Global Descriptors for Visual Pose Estimation of a Noncooperative Target in Space Rendezvous

Comellini, Anthea; Ny, Jérôme Le; Zenou, Emmanuel; Espinosa, Christine; Dubanchet, Vincent

doi:10.1109/taes.2021.3086888

Cited by 7 publications

(2 citation statements)

References 49 publications

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“…Generally, global features (e.g. bags of keypoints, shapes, or even raw images) have been preferred for combination with a variety of ML techniques ranging from nearest neighbour search [26] to unsupervised clustering [27], principal component analysis [28], Bayesian classification [29], and deep learning [30].…”

Section: B Learning-based Methodsmentioning

confidence: 99%

ChiNet: Deep Recurrent Convolutional Learning for Multimodal Spacecraft Pose Estimation

Rondao

Aouf

Richardson

2022

IEEE Trans. Aerosp. Electron. Syst.

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This paper presents an innovative deep learning pipeline which estimates the relative pose of a spacecraft by incorporating the temporal information from a rendezvous sequence. It leverages the performance of long short-term memory (LSTM) units in modelling sequences of data for the processing of features extracted by a convolutional neural network (CNN) backbone. Three distinct training strategies, which follow a coarse-to-fine funnelled approach, are combined to facilitate feature learning and improve end-to-end pose estimation by regression. The capability of CNNs to autonomously ascertain feature representations from images is exploited to fuse thermal infrared data with electro-optical red-green-blue (RGB) inputs, thus mitigating the effects of artifacts from imaging space objects in the visible wavelength. Each contribution of the proposed framework, dubbed ChiNet, is demonstrated on a synthetic dataset, and the complete pipeline is validated on experimental data.

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Section: B Learning-based Methodsmentioning

confidence: 99%

ChiNet: Deep Recurrent Convolutional Learning for Multimodal Spacecraft Pose Estimation

Rondao

Aouf

Richardson

2022

IEEE Trans. Aerosp. Electron. Syst.

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“…In addition to the above two feature extraction methods (HOG [43][44][45][46], SIFT [47][48][49], DTW [50,41,39,40]) for posture recognition, several feature extraction methods are widely used in posture recognition, such as Hu moment invariant (HMI) [51,52], Fourier descriptors (FD) [53,54], nonparametric weighted feature extraction (NWFE) [55,56], gray-level co-occurrence matrix (GLCM) [57,58].…”

Section: Other Feature Extraction Approachesmentioning

confidence: 99%

A Survey on Artificial Intelligence in Posture Recognition

Jiang¹,

Hu²,

Wang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Over the years, the continuous development of new technology has promoted research in the field of posture recognition and also made the application field of posture recognition have been greatly expanded. The purpose of this paper is to introduce the latest methods of posture recognition and review the various techniques and algorithms of posture recognition in recent years, such as scale-invariant feature transform, histogram of oriented gradients, support vector machine (SVM), Gaussian mixture model, dynamic time warping, hidden Markov model (HMM), lightweight network, convolutional neural network (CNN). We also investigate improved methods of CNN, such as stacked hourglass networks, multi-stage pose estimation networks, convolutional pose machines, and high-resolution nets. The general process and datasets of posture recognition are analyzed and summarized, and several improved CNN methods and three main recognition techniques are compared. In addition, the applications of advanced neural networks in posture recognition, such as transfer learning, ensemble learning, graph neural networks, and explainable deep neural networks, are introduced. It was found that CNN has achieved great success in posture recognition and is favored by researchers. Still, a more in-depth research is needed in feature extraction, information fusion, and other aspects. Among classification methods, HMM and SVM are the most widely used, and lightweight network gradually attracts the attention of researchers. In addition, due to the lack of 3D benchmark data sets, data generation is a critical research direction.

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A pose estimation method of space non-cooperative target based on ORBFPFH SLAM

Yan,

Wang,

et al. 2023

Optik

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Global Descriptors for Visual Pose Estimation of a Noncooperative Target in Space Rendezvous

Cited by 7 publications

References 49 publications

ChiNet: Deep Recurrent Convolutional Learning for Multimodal Spacecraft Pose Estimation

ChiNet: Deep Recurrent Convolutional Learning for Multimodal Spacecraft Pose Estimation

A Survey on Artificial Intelligence in Posture Recognition

A pose estimation method of space non-cooperative target based on ORBFPFH SLAM

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