Bin-Picking in the Industry 4.0 Era

Kyprianou, Georgios; Doitsidis, Lefteris; Kapoutsis, Athanasios Ch.; Zinonos, Zinon; Chatzichristofis, Savvas A.

doi:10.1109/icce56470.2023.10043452

Cited by 2 publications

(3 citation statements)

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“…Yellow points mark identified outliers, black points are the remaining datapoints. A plane drawn in grey is added at z = z thr , where z thr = z max − d max and z max is the coordinate of the highest point in ∼ P n , as in (2). Thus, the location of the plane corresponds to the last z-cut Z d processed in a loop by the new procedure, as explained in the flowchart in Figure 7.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Robotic bin-picking is a common task in manufacturing [1]. Its objective is to pick a single part from an unstructured pile in a box, as shown in Figure 1, and make the part available for the next step of the automated process [2]. High levels of autonomy and easily reconfigurable work cells are needed to achieve a smooth flow of the production lines.…”

Section: Introductionmentioning

confidence: 99%

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Filtering Organized 3D Point Clouds for Bin Picking Applications

Franaszek,

Rachakonda,

Saidi

2024

Applied Sciences

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In robotic bin-picking applications, autonomous robot action is guided by a perception system integrated with the robot. Unfortunately, many perception systems output data contaminated by spurious points that have no correspondence to the real physical objects. Such spurious points in 3D data are the outliers that may spoil obstacle avoidance planning executed by the robot controller and impede the segmentation of individual parts in the bin. Thus, they need to be removed. Many outlier removal procedures have been proposed that work very well on unorganized 3D point clouds acquired for different, mostly outdoor, scenarios, but these usually do not transfer well to the manufacturing domain. This paper presents a new filtering technique specifically designed to deal with the organized 3D point cloud acquired from a cluttered scene, which is typical for a bin-picking task. The new procedure was tested on six different datasets (bins filled with different parts) and its performance was compared with the generic statistical outlier removal procedure. The new method outperforms the general procedure in terms of filtering efficacy, especially on datasets heavily contaminated by numerous outliers.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%