Author

Eric D Farmer

Advisor

Ball, John E.

Committee Member

Gurbuz, Ali

Committee Member

Dabbiru, Lalitha

Date of Degree

5-1-2020

Original embargo terms

Complete embargo for 1 year||forever||5/15/2021

Document Type

Graduate Thesis - Open Access

Major

Computer Engineering

Degree Name

Master of Science

College

James Worth Bagley College of Engineering

Department

Department of Electrical and Computer Engineering

Abstract

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.

URI

https://hdl.handle.net/11668/16951

Sponsorship

This work was funded under by the US Army Engineer Research and Development Center (ERDC) and the US ARMY Ground Vehicle Systems Center (GVSC) under the Simulation Based Reliability and Safety (SimBRS) contract W56HZV-17-C-0095.

Comments

autonomous vehicles||deep learning||lidar||mavs||point clouds||squeezeseg

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