Evaluation of Photogrammetry for Use in Industrial Production Systems

Li, Jason; Berglund, Jonatan; Auris, Felix; Hanna, Atieh; Vallhagen, Johan; Åkesson, Knut

doi:10.1109/coase.2018.8560496

Cited by 7 publications

(2 citation statements)

References 14 publications

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“…This method not only capture the geometrical information of the object but also carries the object texture information [14] , however, the scanned object surface is required to have a relatively rough texture with various features patterns. Nevertheless if the object texture is transparent, highly reflective or shiny, it would lead to failing the process of images stitching due to lack of visual features [15] . Another factor of photogrammetry precision is environment illumination since the overall method is completely depends on optical sensory only [13] .…”

Section: D Image Reconstruction Methodsmentioning

confidence: 99%

3D Reconstruction of Fruit Shape based on Vision and Edge Sections

Kamarudin

Lam

Azmi

et al. 2022

j. of electron. & inf. syst.

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The fruit industry has been known as one of the largest businesses in Malaysia, where most of the fruits pass through the peeling process well in advance before the final product as juice in a bottle or slices in a can. The current industrial fruit peeling techniques are passive and inefficient by cutting parts of the pulp of the fruit with peels leading to losses. To avoid this issue, a multi-axis CNC fruit peeler can be used to precisely peel the outer layer with the guidance of a 3D virtual model of fruit. In this work, a new cost-effective method of 3D image reconstruction was developed to convert 36 fruit images captured by a normal RGB camera to a 3D model by capturing a single image every 10 degrees of fruit rotation along a fixed axis. The point cloud data extracted with edge detection were passed to Blender 3D software for meshing in different approaches. The vertical link frame meshing method developed in this research proved a qualitative similarity between the output result and the scanned fruit in a processing time of less than 50 seconds.

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Section: D Image Reconstruction Methodsmentioning

confidence: 99%

3D Reconstruction of Fruit Shape based on Vision and Edge Sections

Kamarudin

Lam

Azmi

et al. 2022

j. of electron. & inf. syst.

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“…Additional applications of photogrammetry technology in manufacturing layout design mainly focus on the construction or reconstruction of the existing layout. Regarding inspection application in industrial production systems, instead of using a laser scanner, in [26], photogrammetry techniques were applied to update their digital twin model of the work cell. Similarly, in [27], the manufacturing layout redesign is studied with three-dimensional scanning.…”

Section: Related Workmentioning

confidence: 99%

Deep Dynamic Layout Optimization of Photogrammetry Camera Position Based on Digital Twin

Wang,

Kendall

et al. 2024

IEEE Trans. Automat. Sci. Eng.

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The photogrammetry system has been widely used in industrial manufacturing applications, such as high-precision assembly, reverse engineering and additive manufacturing. In order to meet the demand of the product variety and short product lifecycle, the factory facilities including photogrammetry devices, should be relocated in response to rapid change in mechanical structure and hardware integration. Nevertheless, the camera position of the photogrammetry system is difficult to select to guarantee an optimal field of view (FoV) coverage of retro-reflective targets during the whole production horizon. Especially in a reconfigurable manufacturing work cell, scaling and calibration of a photogrammetry system requires professional skills and these would cost tremendous labour for rapid configuration each time. In this paper, we propose a novel deep optimisation framework for the photogrammetry camera position for the dynamic layout design based on digital twin. The optimisation framework follows an effective coarse-to-fine procedure to evaluate the FoV visibility over the target frame. In addition, the deep Q-learning algorithm is utilised to find the maximum FoV coverage and avoid collision. Three experiments are implemented to verify the application feasibility of the proposed deep camera position optimisation framework.Note to Practitioners-Large-volume in-process metrology is an essential element in flexible manufacturing systems. Quality of large-volume measurement relies heavily on target visibility within its field of view. In a compact industrial robotic cell, this is extremely challenging as the robot would take the primary position and causing view blockage throughout its operation. This makes the simultaneous monitoring of robot head and the work piece key feature extremely difficult. Manual trial-anderror positioning approach is lengthy and requires high level of expertise, due to both safety and spatial concerns. We approached this problem by simulating the camera's view in a digital twin environment and maximising the target visibility throughout the full operation cycle. The generic framework can provide guidance in metrology setup within automated manufacturing environment, accelerate the system commissioning time, remove dependency of skill level and expand the capability for flexible/reconfigurable manufacturing systems. Although V-STARS camera are used in this application, the framework can be applied for other types of vision systems that requires field of view.

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