Image-based attitude maneuvers for space debris tracking

Felicetti, Leonard; Emami, M. Reza

doi:10.1016/j.ast.2018.02.002

Cited by 28 publications

(12 citation statements)

References 19 publications

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“…A feature-tracking scheme was also presented in [19] that combines traditional feature-point detector and frame-wise matching to track a noncooperative and unknown satellite. Felicetti [20] put forward an active space debris visual tracking method, in which the chaser satellite can keep the moving object in field of view of optical camera by continuously pose correction. Those feature-based trackers heavily relied on the manually designed feature detector and cannot handle extreme cases in space (e.g.…”

Section: Related Work a Visual Tracking In Aerospacementioning

confidence: 99%

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3D Visual Tracking Framework with Deep Learning for Asteroid Exploration

Zhou

Sun

Hong

2021

Preprint

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3D visual tracking is significant to deep space exploration programs, which can guarantee spacecraft to flexibly approach the target. In this paper, we focus on the studied accurate and real-time method for 3D tracking. Considering the fact that there are almost no public dataset for this topic, A new large-scale 3D asteroid tracking dataset is presented, including binocular video sequences, depth maps, and point clouds of diverse asteroids with various shapes and textures. Benefitting from the power and convenience of simulation platform, all the 2D and 3D annotations are automatically generated. Meanwhile, we propose a deep-learning based 3D tracking framework, named as Track3D, which involves 2D monocular tracker and a novel light-weight amodal axis-aligned bounding-box network, A3BoxNet. The evaluation results demonstrate that Track3D achieves state-of-the-art 3D tracking performance in both accuracy and precision, comparing to a baseline algorithm. Moreover, our framework has great generalization ability to 2D monocular tracking performance.

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Section: Related Work a Visual Tracking In Aerospacementioning

confidence: 99%

“…Nsize j=1 e yj (20) in which ŷ is N size dimensional one-hot vector of size category label, y is the partial outputs of amodal box estimation network, of which dimension is also N size . Furthermore, L center res and L size res both use huber loss function.…”

mentioning

confidence: 99%

3D Visual Tracking Framework with Deep Learning for Asteroid Exploration

Zhou

Sun

Hong

2021

Preprint

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“…According to the academic simulation outcomes, the proposed filter provides far more accurate estimates for relative attitude and position than the extended Kalman filter [47]. Some researchers also use different algorithm systems which are Monte Carlo Simulation Method [52,53], Lyapunov Method [54,18], etc. or novel versions of Kalman filters as if Cubature Kalman filters [47], Adaptive Fading Kalman Filters (AFKF) [55,56], Federal Kalman Filters, for increasing accuracy of linearization, estimation, optimization of states within not only flying vehicles but also all movement vehicles for autonomous control, docking, relative navigation aims [57].…”

Section: Relative Navigation Algorithms For Uavsmentioning

confidence: 99%

Relative Navigation in UAV Applications

Erkec

Hajiyev

2020

IJAST

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This paper is committed to the relative navigation of Unmanned Aerial Vehicles (UAVs) flying in formation flight. The concept and methods of swarm UAVs technology and architecture have been explained. The relative state estimation models of unmanned aerial vehicles which are based on separate systems as Inertial Navigation Systems (INS)&Global Navigation Satellite System (GNSS), Laser&INS and Vision based techniques have been compared via various approaches. The sensors are used individually or integrated each other via sensor integration for solving relative navigation problems. The UAV relative navigation models are varied as stated in operation area, type of platform and environment. The aim of this article is to understand the correlation between relative navigation systems and potency of state estimation algorithms as well during formation flight of UAV.

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“…The amount of Space Debris (SD) in orbit around the Earth has concerned the space agencies around the world, because of the increase the risk over space activities [1,2,3,4,5]. Projections indicate that we have already reached the point of imbalance, i.e., given the likelihood of future collisions between existing objects, the LEO population will continue to increase even without new launches in the next 200 years [4,3].…”

Section: Introductionmentioning

confidence: 99%

Space evasive maneuvers: what is the viable technological parameters?15 / Manobras evasivas do espaço: quais são os parâmetros tecnológicos viáveis?

Costa¹,

Jesus²

2022

BJDV

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Space Debris (SD) is the by-product of space missions and Earth's gravitational capture in the dynamics of the Solar System. Their accumulation in this environment constitutes a real and permanent risk for these missions. Mathematical and computational analysis for the relative dynamics between SD and a space vehicle, considering the Earth's gravitational and propulsion forces, is performed in this work. Exhaustive numerical simulations of the model were performed for suboptimal technological parameters (power factor, exhaust speed) and physical parameters (start and duration of propulsion) suitable for evasive maneuvers for minimum fuel consumption and relative distance accuracy. The simulation results identify important patterns that help us understand the characteristics of space vehicle propellant control parameters for evasive maneuvers.

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Image-based attitude maneuvers for space debris tracking

Cited by 28 publications

References 19 publications

3D Visual Tracking Framework with Deep Learning for Asteroid Exploration

3D Visual Tracking Framework with Deep Learning for Asteroid Exploration

Relative Navigation in UAV Applications

Space evasive maneuvers: what is the viable technological parameters?15 / Manobras evasivas do espaço: quais são os parâmetros tecnológicos viáveis?

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