3D点群を用いた宇宙居住空間のための教師なし変化検出
Unsupervised Change Detection for Space Habitats Using 3D Point Clouds
ラベルなしの生の3D点群からGMMクラスタリングとEarth Mover's Distanceでシーン変化を検出する教師なし手法を提案し、Astrobeeロボットのデータで検証した。
著者: Jamie Santos, Holly Dinkel, Julia Di, Paulo V. K. Borges, Marina Moreira, Oleg Alexandrov, Brian Coltin, Trey Smith
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
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. The algorithm is validated quantitatively and qualitatively using a test dataset collected by an Astrobee robot in the NASA Ames Granite Lab comprising single frame depth images taken directly by Astrobee and full-scene reconstructed maps built with RGB-D and pose data from Astrobee. The runtimes of the approach are also analyzed in depth. The source code is publicly released to promote further development.
