City Research Online

Abstract

This paper presents a novel deep learning-based approach for relative pose estimation using a focused Plenoptic camera for space rendezvous operations of On-Orbit Servicing (OOS) applications. Plenoptic cameras, also known as light-field cameras, are similar to traditional cameras but have an array of microlenses in front of the sensor. This configuration offers several advantages, such as software-based refocusing and increased image quality in low-light conditions while maintaining an extended depth of field. Moreover, it enables the derivation of 3D depth images from the same light field, making it possible to use a single camera as a stereo vision system for autonomous space rendezvous navigation challenges. We propose a robust deep learning solution suitable for uncooperative close-range rendezvous missions, such as debris removal, based on a Bidirectional Long Short-Term Memory (BiLSTM) network and a Convolutional Neural Network (CNN), to accurately estimate the target’s pose from images captured by a Plenoptic camera mounted rigidly on the chaser satellite. We validate the proposed approach, named Cascaded Deep Network (CaDNET), using on-ground data obtained from a designed experimental setup. Through the quality experimental results achieved, we demonstrate the feasibility of adopting the Plenoptic camera as an AI-based relative navigation solution for space rendezvous missions.

Publication Type:	Article
Additional Information:	© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords:	Cameras, Space vehicles, Pose estimation, Robot vision systems, Deep learning, Robots, Light fields
Subjects:	Q Science > QC Physics T Technology > TJ Mechanical engineering and machinery
Departments:	School of Science & Technology School of Science & Technology > Engineering
SWORD Depositor:	Symplectic Administrator

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