Robust Features Extraction for On-board Monocular-based Spacecraft Pose Acquisition

Capuano, Vincenzo; Alimo, Shahrouz Ryan; Ho, Andrew; Chung, Soon‐Jo

doi:10.2514/6.2019-2005

Cited by 29 publications

(18 citation statements)

References 18 publications

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“…The implementation of CNNs for monocular pose estimation in space has already become an attractive solution in recent years [10][11][12], also thanks to the creation of the Spacecraft PosE Estimation Dataset (SPEED) [11], a database of highly representative synthetic images of PRISMA's TANGO spacecraft made publicly available by Stanford's Space Rendezvous Laboratory (SLAB) applicable to train and test different network architectures. One of the main advantages of CNNs over standard feature-based algorithms for relative pose estimation [3,13,14] is an increase in the robustness under adverse illumination condition, as well as a reduction in the computational complexity. Since the pose accuracies of the first adopted CNNs proved to be lower than the accuracies returned by common pose estimation solvers, especially in the estimation of the relative attitude [10], recent efforts investigated the capability of CNNs to perform keypoint localization prior to the actual pose estimation [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

CNN-Based Pose Estimation System for Close-Proximity Operations Around Uncooperative Spacecraft

Cassinis

Fonod

Gill

et al. 2020

AIAA Scitech 2020 Forum

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This paper introduces a novel framework which combines a Convolutional Neural Network (CNN) for feature detection with a Covariant Efficient Procrustes Perspective-n-Points (CEPPnP) solver and an Extended Kalman Filter (EKF) to enable robust monocular pose estimation for close-proximity operations around an uncooperative spacecraft. The relative pose estimation of an inactive spacecraft 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, such as In-Orbit Servicing and Active Debris Removal. The main contribution of this work stands in deriving statistical information from the Image Processing step, by associating a covariance matrix to the heatmaps returned by the CNN for each detected feature. This information is included in the CEPPnP to improve the accuracy of the pose estimation step during filter initialization. The derived measurement covariance matrix is used in a tightlycoupled EKF to allow an enhanced representation of the measurements error from the feature detection step. This increases the filter robustness in case of inaccurate CNN detections. The proposed method is capable of returning reliable estimates of the relative pose as well as of the relative translational and rotational velocities, under adverse illumination conditions and partial occultation of the target. Synthetic 2D images of the European Space Agency's Envisat spacecraft are used to generate datasets for training, validation and testing of the CNN. Likewise, the images are used to recreate representative close-proximity scenarios for the validation of the proposed method.

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Section: Introductionmentioning

confidence: 99%

CNN-Based Pose Estimation System for Close-Proximity Operations Around Uncooperative Spacecraft

Cassinis

Fonod

Gill

et al. 2020

AIAA Scitech 2020 Forum

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show abstract

“…However, in general, applying only one of these methods directly to real images may not be successful since these methods are indiscriminate towards background and foreground. As suggested in our prior work [59], a combination of different methods should be used.…”

Section: Image Acquisition and Processingmentioning

confidence: 99%

“…In [59], we proposed a new robust feature detection algorithm able to deal with actual space imagery, characterized by variable and unfavorable illumination conditions. The proposed strategy is based on a gradient based filter for background elimination, on the use of three multiple processing streams and on the synthesis of polylines to reduce the number of outliers.…”

Section: Image Acquisition and Processingmentioning

confidence: 99%

“…However, for such a goal the 3D model created via SfM or after several iterations of the SEPS filter should be simple enough and consist of a limited number of points to enable a fast 2D to 3D matching. One way to ensure that, proposed in our previous work [59], would be by robustly extracting only the points that belong to high level geometric structures identifiable in the target geometry, as polygons, or in general polylines.…”

Section: On-board Offline 3d Reconstructionmentioning

confidence: 99%

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Monocular-based pose determination of uncooperative space objects

et al. 2020

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Vision-based methods to determine the relative pose of an uncooperative orbiting object are investigated in applications to spacecraft proximity operations, such as on-orbit servicing, spacecraft formation flying, and small bodies exploration. Depending on whether the object is known or unknown, a shape model of the orbiting target object may have to be constructed autonomously in real-time by making use of only optical measurements. The Simultaneous Estimation of Pose and Shape (SEPS) algorithm that does not require a priori knowledge of the pose and shape of the target is presented. This makes use of a novel measurement equation and filter that can efficiently use optical flow information along with a star tracker to estimate the target's angular rotational and translational relative velocity as well as its center of gravity. Depending on the mission constraints, SEPS can be augmented by a more accurate offline, on-board 3D reconstruction of the target shape, which allows for the estimation of the pose as a known target. The use of Structure from Motion (SfM) for this purpose is discussed. A model-based approach for pose estimation of known targets is also presented. The architecture and implementation of both the proposed approaches are elucidated and their performance metrics are evaluated through numerical simulations by using a dataset of images that are synthetically generated according to a chaser/target relative motion in Geosynchronous Orbit (GEO).

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“…Therefore, we hope to obtain high-precision analytical solutions through analytical algorithms. 2) Based on noncooperative space measurement, the transformation of pose is calculated by using pattern matching and 3D point cloud technology [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Spacecraft Pose Estimation Based on Different Camera Model

Lidong

Chi

Zhao

2020

Preprint

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Spacecraft pose estimation is an important technology for spacecraft to maintain or change its orientation in space. For spacecraft pose estimation, when two spacecraft are relatively far away, the depth information of the space point is less than that of measuring distance, so that the camera model can be seen as a weak perspective projection model. In this paper, a spacecraft pose estimation algorithm based on four symmetrical points of the spacecraft outline is proposed. Analytical solution of spacecraft pose is obtained by solving the weak perspective projection model, which can meet the requirements of the measurement model when there is a long measurement distance. Optimal solution is obtained from the weak perspective projection model to the perspective projection model, which can meet the measurement requirement when the measuring distance is close. The simulation results show that the proposed algorithm can get better results even though the noise is large.

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Robust Features Extraction for On-board Monocular-based Spacecraft Pose Acquisition

Cited by 29 publications

References 18 publications

CNN-Based Pose Estimation System for Close-Proximity Operations Around Uncooperative Spacecraft

CNN-Based Pose Estimation System for Close-Proximity Operations Around Uncooperative Spacecraft

Monocular-based pose determination of uncooperative space objects

Spacecraft Pose Estimation Based on Different Camera Model

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