3D object detection and pose estimation from depth image for robotic bin picking

Kuo, Hao-Yuan; Su, Hong-Ren; Lai, Shang-Hong; Wu, Chin-Chia

doi:10.1109/coase.2014.6899489

Cited by 43 publications

(20 citation statements)

References 15 publications

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“…Most previous attempts on a systems approach to bin-picking mainly focussed on the perception problem [25,24,23,21,4,20,19,18,16,15,14], while assuming accurate robot grasping. However, model inaccuracies and sensor uncertainties make it difficult for a majority of the perception algorithms to provide reliable object recognition and localization estimates, thereby affecting overall bin-picking performance.…”

Section: Perception For Robotic Bin-pickingmentioning

confidence: 99%

Addressing perception uncertainty induced failure modes in robotic bin-picking

Kaipa

Kankanhalli-Nagendra

Kumbla

et al. 2016

Robotics and Computer-Integrated Manufacturing

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We present a comprehensive approach to handle perception uncertainty to reduce failure rates in robotic bin-picking. Our focus is on mixed-bins. We identify the main failure modes at various stages of the bin-picking task and present methods to recover from them. If uncertainty in part detection leads to perception failure, then human intervention is invoked. Our approach estimates the confidence in the part match provided by an automated perception system, which is used to detect perception failures. Human intervention is also invoked if uncertainty in estimated part location and orientation leads to a singulation planning failure. We have developed a user interface that enables remote human interventions when necessary. Finally, if uncertainty in part posture in the gripper leads to failure in placing the part with the desired accuracy, sensor-less fine-positioning moves are used to correct the final placement errors. We have developed a fine-positioning planner with a suite of fine-motion strategies that offer different tradeoffs between completion time and postural accuracy at the destination. We report our observations from system characterization experiments with a dual-armed Baxter robot, equipped with a Ensenso three-dimensional camera, to perform bin-picking on mixed-bins.

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Section: Perception For Robotic Bin-pickingmentioning

confidence: 99%

Addressing perception uncertainty induced failure modes in robotic bin-picking

Kaipa

Kankanhalli-Nagendra

Kumbla

et al. 2016

Robotics and Computer-Integrated Manufacturing

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“…There are a lot of works done in various areas on the image recognition and detection as in [23][24][25][26][27] studies based on this approach. In these studies, the RoIs around keypoints were analyzed with various pattern recognition and matching algorithms.…”

Section: Keypoint and Feature Extractionmentioning

confidence: 99%

Görsel Sözlük Ve Siniflandirma Yaklaşimlarindan Faydalanarak Di̇yabeti̇k Reti̇nopati̇li̇ Reti̇nal Görüntülerde Sert Eksudalarin Tespi̇ti̇

Akyol

Bayir²,

Şen³

2016

Mugla Journal of Science and Technology

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“…its position and rotation angle of the particular object has to be estimated among other objects, considering various limitations. Many different techniques have been proposed during the last decades [4], [5], [6]. Almost every proposed approach uses some sort of computer vision.…”

Section: Introductionmentioning

confidence: 99%

Bin Picking Success Rate Depending on Sensor Sensitivity

Doležel

Pidanič

Zálabský

et al. 2019

2019 20th International Carpathian Control Conference (ICCC)

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The goal of this contribution is to determine correlation between an applied sensor for object registration and the success rate of the bin-picking problem. In most applications of a bin picking problem in industry, the procedure consists of two consecutive steps. The first step provides an initial guess of both position and rotation angle of the object to be registered, while the second one improves the exact pose accuracy, as required in following tasks. The second step can be, among others, implemented by the Iterative Closest Point Algorithm (ICP). It is well known that the ICP algorithm is very sensitive to the initial guess of the position and rotation angle of the object. Another interesting feature, especially from the technicians point of view, is the sensitivity of the ICP algorithm in relation to the applied sensor. Therefore, one particular bin picking application, involving complex irregular objects, is examined in this paper. Various kinds of sensors for 3D scene reconstruction are employed and, as a result of this contribution, a comprehensive set of relations between sensor quality and the ICP algorithm sensitivity is formulated.

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3D object detection and pose estimation from depth image for robotic bin picking

Cited by 43 publications

References 15 publications

Addressing perception uncertainty induced failure modes in robotic bin-picking

Addressing perception uncertainty induced failure modes in robotic bin-picking

Görsel Sözlük Ve Siniflandirma Yaklaşimlarindan Faydalanarak Di̇yabeti̇k Reti̇nopati̇li̇ Reti̇nal Görüntülerde Sert Eksudalarin Tespi̇ti̇

Bin Picking Success Rate Depending on Sensor Sensitivity

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