Date of Award

3-2024

Document Type

Thesis

Degree Name

Master of Science in Systems Engineering

Department

Department of Systems Engineering and Management

First Advisor

David H. Curtis, PhD

Abstract

Rendezvous, Proximity, Operations, and Docking (RPOD) are proliferate in space applications such as on-orbit servicing and refueling. As a chaser spacecraft approaches, it needs to know the relative pose of the target spacecraft. This paper proposes an approach that uses the following structure: 1) known 3-dimensional model data of the spacecraft, 2) a visualization engine to acquire local body space features and generate synthetic imagery, 3) a Convolutional Neural Network (CNN) to detect features from imagery, and 4) a Perspective-n-Point (PnP) solver for pose estimation. Furthermore, lighting conditions play a significant role in the CNN’s ability to identify features, which, in turn, affects the pose estimation accuracy. The research presented here examines how the illumination angles affect the accuracy of the pose estimate.

AFIT Designator

AFIT-ENV-MS-24-M-120

Comments

A 12-month embargo was observed for posting this work on AFIT Scholar.

Distribution Statement A, Approved for Public Release. PA case number 88ABW-2026-0633.

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