant. Quadrasense : immersive UAV-based cross-reality environmental sensor networks. Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/101827