COMING SOON! PQDT Open is getting a new home!

ProQuest Open Access Dissertations & Theses will remain freely available as part of a new and enhanced search experience at

Questions? Please refer to this FAQ.

Dissertation/Thesis Abstract

Sensor Capture and Point Cloud Processing for Off-Road Autonomous Vehicles
by Farmer, Eric D., M.S., Mississippi State University, 2020, 62; 27837693
Abstract (Summary)

Autonomous vehicles are complex robotic and artificial intelligence systems working together to achieve safe operation in unstructured environments. The objective of this work is to provide a foundation to develop more advanced algorithms for off-road autonomy. The project explores the sensors used for off-road autonomy and the data capture process. Additionally, the point cloud data captured from lidar sensors is processed to restore some of the geometric information lost during sensor sampling. Because ground truth values are needed for quantitative comparison, the MAVS was leveraged to generate a large off-road dataset in a variety of ecosystems. The results demonstrate data capture from the sensor suite and successful reconstruction of the selected geometric information. Using this geometric information, the point cloud data is more accurately segmented using the SqueezeSeg network.

Indexing (document details)
Advisor: Ball, John E
Commitee: Gurbuz, Ali, Dabbiru, Lalitha
School: Mississippi State University
Department: Electrical and Computer Engineering
School Location: United States -- Mississippi
Source: MAI 81/11(E), Masters Abstracts International
Subjects: Computer Engineering, Artificial intelligence, Robotics
Keywords: Autonomous vehicles, Deep learning, Lidar, MAVS, Point clouds, Squeezeseg
Publication Number: 27837693
ISBN: 9798643196082
Copyright © 2021 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy