Advisor

Ball, John E.

Committee Member

Archibald, Christopher

Committee Member

Du, Qian

Committee Member

Fowler, James E.

Date of Degree

8-1-2019

Original embargo terms

||8/15/2020||8/15/2020

Document Type

Graduate Thesis - Open Access

Major

Electrical and Computer Engineering

Degree Name

Master of Science

College

James Worth Bagley College of Engineering

Department

Department of Electrical and Computer Engineering

Abstract

For autonomous vehicles, 3D, rotating LiDAR sensors are critically important towards the vehicle's ability to sense its environment. Generally, these sensors scan their environment, using multiple laser beams to gather information about the range and the intensity of the reflection from an object. For multi--LiDAR systems, the placement of the sensors determines the density of the combined point cloud. I perform preliminary research on the optimal LiDAR placement strategy for an off--road, autonomous vehicle known as the Halo project. I use simulation to generate large amounts of labeled LiDAR data that can be used to train and evaluate a neural network used to process LiDAR data in the vehicle. The performance metrics of the network are then used to generalize the performance of the sensor pose. I also, describe and evaluate intrinsic and extrinsic calibration methods that are applied in the multi--LiDAR system.

URI

https://hdl.handle.net/11668/14503

Comments

Autonomy||LiDAR||Machine Learning||Neural Network||Calibration||Vehicle||Off Road||Simulation||co-registration||optimization

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