Detecting Changes on the ISS Autonomously with 3D Point Clouds: An Unsupervised Learning Approach Using GMM Clustering
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Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2023
Författare
Santos, Jamie
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
New space habitats, such as the Lunar Gateway and subsequent stations on Mars, will be increasingly difficult
to continuously staff with astronauts, necessitating robotic support in the absence of humans. In this work, an
unsupervised change detection algorithm requiring only 3D depth data as input is proposed in order to enable
autonomous robotic caretaking. Scene change detection is necessary for robotic caretakers to accomplish many
routine tasks, such as map updating and surveillance of the habitat. Upon the International Space Station (ISS),
Astrobee is one such robot used for development and demonstration of these technologies under the Integrated
System for Adaptive Autonomous Caretaking (ISAAC) project. Current scene analysis developed within the
ISAAC project uses semantic localization to detect manually labeled objects within the map. However, this
is not a sustainable approach for generalized change detection, as thousands of images must be captured
and labeled for accurate results. In contrast, the proposed algorithm uses an unsupervised GMM clustering
algorithm to compare “before” and “after” point clouds of the scene, and is therefore capable of detecting
changes in the scene without being restricted to manually labeled objects. Experiments with data collected at
NASA Ames’ Granite Lab, a mock-up of the ISS, successfully demonstrate the detection of one or more object
appearances or disappearances in the scene with an initial average F1 score of 74% for volumetric reconstructed
maps of the scene with two and three changed objects, and 70% for the comparison of single frames from the
depth camera with one object. With a number of optimizations that can be made to improve the accuracy, the
source code is released to the public to promote further research and development.
Beskrivning
Ämne/nyckelord
robotics , Gaussian mixture model , earth mover’s distance , change detection , unsupervised learning , expectation maximization , point cloud , ISS