Pose estimation of rigid transparent objects in transparent clutter

Lysenkov, Ilya; Rabaud, Vincent

doi:10.1109/icra.2013.6630571

Cited by 50 publications

(17 citation statements)

References 20 publications

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“…Both methods assume that the background has sufficient texture. Albrecht et al [1] and Lysenkov et al [14] use the observation that NIR structured light cameras are not able to produce depth data for transparent and most reflective materials, as the light is scattered away. Their approaches require a prior full 3D model.…”

Section: Related Workmentioning

confidence: 99%

On Recognizing Transparent Objects in Domestic Environments Using Fusion of Multiple Sensor Modalities

Hagg

Hegger

Plöger

2017

RoboCup 2016: Robot World Cup XX

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Abstract. Current object recognition methods fail on object sets that include both diffuse, reflective and transparent materials, although they are very common in domestic scenarios. We show that a combination of cues from multiple sensor modalities, including specular reflectance and unavailable depth information, allows us to capture a larger subset of household objects by extending a state of the art object recognition method. This leads to a significant increase in robustness of recognition over a larger set of commonly used objects.

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Section: Related Workmentioning

confidence: 99%

On Recognizing Transparent Objects in Domestic Environments Using Fusion of Multiple Sensor Modalities

Hagg

Hegger

Plöger

2017

RoboCup 2016: Robot World Cup XX

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show abstract

“…disparity holes for transparent, shiny or matte and absorbing objects, sunlight interference) [24]- [25]. These drawbacks can be reduced by proper techniques and algorithms, which usually exploit the large amount of information that it is possible to obtain through the devices by data fusion (e.g., multiple IR camera configurations and RGB-Depth data fusion) [26], [27], [28].…”

Section: A Low-cost I Autonomentioning

confidence: 99%

A low-cost indoor and outdoor terrestrial autonomous navigation model

Susi¹,

Cristini²,

Salerno³

et al. 2014

2014 22nd Telecommunications Forum Telfor (TELFOR)

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“…The problem of performing grasping in transparent clutter is complicated by the fact that robots cannot perceive and describe the transparent surfaces correctly. Several previous methods [14], [15] tried to approach this problem by finding invalid values in depth observation, but they were limited to top-down grasping and made assumption that target objects establish distinguishable contour (formed by invalid points) in depth map. Recently, several approaches employed light Z.…”

Section: Introductionmentioning

confidence: 99%

GlassLoc: Plenoptic Grasp Pose Detection in Transparent Clutter

Zhou

Pan

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Transparent objects are prevalent across many environments of interest for dexterous robotic manipulation. Such transparent material leads to considerable uncertainty for robot perception and manipulation, and remains an open challenge for robotics. This problem is exacerbated when multiple transparent objects cluster into piles of clutter. In household environments, for example, it is common to encounter piles of glassware in kitchens, dining rooms, and reception areas, which are essentially invisible to modern robots. We present the GlassLoc algorithm for grasp pose detection of transparent objects in transparent clutter using plenoptic sensing. GlassLoc classifies graspable locations in space informed by a Depth Likelihood Volume (DLV) descriptor. We extend the DLV to infer the occupancy of transparent objects over a given space from multiple plenoptic viewpoints. We demonstrate and evaluate the GlassLoc algorithm on a Michigan Progress Fetch mounted with a first generation Lytro. The effectiveness of our algorithm is evaluated through experiments for grasp detection and execution with a variety of transparent glassware in minor clutter.

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Pose estimation of rigid transparent objects in transparent clutter

Cited by 50 publications

References 20 publications

On Recognizing Transparent Objects in Domestic Environments Using Fusion of Multiple Sensor Modalities

On Recognizing Transparent Objects in Domestic Environments Using Fusion of Multiple Sensor Modalities

A low-cost indoor and outdoor terrestrial autonomous navigation model

GlassLoc: Plenoptic Grasp Pose Detection in Transparent Clutter

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