Topographic Mapping and Analysis Based on 3d Reconstruction Model of Simulated Asteroid

Wang, R.; Di, Kaichang; Wan, Wenhui; Liu, Zaihua; Wang, Yilong; Liang, Wei; Chen, X.; Zhi, Shuai

doi:10.5194/isprs-archives-xliii-b3-2020-1165-2020

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…But LiDAR can only achieve high resolutions when scanning at close range. The second method is based on optical images (Pajusalu & Slavinskis, 2019; R. Wang et al., 2020). It's the most accurate method and the measurement range can be larger than LiDAR.…”

Section: Introductionmentioning

confidence: 99%

A 3‐D Shape Reconstruction Strategy for Small Solar System Bodies With Single Flyby Spaceborne Radar

Wei

Huang

et al. 2023

Earth and Space Science

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Small solar system bodies, such as asteroids and comets, have held important keys to understanding the origin and evolution of the solar system. As a result, the study of these celestial bodies has garnered worldwide attention for many years. The knowledge of the 3D shape of small solar system bodies is fundamental for determining their global physical properties (volume, density, rotational parameters), choosing the landing site, and observing their interior structure.There are three kinds of methods that can be used for reconstructing 3D global shapes of small solar system bodies. The first method is based on Light Detection and Ranging (LiDAR) (Mukai et al., 2007;Zuber et al., 2000). But LiDAR can only achieve high resolutions when scanning at close range. The second method is based on optical images (Pajusalu & Slavinskis, 2019; R. Wang et al., 2020). It's the most accurate method and the measurement range can be larger than LiDAR. However, the performance of camera is severely limited by the illumination condition. The third method is using radio waves, for example, radar tomography and synthetic aperture radar (SAR) techniques. The resolution of SAR is restricted by the transmit bandwidth and synthetic aperture length (Soumekh, 1999), so it can overcome the above problems of long range and poor illumination conditions. Furthermore, different from rocky moons and planets whose interior structure can be investigated by seismic waves, the small solar system bodies have much looser structure, such as a rubble-pile, and the use of seismic waves is limited. At present, the interior of a small solar system body can only be recovered through the detection of radio waves.In recent years, with the development of space instrument technology, many spaceborne radars have been used in the Moon and major planet exploration missions. Computed radar tomography (CRT) approach, SAR technique, and ground penetrating radar system, etc., have been used and achieved rich results (C. Li et al., 2020;Neish et al., 2011;Simpson et al., 2006). Different from the Moon and major planets, small solar system bodies are much smaller in size and have weaker gravity. Moreover, as shown by (Warner et al., 2009), most small solar system bodies spin rapidly, causing relative motion between the radar and the target in multiple directions, which

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Section: Introductionmentioning

confidence: 99%

A 3‐D Shape Reconstruction Strategy for Small Solar System Bodies With Single Flyby Spaceborne Radar

Wei

Huang

et al. 2023

Earth and Space Science

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A LiDAR-less approach to autonomous hazard detection and avoidance systems based on semantic segmentation

Peñarroya

Centuori²,

Sanjurjo

et al. 2023

Celest Mech Dyn Astron

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In this paper, a passive hazard detection and avoidance (HDA) system is presented, relying only on images as observations. To process these images, convolutional neural networks (CNNs) are used to perform semantic segmentation and identify hazards corresponding to three different layers, namely feature detection, shadow detection, and slope estimation. The absence of active sensors such as light detection and ranging (LiDAR) makes it challenging to assess the surface geometry of a celestial body, and the training of the neural networks in this work is oriented towards coping with that drawback. The image data set for the training is generated using blender, and different body shape models (also referred to as meshes) are included, onto which stochastic feature populations and illumination conditions are imposed to produce a more diverse database. The CNNs are trained following a transfer learning approach to reduce the training effort and take advantage of previously trained networks. The results accurately predict the hazards in the images belonging to the data set, but struggle to yield successful predictions for the slope estimation, when images external to the data set are used, indicating that including the geometry of the target body in the training phase makes an impact on the quality of these predictions. The obtained predictions are composed to create safety maps, which are meant to be given as input to the guidance block of the spacecraft to evaluate the need for a manoeuvre to avoid hazardous areas. Additionally, preliminary hardware-in-the-loop (HIL) test results are included, in which the algorithms developed are confronted against images taken using real hardware.

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Topographic Mapping and Analysis Based on 3d Reconstruction Model of Simulated Asteroid

Cited by 2 publications

References 8 publications

A 3‐D Shape Reconstruction Strategy for Small Solar System Bodies With Single Flyby Spaceborne Radar

A 3‐D Shape Reconstruction Strategy for Small Solar System Bodies With Single Flyby Spaceborne Radar

A LiDAR-less approach to autonomous hazard detection and avoidance systems based on semantic segmentation

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