Design and performance assessment of hazard avoidance techniques for vision-based landing

Rogata, Paola; Sotto, Emanuele Di; Câmara, Francisco; Caramagno, Augusto; Rebordão, J.; Correia, Bento A. Brazio; Duarte, Pedro; Mancuso, Salvatore

doi:10.1016/j.actaastro.2007.01.030

Cited by 24 publications

(6 citation statements)

References 6 publications

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“…In any case, such level of representativeness in HDA testing (encompassing 6DoF simulation, in closed loop with the full GNC chain, and with sensors and actuators errors) has already been achieved in previous studies [4][5][6][7][8] , and was later achieved within the Lunar Lander project, first in the context of G(N)C integration activities, and finally at full system level integration.…”

Section: Test Environmentmentioning

confidence: 96%

“…This was quickly followed in the late 1990's by the Integrated Vision and Navigation for Planetary Exploration (IVN) study 3 , that has been the outgrowth of the LEDA study. In 2003, ESA initiated the Vision Based Relative Navigation Techniques Framework (VBRNAV) study [4][5][6] that would become the foundation for the HDA concept proposed for Lunar Lander. In addition, within the Aurora Core programme, the Agency has since initiated the Hazard Avoidance System Experiment (HASE) study 7-8 in 2008, in order to further mature the vision-based HDA solution originating from VBRNAV.…”

Section: Introductionmentioning

confidence: 99%

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Hazard Detection and Avoidance in ESA Lunar Lander: Concept and Performance

Parreira¹,

Vasconcelos²,

Montaño³

et al. 2013

AIAA Guidance, Navigation, and Control (GNC) Conference

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The ESA Lunar Lander mission is a step in the preparation for future exploration missions, being tasked with demonstrating autonomous soft, safe precision landing on the Moon. The last segment of the mission's descent and landing phase is the approach phase, during which the GNC subsystem steers the spacecraft to the targeted landing site (LS). The Hazard Detection and Avoidance (HDA) subsystem is active during this phase and uses the measurements provided by a camera and a LIDAR to assess the safety of the terrain. The HDA subsystem also determines which are the ground locations that can be reached by the spacecraft. Based on the safety and reachability assessments, the HDA subsystem then decides autonomously if a retargeting should be commanded to a new LS. In that case, the GNC subsystem is notified and tasked with driving the vehicle to perform a soft landing at the designated LS. This paper describes the concept and performances of the HDA subsystem proposed for the Lunar Lander mission. Sensitivity tests are performed in order to evaluate the robustness of the system. The performance of the HDA subsystem is demonstrated through Monte Carlo simulation campaigns on a functional engineering simulator.

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Section: Test Environmentmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Hazard Detection and Avoidance in ESA Lunar Lander: Concept and Performance

Parreira¹,

Vasconcelos²,

Montaño³

et al. 2013

AIAA Guidance, Navigation, and Control (GNC) Conference

View full text Add to dashboard Cite

show abstract

“…For HDA, the VB-CR-HDA algorithm was chosen [87], which analyzes the terrain topography and detects slopes, shadows and analyzes the textures using an image processing algorithm using a single optical camera.…”

Section: Structures and Mechanismsmentioning

confidence: 99%

A lander mission to probe subglacial water on Saturn׳s moon Enceladus for life

et al. 2015

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“…Optical imaging sensors have been widely used as the essential payloads of vision systems in aerospace applications: autonomous rendezvous and docking [1][2][3][4], vision-based landing [5], position and pose estimation [6][7][8][9][10][11][12][13], on-orbit serving [14,15], space robotics [16], satellite recognition [12,[17][18][19], 3D structure reconstruction and component detection [20,21], etc. Vision-based recognition and pose estimation of a target satellite are one of the key technologies to achieve these applications.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Satellite Recognition and Pose Estimation Using Gaussian Process Regression

Zhang

Jiang

et al. 2019

International Journal of Aerospace Engineering

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In this paper, we address the problem of vision-based satellite recognition and pose estimation, which is to recognize the satellite from multiviews and estimate the relative poses using imaging sensors. We propose a vision-based method to solve these two problems using Gaussian process regression (GPR). Assuming that the regression function mapping from the image (or feature) of the target satellite to its category or pose follows a Gaussian process (GP) properly parameterized by a mean function and a covariance function, the predictive equations can be easily obtained by a maximum-likelihood approach when training data are given. These explicit formulations can not only offer the category or estimated pose by the mean value of the predicted output but also give its uncertainty by the variance which makes the predicted result convincing and applicable in practice. Besides, we also introduce a manifold constraint to the output of the GPR model to improve its performance for satellite pose estimation. Extensive experiments are performed on two simulated image datasets containing satellite images of 1D and 2D pose variations, as well as different noises and lighting conditions. Experimental results validate the effectiveness and robustness of our approach.

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Design and performance assessment of hazard avoidance techniques for vision-based landing

Cited by 24 publications

References 6 publications

Hazard Detection and Avoidance in ESA Lunar Lander: Concept and Performance

Hazard Detection and Avoidance in ESA Lunar Lander: Concept and Performance

A lander mission to probe subglacial water on Saturn׳s moon Enceladus for life

Vision-Based Satellite Recognition and Pose Estimation Using Gaussian Process Regression

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