The task of searching and grasping objects in cluttered scenes, typical of robotic applications in domestic environments requires fast object detection and segmentation. Attentional mechanisms provide a means to detect and prioritize processing of objects of interest. In this work, we combine a saliency operator based on symmetry with a segmentation method based on clustering locally planar surface patches, both operating on 2.5D point clouds (RGB-D images) as input data to yield a novel approach to table-top scene segmentation. Evaluation on indoor table-top scenes containing man-made objects clustered in piles and dumped in a box show that our approach to selection of attention points significantly improves performance of state-of-the-art attention-based segmentation methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.