The Influence of Autofocus Lenses in the Camera Calibration Process

Ricolfe-Viala, Carlos; Esparza, Alicia

doi:10.1109/tim.2021.3055793

Cited by 14 publications

(5 citation statements)

References 33 publications

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“… Camera obscura modelling Improvement in model accuracy Eye camera obscura model augmented with geometric algebra to characterize eye position and rotation axes 27 . Camera obscura calibration using planar template images for focal length changes 28 . Combination of a camera obscura model and non-metric and self-calibration methods 29 .…”

Section: Related Studiesmentioning

confidence: 99%

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Industrial camera model positioned on an effector for automated tool center point calibration

Konecny,

Beremlijski,

Bailova

et al. 2024

Sci Rep

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The study presents a novel, full model of an industrial camera suitable for robotic manipulator tool center point (TCP) calibration. The authors propose a new solution which employs a full camera model positioned on the effector of an industrial robotic arm. The proposed full camera model simulates the capture of a calibration pattern for use in automated TCP calibration. The study describes an experimental test robot stand for producing a reference data set, a full camera model, the parameters of a generally known camera obscura model, and a comparison of proposed solution with the camera obscura model. The results are discussed in the context of an innovative approach which features a full camera model to assist the TCP calibration process. The results showed that the full camera model produced greater accuracy, a significant benefit not provided by other state-of-the-art methods. In several cases, the absolute error produced was up to seven times lower than with the state-of-the-art camera obscura model. The error for small rotation (max. of 5$$^\circ $$ ∘ ) and small translation (max. of 20 mm) was 3.65 pixels. The results also highlighted the applicability of the proposed solution in real-life industrial processes.

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Section: Related Studiesmentioning

confidence: 99%

“…Because all the camera parameters are known, any computed results are considered true. However, the camera obscura model does not accurately represent camera behavior if an autofocus device is used 28 . Lens distortion is not a problem with camera obscura models 29 .…”

Section: Related Studiesmentioning

confidence: 99%

Industrial camera model positioned on an effector for automated tool center point calibration

Konecny,

Beremlijski,

Bailova

et al. 2024

Sci Rep

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show abstract

“…To mitigate these challenges, two primary approaches are considered: i) optimization of intrinsic parameters and ii) recognition of 3D objects to estimate the center point. Numerous algorithms have been developed for obtaining intrinsic parameters for imaging sensors [6][7][8]. Intrinsic calibration typically involves a camera observing anchor points on a calibration pattern, with commonly used patterns including checkerboards [9], coplanar circles [10,11], and AprilTags [12].…”

Section: Fig 1: Overview Industrial Robot Vision Systemmentioning

confidence: 99%

M-Calib: A Monocular 3D Object Localization using 2D Estimates for Industrial Robot Vision System

Canh,

Ngoc,

HoangVan

2024

Preprint

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3D Object Localization has been emerging recently as one of the challenges of Machine Vision or Robot Vision tasks. In this paper, we proposed a novel method designed for the localization of isometric flat 3D objects, leveraging a blend of deep learning techniques primarily rooted in object detection, post-image processing algorithms, and pose estimation. Our approach involves the strategic application of 3D calibration methods tailored for low-cost industrial robotics systems, requiring only a single 2D image input. Initially, object detection is performed using the You Only Look Once (YOLO) model, followed by segmentation of the object into two distinct parts— the top face and the remainder— using the Mask R-CNN model. Subsequently, the center of the top face serves as the initialization position and a unique combination of post-processing techniques and a novel calibration algorithm is employed to refine the object’s position. Experimental results demonstrate a notable reduction in localization error by 87.65% when compared to existing methodologies. Furthermore , we have made our code and datasets openly accessible to the public, facilitating further research and collaboration in this domain (https://github. com/NguyenCanhThanh/MonoCalibNet)

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“…Another practically valuable feature that result of the aforementioned capabilities of neuromorphic vision is the ability to perceive under very small aperture, leading to a substantially wide depth of field. In applications such as ours where the camera is expected to acquire information across a varied depth, this feature can alleviate the need of an autofocus system, which often requires additional hardware [57] and induces uncertainty in the camera projection model [58]. Other advantages of neuromoorphic vision include low power consumption and reduction in signal redundancy as only informative data is transmitted in the form of events.…”

Section: Neuromorphic Vision Sensormentioning

confidence: 99%

Neuromorphic Vision Based Control for the Precise Positioning of Robotic Drilling Systems

Ayyad¹,

Halwani²,

Swart³

et al. 2022

Preprint

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The manufacturing industry is currently witnessing a paradigm shift with the unprecedented adoption of industrial robots, and machine vision is a key perception technology that enables these robots to perform precise operations in unstructured environments. However, the sensitivity of conventional vision sensors to lighting conditions and high-speed motion sets a limitation on the reliability and work-rate of production lines. Neuromorphic vision is a recent technology with the potential to address the challenges of conventional vision with its high temporal resolution, low latency, and wide dynamic range. In this paper and for the first time, we propose a novel neuromorphic vision based controller for faster and more reliable machining operations, and present a complete robotic system capable of performing drilling tasks with sub-millimeter accuracy. Our proposed system localizes the target workpiece in 3D using two perception stages that we developed specifically for the asynchronous output of neuromorphic cameras. The first stage performs multi-view reconstruction for an initial estimate of the workpiece's pose, and the second stage refines this estimate for a local region of the workpiece using circular hole detection. The robot then precisely positions the drilling endeffector and drills the target holes on the workpiece using a combined position-based and imagebased visual servoing approach. The proposed solution is validated experimentally for drilling nutplate holes on workpieces placed arbitrarily in an unstructured environment with uncontrolled lighting. Experimental results prove the effectiveness of our solution with an average positional errors of less than 0.1 mm, and demonstrate that the use of neuromorphic vision overcomes the lighting and speed limitations of conventional cameras. The findings of this paper identify neuromorphic vision as a promising technology that can expedite and robustify robotic manufacturing processes in line with the requirements of the fourth industrial revolution.

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The Influence of Autofocus Lenses in the Camera Calibration Process

Cited by 14 publications

References 33 publications

Industrial camera model positioned on an effector for automated tool center point calibration

Industrial camera model positioned on an effector for automated tool center point calibration

M-Calib: A Monocular 3D Object Localization using 2D Estimates for Industrial Robot Vision System

Neuromorphic Vision Based Control for the Precise Positioning of Robotic Drilling Systems

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