Date of Award

3-2021

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Electrical and Computer Engineering

First Advisor

Joseph A. Curro, PhD

Abstract

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

AFIT-ENG-MS-21-M-006

DTIC Accession Number

AD1127376

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