Unsupervised Change Detection for Space Habitats Using 3D Point Clouds
Paper i proceeding, 2024

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 beuncre wed 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 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. The algorithm is validated quantitatively and qualitatively using a test dataset collected by an A strobee robot in the NASA Ames Granite Lab comprising single frame depth images taken directly by Astrobee and full-scene reconstructed maps built with RGB-Dand pose data from Astrobee. The runtimes of the approach are also analyzed in depth. The source code is publicly released to promote further development.


Jamie Santos

Student vid Chalmers

Holly Dinkel

The Grainger College of Engineering

Julia Di

Stanford Engineering

Paulo V.K. Borges

Commonwealth Scientific and Industrial Research Organisation (CSIRO)

Marina Moreira

NASA Ames Research Center

Oleg Alexandrov

NASA Ames Research Center

Brian Coltin

NASA Ames Research Center

Trey Smith

NASA Ames Research Center

AIAA SciTech Forum and Exposition, 2024

9781624107115 (ISBN)

AIAA SciTech Forum and Exposition, 2024
Orlando, USA,


Robotteknik och automation

Datorseende och robotik (autonoma system)



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