Data-Driven Image Processing for Onboard Optical Navigation Around a Binary Asteroid

Pugliatti, Mattia; Franzese, V.; Topputo, Francesco

doi:10.2514/1.a35213

Cited by 22 publications

(14 citation statements)

References 26 publications

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“…The process of designing close-proximity operations in a binary asteroid system is discussed at length in [13] while the preliminary mission analysis and GNC design of Milani which constituted the proposal and phase 0 of the mission are illustrated in [12]. A detailed description of the IP can be found in [17,18]. In [14] the important relationship between the trajectory design and orbit determination processes is shown.…”

Section: Mission Overviewmentioning

confidence: 99%

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The Milani mission: overview and architecture of the optical-based GNC system

Pugliatti

Rizza

Piccolo

et al. 2022

AIAA SCITECH 2022 Forum

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Milani is a 6U CubeSat that will be part of the Hera mission around the Didymos binary system. Its objectives are both scientific and technological: to study and characterize the Didymos environment, and to demonstrate the use of CubeSat technologies for interplanetary missions. The latter includes the usage of autonomous navigation algorithms in a close-proximity environment. The purpose of this work is twofold. First, to provide an updated overview of the Milani mission. Second, for the first time, to illustrate the architecture and some preliminary results of the semi-autonomous optical-based GNC system.

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Section: Mission Overviewmentioning

confidence: 99%

“…A detailed description of the WCOB is out of the scope of this work. The interested reader is referred to [17,18] for a more in-depth discussion about the IP of Milani.…”

Section: A Ipmentioning

confidence: 99%

The Milani mission: overview and architecture of the optical-based GNC system

Pugliatti

Rizza

Piccolo

et al. 2022

AIAA SCITECH 2022 Forum

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“…If only few Fig. 16 The Final-Cube. Each marker represents the best technique for a given shape bodies were tested it would not be possible to extract patterns from the available data, and thus suggest the best performing algorithm for a given shape.…”

Section: Methods Comparisonmentioning

confidence: 99%

“…Artificial Intelligence (AI) techniques can also be exploited to provide IP solutions or position estimates [13][14][15][16] completely bypassing traditional approaches. In doing so, however, they rely on large amount of data for training and they lack full explainability when compared to traditional methods.…”

Section: Introductionmentioning

confidence: 99%

Image Processing Robustness Assessment of Small-Body Shapes

2022

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Asteroids and comets are triggering interest due to the richness of precious materials, their scientific value as well as for their potential hazardousness. Owing to their significant diversity, minor bodies do not exhibit uniform shapes: they can range from spherical to irregularly shaped objects with rocky, uneven, and cratered surface. Nowadays, space probes rely more and more on optical navigation techniques, due to the increasing demand for autonomy. When dealing with minor bodies, the diversified range of shapes can significantly affect the performance of these techniques. In order to enable deep space probes to confidently deal with uncertainties, the need for robust image processing methods arises. Commonly, few image processing methods are designed and tested with limited shapes to meet mission requirements. In this work, we depart from this paradigm by developing a new framework, which includes extensive testing of the image processing algorithms with various shapes. The shapes are not randomly analyzed, yet they are arranged in a hierarchical structure called hyper-cube. The cube allows for a better understanding of the methods performance and to infer the way they shift from one shape to the other. The novelty of this approach lies both in the cube representation, which allows a better understanding of the link between the image processing algorithms and shape of the object, but also in the extensive number of shapes that have been tested, which has never been done before. In this analysis, four methods are considered, namely: center of brightness, intensity weighted centroiding, correlation with Lambertian spheres, and least-squares-based ellipse fitting. Results from this test allow us correlating the methods performances to the bodies shape, to suggest the best performing method for each shape family, and to assess their robustness.

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“…From the aforementioned IP algorithm, it is possible to gather the CoF as a data-driven correction of the CoB, a data-driven-derived PA, and the range. A more detailed discussion of the Milani’s IP can be found in Pugliatti et al [ 41 ] and Pugliatti et al [ 42 ].…”

Section: Applicationsmentioning

confidence: 99%

The TinyV3RSE Hardware-in-the-Loop Vision-Based Navigation Facility

Panicucci

Topputo

2022

Sensors

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The increase in number of interplanetary probes has emphasized the need for spacecraft autonomy to reduce overall mission costs and to enable riskier operations without ground support. The perception of the external environment is a critical task for autonomous probes, being fundamental for both motion planning and actuation. Perception is often achieved using navigation sensors which provide measurements of the external environment. For space exploration purposes, cameras are among the sensors that provide navigation information with few constraints at the spacecraft system level. Image processing and vision-based navigation algorithms are exploited to extract information about the external environment and the probe’s position within it from images. It is thus crucial to have the capability to generate realistic image datasets to design, validate, and test autonomous algorithms. This goal is achieved with high-fidelity rendering engines and with hardware-in-the-loop simulations. This work focuses on the latter by presenting a facility developed and used at the Deep-space Astrodynamics Research and Technology (DART) Laboratory at Politecnico di Milano. First, the facility design relationships are established to select hardware components. The critical design parameters of the camera, lens system, and screen are identified and analytical relationships are developed among these parameters. Second, the performances achievable with the chosen components are analytically and numerically studied in terms of geometrical accuracy and optical distortions. Third, the calibration procedures compensating for hardware misalignment and errors are defined. Their performances are evaluated in a laboratory experiment to display the calibration quality. Finally, the facility applicability is demonstrated by testing imageprocessing algorithms for space exploration scenarios.

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Data-Driven Image Processing for Onboard Optical Navigation Around a Binary Asteroid

Cited by 22 publications

References 26 publications

The Milani mission: overview and architecture of the optical-based GNC system

The Milani mission: overview and architecture of the optical-based GNC system

Image Processing Robustness Assessment of Small-Body Shapes

The TinyV3RSE Hardware-in-the-Loop Vision-Based Navigation Facility

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