Spacecraft Pose Estimation from Monocular Images Using Neural Network Based Keypoints and Visibility Maps

Harvard, Alexei; Capuano, Vincenzo; Shao, Eugene Y.; Chung, Soon‐Jo

doi:10.2514/6.2020-1874

Cited by 17 publications

(15 citation statements)

References 11 publications

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“…With the recent increase of hardware acceleration capabilities in consumergrade computers and consequent expansion of deep learning to computer vision problems, Convolutional Neural Networks (CNNs) have demonstrated potential in local feature detection and description applied to relative navigation in space [17,18]. However, these algorithms are often characterised by a supervised selection of landmarks in the pre-training stage which is specific to the target, and therefore are outside of the scope of this paper.…”

Section: The Camera As a Navigation Sensormentioning

confidence: 99%

Benchmarking of local feature detectors and descriptors for multispectral relative navigation in space

et al. 2020

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Optical-based navigation for space is a field growing in popularity due to the appeal of efficient techniques such as Visual Simultaneous Localisation and Mapping (VSLAM), which rely on automatic feature tracking with low-cost hardware. However, low-level image processing algorithms have traditionally been measured and tested for ground-based exploration scenarios. This paper aims to fill the gap in the literature by analysing state-of-the-art local feature detectors and descriptors with a taylor-made synthetic dataset emulating a Non-Cooperative Rendezvous (NCRV) with a complex spacecraft, featuring variations in illumination, rotation, and scale. Furthermore, the performance of the algorithms on the Long Wavelength Infrared (LWIR) is investigated as a possible solution to the challenges inherent to on-orbit imaging in the visible, such as diffuse light scattering and eclipse conditions. The Harris, GFTT, DoG,

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Section: The Camera As a Navigation Sensormentioning

confidence: 99%

Benchmarking of local feature detectors and descriptors for multispectral relative navigation in space

et al. 2020

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“…Specifically, four potential connections can be identified by reviewing the literature. of literature [79,[121][122][123][124][125]. Specifically, in [121], the problem of spacecraft proximity operations including formation flying and on-orbit servicing was considered.…”

Section: Connection Between Ai and Guidance And Control Problemsmentioning

confidence: 99%

Review of advanced guidance and control algorithms for space/aerospace vehicles

Chai

Tsourdos

Chai

et al. 2021

Progress in Aerospace Sciences

112

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“…After training, the network was evaluated on the real imagery of comet 67P. The feature-matching performance outperformed both SURF and BRIEF ( Calonder et al, 2010 ) after approximately 400 training epochs (see Harvard et al (2020) , Villa et al (2020) for more details). While these results are quite promising, further development to couple matching with feature extraction is needed to evaluate the overall performance, as feature extraction remains susceptible to tracking moving shadows.…”

Section: Approachmentioning

confidence: 99%

Autonomous Exploration of Small Bodies Toward Greater Autonomy for Deep Space Missions

et al. 2021

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Autonomy is becoming increasingly important for the robotic exploration of unpredictable environments. One such example is the approach, proximity operation, and surface exploration of small bodies. In this article, we present an overview of an estimation framework to approach and land on small bodies as a key functional capability for an autonomous small-body explorer. We use a multi-phase perception/estimation pipeline with interconnected and overlapping measurements and algorithms to characterize and reach the body, from millions of kilometers down to its surface. We consider a notional spacecraft design that operates across all phases from approach to landing and to maneuvering on the surface of the microgravity body. This SmallSat design makes accommodations to simplify autonomous surface operations. The estimation pipeline combines state-of-the-art techniques with new approaches to estimating the target’s unknown properties across all phases. Centroid and light-curve algorithms estimate the body–spacecraft relative trajectory and rotation, respectively, using a priori knowledge of the initial relative orbit. A new shape-from-silhouette algorithm estimates the pole (i.e., rotation axis) and the initial visual hull that seeds subsequent feature tracking as the body gets more resolved in the narrow field-of-view imager. Feature tracking refines the pole orientation and shape of the body for estimating initial gravity to enable safe close approach. A coarse-shape reconstruction algorithm is used to identify initial landable regions whose hazardous nature would subsequently be assessed by dense 3D reconstruction. Slope stability, thermal, occlusion, and terra-mechanical hazards would be assessed on densely reconstructed regions and continually refined prior to landing. We simulated a mission scenario for approaching a hypothetical small body whose motion and shape were unknown a priori, starting from thousands of kilometers down to 20 km. Results indicate the feasibility of recovering the relative body motion and shape solely relying on onboard measurements and estimates with their associated uncertainties and without human input. Current work continues to mature and characterize the algorithms for the last phases of the estimation framework to land on the surface.

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Spacecraft Pose Estimation from Monocular Images Using Neural Network Based Keypoints and Visibility Maps

Cited by 17 publications

References 11 publications

Benchmarking of local feature detectors and descriptors for multispectral relative navigation in space

Benchmarking of local feature detectors and descriptors for multispectral relative navigation in space

Review of advanced guidance and control algorithms for space/aerospace vehicles

Autonomous Exploration of Small Bodies Toward Greater Autonomy for Deep Space Missions

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