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

Sensors
Jiatong BaoNing Xi

Abstract

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.

References

Oct 6, 2004·IEEE Transactions on Pattern Analysis and Machine Intelligence·David R MartinJitendra Malik
Jan 17, 2009·Journal of Vision·Lingyun ZhangGarrison W Cottrell
Sep 18, 2009·Journal of Vision·Neil D B Bruce, John K Tsotsos
Jul 27, 2011·IEEE Transactions on Pattern Analysis and Machine Intelligence· Xiaodi HouChristof Koch
Dec 28, 2011·IEEE Transactions on Pattern Analysis and Machine Intelligence·Stas GofermanAyellet Tal
Sep 10, 2015·IEEE Transactions on Pattern Analysis and Machine Intelligence·Ming-Ming ChengShi-Min Hu

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Citations

May 14, 2016·Sensors·Kourosh Khoshelham, Sisi Zlatanova

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