GMM-based Spatial Change Detection from Bimanual Tracking and Point Cloud Differences

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.