Deep Learning Crater Detection for Lunar Terrain Relative Navigation

Downes, Lena M.; Steiner, Ted J.; How, Jonathan P.

doi:10.2514/6.2020-1838

Cited by 38 publications

(33 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Of special note is the tremendous progress made in CDAs with machine learning over the last five years. Some notable algorithms include: DeepMoon 2 [131], Python crater detection algorithm (PyCDA) 3 [69], LunaNet [37], CraterIDNet [139], and others [9,33,40].…”

Section: Crater Detectionmentioning

confidence: 99%

Lunar Crater Identification in Digital Images

2021

View full text Add to dashboard Cite

It is often necessary to identify a pattern of observed craters in a single image of the lunar surface and without any prior knowledge of the camera’s location. This so-called “lost-in-space” crater identification problem is common in both crater-based terrain relative navigation (TRN) and in automatic registration of scientific imagery. Past work on crater identification has largely been based on heuristic schemes, with poor performance outside of a narrowly defined operating regime (e.g., nadir pointing images, small search areas). This work provides the first mathematically rigorous treatment of the general crater identification problem. It is shown when it is (and when it is not) possible to recognize a pattern of elliptical crater rims in an image formed by perspective projection. For the cases when it is possible to recognize a pattern, descriptors are developed using invariant theory that provably capture all of the viewpoint invariant information. These descriptors may be pre-computed for known crater patterns and placed in a searchable index for fast recognition. New techniques are also developed for computing pose from crater rim observations and for evaluating crater rim correspondences. These techniques are demonstrated on both synthetic and real images.

show abstract

Section: Crater Detectionmentioning

confidence: 99%

Lunar Crater Identification in Digital Images

2021

View full text Add to dashboard Cite

show abstract

“…There are several prior works that leverage machine learning for detecting craters on the surface of the moon [31][32][33][34][35][36].…”

Section: B Machine Learning For Safe Landing On Planetary Surfacesmentioning

confidence: 99%

Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing

Tomita¹,

Skinner²,

Ho³

2021

Preprint

View full text Add to dashboard Cite

Hazard detection is critical for enabling autonomous landing on planetary surfaces. Current state-of-the-art methods leverage traditional computer vision approaches to automate identification of safe terrain from input digital elevation models (DEMs). However, performance for these methods can degrade for input DEMs with increased sensor noise. At the same time, deep learning techniques have been developed for various applications. Nevertheless, their applicability to safety-critical space missions has been often limited due to concerns regarding their outputs' reliability. In response to this background, this paper proposes an uncertaintyaware learning-based method for hazard detection and landing site selection. The developed approach enables reliable safe landing site selection by: (i) generating a safety prediction map and its uncertainty map together via Bayesian deep learning and semantic segmentation; and (ii) using the generated uncertainty map to filter out the uncertain pixels in the prediction map so that the safe landing site selection is performed only based on the certain pixels (i.e., pixels for which the model is certain about its safety prediction). Experiments are presented with simulated data based on a Mars HiRISE digital terrain model and varying noise levels to demonstrate the performance of the proposed approach.

show abstract

“…Terrain navigation CNN-based classifier [62][63][64] Validate selected features Detect and identify U-net based [66], [68], [24] CraterIDNet(CNN) [60] LunarNet(CNN-based) [73] NN-based…”

Section: Hazard Detectionmentioning

confidence: 99%

“…Additionally, real labelled on-orbit images required for training DL algorithms are expensive and hard to obtain, which leads to a lack of space imagery datasets. Challenges in space missions for applying vision-based DL methods can be summarised from previous research [6,18,19,20,21,22,23,24,25] as follows:…”

Section: Challenges and Motivationsmentioning

confidence: 99%

Deep Learning-based Spacecraft Relative Navigation Methods: A Survey

Song,

Rondao,

Aouf

2021

Preprint

View full text Add to dashboard Cite

Autonomous spacecraft relative navigation technology has been planned for and applied to many famous space missions. The development of on-board electronics systems has enabled the use of vision-based and LiDAR-based methods to achieve better performances. Meanwhile, deep learning has reached great success in different areas, especially in computer vision, which has also attracted the attention of space researchers. However, spacecraft navigation differs from ground tasks due to high reliability requirements but lack of large datasets. This survey aims to systematically investigate the current deep learning-based autonomous spacecraft relative navigation methods, focusing on concrete orbital applications such as spacecraft rendezvous and landing on small bodies or the Moon. The fundamental characteristics, primary motivations, and contributions of deep learningbased relative navigation algorithms are first summarised from three perspectives of spacecraft rendezvous, asteroid exploration, and terrain navigation. Furthermore, popular visual tracking benchmarks and their respective properties are compared and summarised. Finally, potential applications are discussed, along with expected impediments.

show abstract

Deep Learning Crater Detection for Lunar Terrain Relative Navigation

Cited by 38 publications

References 15 publications

Lunar Crater Identification in Digital Images

Lunar Crater Identification in Digital Images

Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing

Deep Learning-based Spacecraft Relative Navigation Methods: A Survey

Contact Info

Product

Resources

About