DOI: https://doi.org/10.15368/theses.2022.116
Available at: https://digitalcommons.calpoly.edu/theses/2630
Date of Award
12-2022
Degree Name
MS in Computer Science
Department/Program
Computer Science
College
College of Engineering
Advisor
Jonathan Ventura
Advisor Department
Computer Science
Advisor College
College of Engineering
Abstract
Depth perception has become a heavily researched area as companies and researchers are striving towards the development of self-driving cars. Self-driving cars rely on perceiving the surrounding area, which heavily depends on technology capable of providing the system with depth perception capabilities. In this paper, we explore developing a single camera (monocular) depth prediction model that is trained on panoramic depth images. Our model makes novel use of transfer learning efficient encoder models, pre-training on a larger dataset of flat depth images, and optimizing the model for use with a Jetson Nano. Additionally, we present a training and optimization framework to make developing and testing new monocular depth perception models easier and faster. While the model failed to achieve a high frame rate, the framework and models developed are a promising starting place for future work.