Unsupervised Change Detection for Space Habitats Using 3D Point Clouds

Abstract

This work presents an algorithm for scene change detection from point clouds to enable autonomous robotic caretaking in future space habitats. Autonomous robotic systems will help maintain future deep-space habitats, such as the Gateway space station, which will be uncrewed for extended periods. Existing scene analysis software used on the International Space Station (ISS) relies on manually-labeled images for detecting changes. In contrast, the algorithm presented in this work uses raw, unlabeled point clouds as inputs. The algorithm first applies modified Expectation-Maximization Gaussian Mixture Model (GMM) clustering to two input point clouds. It then performs change detection by comparing the GMMs using the Earth Mover’s Distance. Experiments on data collected in a ground environment replicating the visual features of the ISS with an Astrobee free-flyer at NASA Ames Research Center demonstrate detection of multiple appearing and disappearing objects. The source code is publicly released to promote further development.