Date of Award


Document Type


Degree Name

Master of Science


Department of Electrical and Computer Engineering

First Advisor

Joseph A. Curro, PhD


The goal of this thesis is to evaluate a new indoor navigation technique by incorporating floor plans along with monocular camera images into a CNN as a potential means for identifying camera position. Building floor plans are widely available and provide potential information for localizing within the building. This work sets out to determine if a CNN can learn the architectural features of a floor plan and use that information to determine a location. In this work, a simulated indoor data set is created and used to train two CNNs. A classification CNN, which breaks up the floor plan into 100 discrete bins and achieved 76.1 percent top 5 accuracy on test data. Also, a regression CNN which achieved a distance error of 25.4 meters or less between the truth and predicted position on 80 percent of the test data. The models are further improved by combining them with a filter solution. The best performing classification CNN is evaluated on real world data captured via a TurtleBot 3, demonstrating the potential for this solution to be useful to real world Air Force indoor localization problems.

AFIT Designator


DTIC Accession Number