Spacecraft Pose Estimation using Principal Component Analysis and a Monocular Camera

Shi, Jianfeng; Ulrich, Steve; Ruel, Stephane

doi:10.2514/6.2017-1034

Cited by 19 publications

(11 citation statements)

References 30 publications

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“…Shi et al [39] proposed a PCA-based method. The PCA algorithm matches the object from the camera image to a stored matrix of images that has been transformed to its eigenspaces.…”

Section: ) Non-network-based Methodsmentioning

confidence: 99%

Fast and Accurate Spacecraft Pose Estimation From Single Shot Space Imagery Using Box Reliability and Keypoints Existence Judgments

Huo

Zhang

2020

IEEE Access

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Real-time 6DOF (6 Degree of Freedom) pose estimation of an uncooperative spacecraft is an important part of proximity operations, e.g., space debris removal, spacecraft rendezvous and docking, on-orbit servicing, etc. In this paper, a novel efficient deep learning based approach is proposed to estimate the 6DOF pose of uncooperative spacecraft using monocular-vision measurement. Firstly, we introduce a new lightweight YOLO-liked CNN to detect spacecraft and predict 2D locations of the projected keypoints of a prior reconstructed 3D model in real-time. Then, we design two novel models for predicting the bounding box (bbox) reliability scores and the probability of keypoints existence. The two models not only significantly reduce the false positive, but also speed up convergence. Finally, the 6DOF pose is estimated and refined using Perspective-n-Point and geometric optimizer. Results demonstrate that the proposed approach achieves 73.2% average precision and 77.6% average recall for spacecraft detection on the SPEED dataset after only 200 training epochs. For the pose estimation task, the mean rotational error is 0.6812 • , and the mean translation error is 0.0320m. The proposed approach achieves competitive pose estimation performance and extreme lightweight (∼ 0.89 million learnable weights in total) on the SPEED dataset while being efficient for real-time applications.

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“…Shi et al [39] proposed a PCA-based method. The PCA algorithm matches the object from the camera image to a stored matrix of images that has been transformed to its eigenspaces.…”

Section: ) Non-network-based Methodsmentioning

confidence: 99%

Fast and Accurate Spacecraft Pose Estimation From Single Shot Space Imagery Using Box Reliability and Keypoints Existence Judgments

Huo

Zhang

2020

IEEE Access

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“…In comparison to feature-based methods, some approaches rely on directly exploiting the appearance of the spacecraft in the image. To the best of our knowledge, the only appearance-based method using a monoc-ular camera for spacecraft pose estimation is the work of [22]. This method performs Principal Component Analysis (PCA) over the spacecraft present in a query image in order to match it to a dataset of stored images with their corresponding pose ground truths.…”

Section: Appearance-based Approachesmentioning

confidence: 99%

LSPnet: A 2D Localization-oriented Spacecraft Pose Estimation Neural Network

García¹,

Musallam²,

Gaudillière³

et al. 2021

Preprint

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Being capable of estimating the pose of uncooperative objects in space has been proposed as a key asset for enabling safe close-proximity operations such as space rendezvous, in-orbit servicing and active debris removal. Usual approaches for pose estimation involve classical computer vision-based solutions or the application of Deep Learning (DL) techniques. This work explores a novel DLbased methodology, using Convolutional Neural Networks (CNNs), for estimating the pose of uncooperative spacecrafts. Contrary to other approaches, the proposed CNN directly regresses poses without needing any prior 3D information. Moreover, bounding boxes of the spacecraft in the image are predicted in a simple, yet efficient manner. The performed experiments show how this work competes with the state-of-the-art in uncooperative spacecraft pose estimation, including works which require 3D information as well as works which predict bounding boxes through sophisticated CNNs.

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“…This type of sensor was flight-tested as part of the LIRIS demonstrator during the ATV5 Mission [21] as well as part of the Raven ISS Hosted Payload [22], and it has been used in [23] as well as in [24] and in [18] to assess the robustness of a TIR-based navigation system for ADR and to validate a pose estimation method based on feature extraction, respectively. Also, Shi et al [25,26,27] used synthetic and real TIR camera images to validate a model-based and an appearance-based pose estimation methods, respectively. Notably, the TIR camera in [22] was fused with a visual camera and a flash LIDAR in order to improve the overall sensors performance.…”

Section: Review Of Monocular Eo Sensorsmentioning

confidence: 99%

“…However, the framework was designed to process 3D point clouds and thus its application was constrained to LIDARs or stereovision systems. To the best of the author's knowledge, the only appearance-based method for spacecraft pose estimation based on a monocular camera was proposed by Shi et al[27], and it is based on PCA.The pose matching algorithm is separated into an off-line training portion and a testing portion that computes the pose of the spacecraft in-flight. The PCA algorithm matches the object from the camera image (test image) to a stored matrix of images that has been transformed to its eigenspaces during the training phase.…”

mentioning

confidence: 99%

Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft

Cassinis

Fonod

Gill

2019

Progress in Aerospace Sciences

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The relative pose estimation of an inactive target by an active servicer spacecraft is a critical task in the design of current and planned space missions, due to its relevance for close-proximity operations, i.e. the rendezvous with a space debris and/or in-orbit servicing. Pose estimation systems based solely on a monocular camera are recently becoming an attractive alternative to systems based on active sensors or stereo cameras, due to their reduced mass, power consumption and system complexity. In this framework, a review of the robustness and applicability of monocular systems for the pose estimation of an uncooperative spacecraft is provided. Special focus is put on the advantages of multispectral monocular systems as well as on the improved robustness of novel image processing schemes and pose estimation solvers. The limitations and drawbacks of the validation of current pose estimation schemes with synthetic images are further discussed, together with the critical trade-offs for the selection of visual-based navigation filters. The state-of-the-art techniques are analyzed in order to provide an insight into the limitations involved under adverse illumination and orbit scenarios, high image contrast, background noise, and low signal-to-noise ratio, which characterize actual space imagery, and which could jeopardize the image processing algorithms and affect the pose estimation accuracy as well as the navigation filter's robustness. Specifically, a comparative

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Spacecraft Pose Estimation using Principal Component Analysis and a Monocular Camera

Cited by 19 publications

References 30 publications

Fast and Accurate Spacecraft Pose Estimation From Single Shot Space Imagery Using Box Reliability and Keypoints Existence Judgments

Fast and Accurate Spacecraft Pose Estimation From Single Shot Space Imagery Using Box Reliability and Keypoints Existence Judgments

LSPnet: A 2D Localization-oriented Spacecraft Pose Estimation Neural Network

Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft

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