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Journal of Sensors
Volume 2017 (2017), Article ID 6842173, 11 pages
Research Article

Distributed Monocular SLAM for Indoor Map Building

Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA

Correspondence should be addressed to Ruwan Egodagamage

Received 28 April 2017; Revised 26 June 2017; Accepted 3 July 2017; Published 10 August 2017

Academic Editor: Jacky C. K. Chow

Copyright © 2017 Ruwan Egodagamage and Mihran Tuceryan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Utilization and generation of indoor maps are critical elements in accurate indoor tracking. Simultaneous Localization and Mapping (SLAM) is one of the main techniques for such map generation. In SLAM an agent generates a map of an unknown environment while estimating its location in it. Ubiquitous cameras lead to monocular visual SLAM, where a camera is the only sensing device for the SLAM process. In modern applications, multiple mobile agents may be involved in the generation of such maps, thus requiring a distributed computational framework. Each agent can generate its own local map, which can then be combined into a map covering a larger area. By doing so, they can cover a given environment faster than a single agent. Furthermore, they can interact with each other in the same environment, making this framework more practical, especially for collaborative applications such as augmented reality. One of the main challenges of distributed SLAM is identifying overlapping maps, especially when relative starting positions of agents are unknown. In this paper, we are proposing a system having multiple monocular agents, with unknown relative starting positions, which generates a semidense global map of the environment.