A flexible 3D object localization system for industrial part handling

Skotheim, Øystein; Lind, Morten; Ystgaard, Pål; Fjerdingen, Sigurd Aksnes

doi:10.1109/iros.2012.6385508

Cited by 41 publications

(27 citation statements)

References 15 publications

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“…An example of result is shown in Figure 4 where scattered parts are recognized 8 out of 9. The theory behind the algorithms is further described in [4]. Further work includes speeding up the matching process.…”

Section: D Localizationmentioning

confidence: 99%

Development of robotic solutions for oil/gas, aluminum and manufacturing industry

Schjølberg

2012

Proceedings of 2012 IEEE 17th International Conference on Emerging Technologies &Amp; Factory Automation (ETFA 2012)

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Future robotics solutions will allow for larger degree of autonomy, easy re-programming and build on sensor based control. Current state-of-the-art technologies in the field of robotics are not sufficient for automating a number of today's manual manipulation tasks. The objective of this paper is to describe the main approach of the project Next Generation Robotics for Norwegian Industry (NextGenRob), contributions and preliminary results developed so far to face the current challenges imposed to the industrial project partners whom represent oil/gas, aluminum and manufacturing industry. The project is a joint effort between industry and academia to develop enabling robotic technology and solutions. A number of demonstrators have been developed and are shortly presented in this paper.

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Section: D Localizationmentioning

confidence: 99%

Development of robotic solutions for oil/gas, aluminum and manufacturing industry

Schjølberg

2012

Proceedings of 2012 IEEE 17th International Conference on Emerging Technologies &Amp; Factory Automation (ETFA 2012)

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“…Biegelbauer and Vincze [12] 3D measured the surface, segmented the range image and fitted a cylinder to detect the actual position of the bore. A system that scans and localizes the workpieces in 3D for assembly and pick-and-place operations was presented by Skotheim et al [13]. Rajaraman et al [14] developed a laser-scanner-based localization system for automatic robot welding.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Robotic Deburring of Die-Cast Parts with Position and Orientation Measurements Using a 3D Laser-Triangulation Sensor

Kosler¹,

Pavlovčič²,

Jezeršek³

et al. 2016

Stroj. vest. - Jour. Mech. Eng.

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“…al. generated workpieces databaseby using 3D CAD model, and a Hough-like voting scheme was set up to recognize three different components [7]. In [8], a method was proposed to pick up the earliest feasible point of moving objects using a search-based algorithm.…”

Section: Introductionmentioning

confidence: 99%

CAD-based pose estimation for random bin-picking of multiple objects using a RGB-D camera

Jiang

Song

2015

2015 15th International Conference on Control, Automation and Systems (ICCAS)

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In this paper, we propose a CAD-based 6-DOF pose estimation design for random bin-picking of multiple different objects using a Kinect RGB-D sensor. 3D CAD models of objects are constructed via a virtual camera, which generates a point cloud database for object recognition and pose estimation. A voxel grid filter is suggested to reduce the number of 3D point cloud of objects for reducing computing time of pose estimation. A voting-scheme method was adopted for the 6-DOF pose estimation a swell as object recognition of different type objects in the bin. Furthermore, an outlier filter is designed to filter out bad matching poses and occluded ones, so that the robot arm always picks up the upper object in the bin to increase pick up success rate. A series of experiments on a Kuka 6-axis robot revels that the proposed system works satisfactorily to pick up all random objects in the bin. The average recognition rate of three different type objects is 93.9% and the pickup success rate is 89.7%.

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A flexible 3D object localization system for industrial part handling

Cited by 41 publications

References 15 publications

Development of robotic solutions for oil/gas, aluminum and manufacturing industry

Development of robotic solutions for oil/gas, aluminum and manufacturing industry

Adaptive Robotic Deburring of Die-Cast Parts with Position and Orientation Measurements Using a 3D Laser-Triangulation Sensor

CAD-based pose estimation for random bin-picking of multiple objects using a RGB-D camera

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