Robots that detect changes in the environment can attain better context awareness and increased autonomy. In this work, a spatial change detection approach is presented which uses a single fixed depth camera to identify environment changes caused by human activities. The proposed method combines hand tracking and the difference between organized point clouds. Bimanual movements are recorded in real-time and encoded in Gaussian Mixture Models (GMMs). We show that GMMs enable change detection in presence of occlusions. We also show that the GMM analysis narrows down potential salient regions of space where manipulation actions are carried out. Experiments have been performed in an indoor environment for object placement, object removal and object repositioning tasks.

GMM-based spatial change detection from bimanual tracking and point cloud differences / Monica, Riccardo; Zinelli, Andrea; Aleotti, Jacopo. - 1834:(2017), pp. 26-30. ((Intervento presentato al convegno 3rd Italian Workshop on Artificial Intelligence and Robotics, AIRO 2016 tenutosi a ita nel 2016.

GMM-based spatial change detection from bimanual tracking and point cloud differences

Monica, Riccardo;ZINELLI, ANDREA;Aleotti, Jacopo
2017

Abstract

Robots that detect changes in the environment can attain better context awareness and increased autonomy. In this work, a spatial change detection approach is presented which uses a single fixed depth camera to identify environment changes caused by human activities. The proposed method combines hand tracking and the difference between organized point clouds. Bimanual movements are recorded in real-time and encoded in Gaussian Mixture Models (GMMs). We show that GMMs enable change detection in presence of occlusions. We also show that the GMM analysis narrows down potential salient regions of space where manipulation actions are carried out. Experiments have been performed in an indoor environment for object placement, object removal and object repositioning tasks.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11381/2837926
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact