dc.contributor.advisor	Majji, Manoranjan
dc.contributor.committeeMember	Junkins, John
dc.contributor.committeeMember	Xiong, Zixiang
dc.creator	Verras, Andrew A
dc.date.accessioned	2022-01-27T22:10:13Z
dc.date.available	2023-08-01T06:41:29Z
dc.date.created	2021-08
dc.date.issued	2021-07-14
dc.date.submitted	August 2021
dc.date.updated	2022-01-27T22:10:13Z
dc.description.abstract	This thesis develops mathematical methods for utilizing monocular camera measurements for the guidance and navigation of various aerospace vehicles. For each of the three projects discussed, a nonlinear estimation algorithm is developed and then applied for testing in the field, in the laboratory, and/or in simulation. The first project is an Entry, Descent, and Landing (EDL) application in the presence of a priori unknown terrain. The proposed algorithm extracts features from the image, and integrates the camera measurements with onboard inertial sensors to orient a landing vehicle with respect to the terrain. The filter is tested using model terrain in a laboratory setting as well as simulated terrain. The second project concerns automated aerial refueling and develops a filter that uses images of a known target along with inertial sensors and GPS to provide accurate estimates of the relative position and orientation of two airborne vehicles. The target vehicle is marked by LED beacons which allow for fast image processing. Results are obtained from field testing using two automobiles and from laboratory testing using robotic platforms from the Land, Air, and Space Robotics (LASR) Laboratory at Texas A&M University. Finally, the third project is for a space debris removal application. The target is a depleted rocket body in Earth orbit. It has a known shape and size but has no onboard sensors or beacons. The image projection of the target's rocket nozzle is detected by the computer vision software, and an ellipse is fitted to the projection. The parameters of this ellipse are used to estimate the target's position and orientation, which then guides an onboard capture system to secure the target. This process is tested using robots from the LASR lab.	en
dc.format.mimetype	application/pdf
dc.identifier.uri	https://hdl.handle.net/1969.1/195240
dc.language.iso	en
dc.subject	Computer Vision	en
dc.subject	Guidance	en
dc.subject	Navigation	en
dc.subject	Kalman Filter	en
dc.subject	EDL	en
dc.subject	Proximity Operations	en
dc.subject	TRN	en
dc.title	Monocular Vision-Based Guidance and Navigation	en
dc.type	Thesis	en
dc.type.material	text	en
local.embargo.terms	2023-08-01
local.etdauthor.orcid	0000-0001-8152-9050
thesis.degree.department	Aerospace Engineering	en
thesis.degree.discipline	Aerospace Engineering	en
thesis.degree.grantor	Texas A&M University	en
thesis.degree.level	Masters	en
thesis.degree.name	Master of Science	en

Monocular Vision-Based Guidance and Navigation

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