Spiral Tessellation on the Sphere

In this paper we describe a tessellation of the unit sphere in the 3-dimensional space realized using a spiral joining the north and the south poles. This tiling yields to a one dimensional labeling of the tiles covering the whole sphere and to a 1-dimensional natural ordering on the set of tiles of the tessellation. The correspondence between a point on the sphere and the tile containing it is derived as an analytical function, allowing the direct computation of the tile. This tessellation exhibits some intrinsic features useful for general applications: absence of singular points and efficient tiles computation. Moreover, this tessellation can be parametrized to obtain additional features especially useful for spherical coordinate indexing: tiles with equal area and good shape uniformity of tiles. An application to spherical indexing of a database is presented, it shows an assessment of our spiral tiling for practical use

Static Attitude Determination Using Convolutional Neural Networks

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Autonomous Detection of Particles and Tracks in Optical Images

During its initial orbital phase in early 2019, the Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer (OSIRIS-REx) asteroid sample return mission detected small particles apparently emanating from the surface of the near-Earth asteroid (101955) Bennu in optical navigation images. Identification and characterization of the physical and dynamical properties of these objects became a mission priority in terms of both spacecraft safety and scientific investigation. Traditional techniques for particle identification and tracking typically rely on manual inspection and are often time-consuming. The large number of particles associated with the Bennu events and the mission criticality rendered manual inspection techniques infeasible for long-term operational support. In this work, we present techniques for autonomously detecting potential particles in monocular images and providing initial correspondences between observations in sequential images, as implemented for the OSIRIS-REx mission.Comment: 23 pages, 10 figure

An Image Processing Pipeline for Autonomous Deep-Space Optical Navigation

A new era of space exploration and exploitation is fast approaching. A multitude of spacecraft will flow in the future decades under the propulsive momentum of the new space economy. Yet, the flourishing proliferation of deep-space assets will make it unsustainable to pilot them from ground with standard radiometric tracking. The adoption of autonomous navigation alternatives is crucial to overcoming these limitations. Among these, optical navigation is an affordable and fully ground-independent approach. Probes can triangulate their position by observing visible beacons, e.g., planets or asteroids, by acquiring their line-of-sight in deep space. To do so, developing efficient and robust image processing algorithms providing information to navigation filters is a necessary action. This paper proposes an innovative pipeline for unresolved beacon recognition and line-of-sight extraction from images for autonomous interplanetary navigation. The developed algorithm exploits the k-vector method for the non-stellar object identification and statistical likelihood to detect whether any beacon projection is visible in the image. Statistical results show that the accuracy in detecting the planet position projection is independent of the spacecraft position uncertainty. Whereas, the planet detection success rate is higher than 95% when the spacecraft position is known with a 3sigma accuracy up to 10^5 km.Comment: 26 pages, 7 figure

A compressive sensing algorithm for attitude determination

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 29-30).We propose a framework for compressive sensing of images with local distinguishable objects, such as stars, and apply it to solve a problem in celestial navigation. Specifically, let x [epsilon] RN be an N-pixel image, consisting of a small number of local distinguishable objects plus noise. Our goal is to design an m x N measurement matrix A with m << N, such that we can recover an approximation to x from the measurements Ax. We construct a matrix A and recovery algorithm with the following properties: (i) if there are k objects, the number of measurements m is O((klog N)/(log k)), undercutting the best known bound of O(klog(N/k)) (ii) the matrix A is ultra-sparse, which is important when the signal is weak relative to the noise, and (iii) the recovery algorithm is empirically fast and runs in time sub-linear in N. We also present a comprehensive study of the application of our algorithm to attitude determination, or finding one's orientation in space. Spacecraft typically use cameras to acquire an image of the sky, and then identify stars in the image to compute their orientation. Taking pictures is very expensive for small spacecraft, since camera sensors use a lot of power. Our algorithm optically compresses the image before it reaches the camera's array of pixels, reducing the number of sensors that are required.by Rishi Vijay Gupta.M.Eng

Compressive Sensing with Local Geometric Features

We propose a framework for compressive sensing of images with local distinguishable objects, such as stars, and apply it to solve a problem in celestial navigation. Specifically, let x be an N-pixel real-valued image, consisting of a small number of local distinguishable objects plus noise. Our goal is to design an m-by-N measurement matrix A with m << N, such that we can recover an approximation to x from the measurements Ax. We construct a matrix A and recovery algorithm with the following properties: (i) if there are k objects, the number of measurements m is O((k log N)/(log k)), undercutting the best known bound of O(k log(N/k)) (ii) the matrix A is very sparse, which is important for hardware implementations of compressive sensing algorithms, and (iii) the recovery algorithm is empirically fast and runs in time polynomial in k and log(N). We also present a comprehensive study of the application of our algorithm to attitude determination, or finding one's orientation in space. Spacecraft typically use cameras to acquire an image of the sky, and then identify stars in the image to compute their orientation. Taking pictures is very expensive for small spacecraft, since camera sensors use a lot of power. Our algorithm optically compresses the image before it reaches the camera's array of pixels, reducing the number of sensors that are required

