Date of Award

2024

Document Type

Open Access Master's Thesis

Degree Name

Master of Science in Electrical and Computer Engineering (MS)

Administrative Home Department

Department of Electrical and Computer Engineering

Advisor 1

Timothy Havens

Advisor 2

Jin Choi

Committee Member 1

Anthony Pinar

Committee Member 2

Evan Lucas

Abstract

The demand for autonomous vehicles (AVs) is rising across both military and civilian sectors. These unmanned systems offer numerous advantages, such as improved efficiency, safety, and adaptability. Addressing this demand requires the development of resilient and versatile autonomous vehicles crucial for the transport and reconnaissance markets.

The sensory perception of autonomous vehicles of any kind is paramount to their ability to navigate and localize in their environment. Factors such as sensor noise, erroneous readings, and deliberate attacks should all be considered when developing a robust autonomous system. This work aims to quantify the degradation of sensor data which causes mapping algorithms to fail and properly localize.

In this thesis, we explore five different simulated LIDAR perturbation models and their effects on mapping indoor and outdoor locations. The noise models are categorized into two types: \emph{fake} and \emph{real} point returns. A similarity metric is utilized to quantify the degradation of the resulting point clouds. An advantage of this approach, over implementing perturbations in physical environments, is the ability to test challenging or impractical perturbations on a simulated system.

Our findings confirm that increased levels of noise correlate with elevated errors in mapping. We discuss the process of cascading failures and the additional overlaid topography that is produced. We also discovered that certain types of sensor noise affect indoor mapping more than outdoor, particularly when the noise is localized.

In future research, we plan to investigate methods to physically implement the noise models employed in this study and to develop strategies for mitigating their impact on autonomous navigation.

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