Detection and Initial Assessment of Lunar Landing Sites Using Neural Networks

Posada, Daniel; Jordan, Jarred; Angelica, Radulovic,; Hong, Lillian; Malik, Aryslan; Henderson, Troy

doi:10.48550/arxiv.2207.11413

Cited by 1 publication

(5 citation statements)

References 23 publications

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“…For the effect of viewing the raw data, the outlier was left on the calculation, increasing the error in the slope estimate. For the lander pictured in Figure 6, a slope of 10 • is the limit before tilting [42], so even with this outlier, the plain is still suitable if chosen as a landing site.…”

Section: Jetson Tx2 Resultsmentioning

confidence: 99%

“…On the other hand, DISK performed ideally even in the most complex lighting scenarios, as it detects many features even at small resolutions where detail could be lost. This advantage is significant because, based on the HDA logic using quadtree [42], it is more efficient to analyze a certain amount of regions of interest (ROIs) that could be safe for landing than to analyze the complete image and deal with too many data points. At 400 m in altitude, for the camera described previously, an ROI of ≈20 × 20 m is equivalent to a 256 × 256 image patch.…”

Section: Discussionmentioning

confidence: 99%

“…To model the images, a spacecraft trajectory was based on [42], which bases the trajectory of a possible landing scenario for Intuitive Machines' Nova-C lunar lander. During this maneuver, maintaining an attitude hold while acquiring the images is required to maximize the baseline effectiveness for SfM.…”

Section: Spacecraft and Cameramentioning

confidence: 99%

“…Hazard detection and avoidance and structure-from-motion are pivotal in ensuring the safe landing of spacecraft on planetary bodies like the Moon. As detailed in the papers on lunar landings [35,42], HDA systems use advanced algorithms to quickly identify and avoid potential hazards such as rocks, craters, and uneven terrain within seconds of landing. This rapid assessment is crucial in a space environment where real-time decisions are necessary for the safety of the mission.…”

Section: Hda Sfm and Slope Estimationmentioning

confidence: 99%

“…When dealing with a practical situation in a limited setting, having this amount of data to analyze simultaneously is excessive. Therefore, Section 4.3 will illustrate results on an implemented pipeline wherein the image is split efficiently using quadtrees as explained in [42]. Additionally, these results are obtained using floating point precision in a regular desktop computer with an AMD Ryzen 7 3700x 8-core processor (AMD, Santa Clara, CA, USA) and 32 GB of RAM.…”

Section: Feature Matching Testing On a Desktop Computermentioning

confidence: 99%

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Dense Feature Matching for Hazard Detection and Avoidance Using Machine Learning in Complex Unstructured Scenarios

Posada,

Henderson

2024

Aerospace

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Exploring the Moon and Mars are crucial steps in advancing space exploration. Numerous missions aim to land and research in various lunar locations, some of which possess challenging surfaces with unchanging features. Some of these areas are cataloged as lunar light plains. Their main characteristics are that they are almost featureless and reflect more light than other lunar surfaces. This poses a challenge during navigation and landing. This paper compares traditional feature matching techniques, specifically scale-invariant feature transform and the oriented FAST and rotated BRIEF, and novel machine learning approaches for dense feature matching in challenging, unstructured scenarios, focusing on lunar light plains. Traditional feature detection methods often need help in environments characterized by uniform terrain and unique lighting conditions, where unique, distinguishable features are rare. Our study addresses these challenges and underscores the robustness of machine learning. The methodology involves an experimental analysis using images that mimic lunar-like landscapes, representing these light plains, to generate and compare feature maps derived from traditional and learning-based methods. These maps are evaluated based on their density and accuracy, which are critical for effective structure-from-motion reconstruction commonly utilized in navigation for landing. The results demonstrate that machine learning techniques enhance feature detection and matching, providing more intricate representations of environments with sparse features. This improvement indicates a significant potential for machine learning to boost hazard detection and avoidance in space exploration and other complex applications.

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Section: Jetson Tx2 Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Spacecraft and Cameramentioning

confidence: 99%

Section: Hda Sfm and Slope Estimationmentioning

confidence: 99%

Section: Feature Matching Testing On a Desktop Computermentioning

confidence: 99%

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