Computer Vision for Terrain Mapping and 3D Printing In-situ of Extra/-terrestrial Habitats

This paper addresses the complexities inherent in constructing sustainable extraterrestrial habitats within lava tubes that are envisioned as promising locations for human habitation and scientific inquiry. These environments are characterized by various challenges, which are addressed in this case by integrating computer vision (CV) techniques and 3D printing in-situ. The CV component generates a detailed depth map from synthetic imagery to combine this depth map with an adaptive 3D printing process, which is proposed to ensure level surfaces at various depths, facilitating precise foundation and habitat placement within the demanding context of lava tubes. Significantly, synthetic imagery is employed due to the absence of real lava tube photos at this early stage of the current exploration. The focal point lies in utilizing advanced deep learning (DL) algorithms and convolutional neural networks (CNN) to generate depth maps for extra/-terrestrial environments. This research represents a platform for further knowledge development in the fields of CV and its application to 3D printing in-situ, hence opening new avenues for sustainable extraterrestrial habitats.