Valtreyak_Spryzen_Abstract.txt

Lunar Navigation: Safe lunar navigation, critical for the success of lunar missions, involves several advanced techniques. This comprehensive approach includes generating high-resolution hazard maps, employing super-resolution methods, detecting craters, matching crater patterns, and performing visual terrain relative navigation. Super-resolution involves using Keras and GANs to enhance low-resolution images taken by terrain mapping cameras, providing a clearer view of the lunar surface. Crater detection employs the Ellipse R-CNN model, combining object retrieval and occlusion pattern recognition to identify hazards. Crater matching aids in recognizing recurring patterns, enhancing hazard prediction. Visual terrain relative navigation focuses on identifying lunar surface features, classifying terrain using DenseNet, and estimating depth with Pix2Pix and GANs. Challenges include training feature-based transforms, accounting for varying lunar lighting conditions, ensuring regular updates in the dynamic lunar environment, and enhancing image resolution from terrain relative cameras. This multifaceted lunar navigation system relies on deep learning models, substantial computational resources, fine-tuned generative models, and advanced image processing. It is integral for achieving safe lunar landings, advancing lunar exploration, and scientific endeavors.