POLA POSISI BERBASIS FUZZY DALAM DOMAIN FREKUENSI UNTUK TEMU KEMBALI CITRA BINTANG

AbstrakPenelusuran bintang dilakukan untuk beberapa aplikasi teknologi satelit dan ruang angkasa. Identifikasi bintang merupakan tugas utama dalam penelusuran bintang. Salah satu cara untuk melakukan identifikasi bintang adalah membandingkan citra kamera satelit terhadap citra database dan melakukan temu kembali bintang yang sama. Tugas tersebut menjadi sulit ketika pengambilan citra dilakukan pada waktu atau kondisi yang berbeda. Penelitian ini melakukan temu kembali citra bintang dengan menggunakan keuntungan pada metode pola posisi berbasis Fuzzy dalam domain frekuensi. Pada tahap awal, dilakukan preprocessing. Kemudian dilakukan proses ekstraksi fitur pola bintang menggunakan pola posisi berbasis Fuzzy dalam domain frekuensi. Selanjutnya, dilakukan perhitungan nilai similaritas antar fitur pola bintang. Pada tahap akhir, dilakukan temu kembali citra masukan sesuai tingkat kemiripan fitur citra tersebut dengan fitur citra database. Beberapa pengujian telah dilakukan dengan menggunakan 172 dataset yang didapatkan dari database aplikasi Stellarium. Pengujian pertama dilakukan untuk melakukan temu kembali citra bintang tanpa dipengaruhi adanya perubahan waktu dan kondisi. Pengujian kedua dilakukan untuk melakukan temu kembali citra bintang dengan adanya pengaruh dari perubahan rotasi. Pengujian ketiga dilakukan untuk melakukan temu kembali citra bintang dengan adanya pengaruh waktu pengambilan data. Hasil ujicoba menunjukkan bahwa penelitian ini mampu melakukan temu kembali citra bintang dengan tingkat akurasi sebesar 80.81%.Kata kunci: citra bintang, identifikasi bintang, pola Fuzzy, temu kembali citra

Development of a low-cost multi-camera star tracker for small satellites

This thesis presents a novel small satellite star tracker that uses an array of low-cost, off the shelf imaging sensors to achieve high accuracy attitude determination performance. The theoretical analysis of improvements in star detectability achieved by stacking images from multiple cameras is presented. An image processing algorithm is developed to combine images from multiple cameras with arbitrary focal lengths, principal point offsets, distortions, and misalignments. The star tracker also implements other algorithms including the region growing algorithm, the intensity weighted centroid algorithm, the geometric voting algorithm for star identification, and the singular value decomposition algorithm for attitude determination. A star tracker software simulator is used to test the algorithms by generating star images with sensor noises, lens defocusing, and lens distortion. A hardware prototype is being assembled for eventual night sky testing to verify simulated performance levels. Star tracker flight hardware is being developed in the Laboratory for Advanced Space Systems at Illinois (LASSI) at the University of Illinois at Urbana Champaign for future CubeSat missions

NaRPA: Navigation and Rendering Pipeline for Astronautics

This paper presents Navigation and Rendering Pipeline for Astronautics (NaRPA) - a novel ray-tracing-based computer graphics engine to model and simulate light transport for space-borne imaging. NaRPA incorporates lighting models with attention to atmospheric and shading effects for the synthesis of space-to-space and ground-to-space virtual observations. In addition to image rendering, the engine also possesses point cloud, depth, and contour map generation capabilities to simulate passive and active vision-based sensors and to facilitate the designing, testing, or verification of visual navigation algorithms. Physically based rendering capabilities of NaRPA and the efficacy of the proposed rendering algorithm are demonstrated using applications in representative space-based environments. A key demonstration includes NaRPA as a tool for generating stereo imagery and application in 3D coordinate estimation using triangulation. Another prominent application of NaRPA includes a novel differentiable rendering approach for image-based attitude estimation is proposed to highlight the efficacy of the NaRPA engine for simulating vision-based navigation and guidance operations.Comment: 49 pages, 22 figure