Saliency-Guided Detection of Unknown Objects in RGB-D Indoor Scenes

Sensors

Jiatong Bao¹  Yunyi Jia²  Yu Cheng²  Ning Xi²  Kourosh Khoshelham³ 

[1] Department of Hydraulic, Energy and Power Engineering, Yangzhou University, Yangzhou 225000, China;Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA; E-Mails:;id="af1-sensors-15-21054">1Department of Hydraulic, Energy and Power Engineering, Yangzhou University, Yangzhou 225000, Chi

关键词: unknown object detection;    saliency detection;    RGB-D object segmentation;   

DOI  :  10.3390/s150921054

来源: mdpi

【 摘 要 】

This paper studies the problem of detecting unknown objects within indoor environments in an active and natural manner. The visual saliency scheme utilizing both color and depth cues is proposed to arouse the interests of the machine system for detecting unknown objects at salient positions in a 3D scene. The 3D points at the salient positions are selected as seed points for generating object hypotheses using the 3D shape. We perform multi-class labeling on a Markov random field (MRF) over the voxels of the 3D scene, combining cues from object hypotheses and 3D shape. The results from MRF are further refined by merging the labeled objects, which are spatially connected and have high correlation between color histograms. Quantitative and qualitative evaluations on two benchmark RGB-D datasets illustrate the advantages of the proposed method. The experiments of object detection and manipulation performed on a mobile manipulator validate its effectiveness and practicability in robotic applications.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.