Vision-based bin-picking: recognition and localization of multiple complex objects using simple visual cues

Rahardja, K.; Kosaka, Akio

doi:10.1109/iros.1996.569005

Cited by 102 publications

(44 citation statements)

References 11 publications

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“…기존의 빈-피킹 기술은 스테레오 카메라 [2,3] 나 레이저, 적외선 Quad-pinch 0.2312 Table 4 Grasp posture identification using cylinder type objects …”

Section: Article Info Abstractunclassified

Method of Object Identification Using Joint Data of Multi-Joint Robotic Gripper for Bin-picking

Park¹,

Park²,

Park³

et al. 2016

Journal of The Korean Society of Manufacturing Technology Engin

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ARTICLE INFO ABSTRACTArticle history:In this study, we propose an object identification method for bin-picking developed for industrial robots. We identify the grasp posture and the associated geometric parameters of grasp objects using the joint data of a robotic gripper.Prior to grasp identification, we analyze the grasping motion in a low-dimensional space using principle component analysis (PCA) to reduce the dimensions. We collected the joint data from a human hand to demonstrate the grasp-identification algorithm. For data acquisition of the human grasp data, we conducted additional research on the motion characteristics of a human hand. We explain the method for using the algorithm of grasp identification for bin-picking. Finally, we present a subject for future research using our proposed algorithm of grasp model and identification.

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“…기존의 빈-피킹 기술은 스테레오 카메라 [2,3] 나 레이저, 적외선 Quad-pinch 0.2312 Table 4 Grasp posture identification using cylinder type objects …”

Section: Article Info Abstractunclassified

Method of Object Identification Using Joint Data of Multi-Joint Robotic Gripper for Bin-picking

Park¹,

Park²,

Park³

et al. 2016

Journal of The Korean Society of Manufacturing Technology Engin

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“…If parts are well organized in the bins, a robot can pick them with a precision which is good enough for the assembly process. However, it is a costly and time consuming process [1] to collect and organize parts for such assembly robots.…”

Section: Introductionmentioning

confidence: 99%

“…An alternative is Bin Picking [1], where the assembly robot is able to pick up parts that are randomly distributed in a bin. In the bin the parts are possibly on top of each other, they have random positions and orientations.…”

Section: Introductionmentioning

confidence: 99%

Fast visual part inspection for bin picking

Rofalis

Olesen

Jakobsen

et al. 2016

2016 IEEE International Conference on Imaging Systems and Techniques (IST)

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Abstract-In this paper we present a novel 3D sensing approach for industrial bin-picking applications that is low-cost, fast and precise. The system uses a simple laser-line emitter. While a robot arm moves the object through the laser light, a synchronized camera captures the laser line image on the object. A full point cloud as well as an edge point cloud suitable for subsequent pose estimation is generated by the developed system. The aim of this work is to deliver an accurate point cloud based on which an object pose can be generated to support a manufacturing robot to deliver an object with high precision. The experimental evaluation of our system shows robust and accurate scanning results.

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“…Removing individual parts from unordered piles in boxes-bin picking-is one of the classical problems of robotics research [1][2][3]. So far, bin picking robots have been stationary.…”

Section: Introductionmentioning

confidence: 99%

Active Recognition and Manipulation for Mobile Robot Bin Picking

Holz

Nieuwenhuisen

Droeschel

et al. 2014

Springer Tracts in Advanced Robotics

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Abstract. Grasping individual objects from an unordered pile in a box has been investigated in stationary scenarios so far. In this work, we present a complete system including active object perception and grasp planning for bin picking with a mobile robot. At the core of our approach is an efficient representation of objects as compounds of simple shape and contour primitives. This representation is used for both robust object perception and efficient grasp planning. For being able to manipulate previously unknown objects, we learn object models from single scans in an offline phase. During operation, objects are detected in the scene using a particularly robust probabilistic graph matching. To cope with severe occlusions we employ active perception considering not only previously unseen volume but also outcomes of primitive and object detection. The combination of shape and contour primitives makes our object perception approach particularly robust even in the presence of noise, occlusions, and missing information. For grasp planning, we efficiently pre-compute possible grasps directly on the learned object models. During operation, grasps and arm motions are planned in an efficient local multiresolution height map. All components are integrated and evaluated in a bin picking and part delivery task.

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Vision-based bin-picking: recognition and localization of multiple complex objects using simple visual cues

Cited by 102 publications

References 11 publications

Method of Object Identification Using Joint Data of Multi-Joint Robotic Gripper for Bin-picking

Method of Object Identification Using Joint Data of Multi-Joint Robotic Gripper for Bin-picking

Fast visual part inspection for bin picking

Active Recognition and Manipulation for Mobile Robot Bin Picking

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