3D Object Detection and Recognition for Robotic Grasping Based on RGB-D Images and Global Features

Czajewski, Witold; Kołomyjec, Krzysztof

doi:10.1515/fcds-2017-0011

Cited by 16 publications

(4 citation statements)

References 35 publications

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“…The third group of experimental images contained two target crops that were occluded. In the case of ensuring that the object is at the effective measuring distance of the camera [25,26], try to make the background of each picture clear and concise, which can facilitate the reading of experimental data. Therefore, it is reasonable to crop the pictures that have a cluttered background and where it is not easy to see the 3D coordinate values, and intercept the parts of the figure that contain the coordinates with clear values.…”

Section: Experimental Process and Analysis Of Resultsmentioning

confidence: 99%

Extraction and Research of Crop Feature Points Based on Computer Vision

Cui

Zhang

Sun

et al. 2019

Sensors

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Based on computer vision technology, this paper proposes a method for identifying and locating crops in order to successfully capture crops in the process of automatic crop picking. This method innovatively combines the YOLOv3 algorithm under the DarkNet framework with the point cloud image coordinate matching method, and can achieve the goal of this paper very well. Firstly, RGB (RGB is the color representing the three channels of red, green and blue) images and depth images are obtained by using the Kinect v2 depth camera. Secondly, the YOLOv3 algorithm is used to identify the various types of target crops in the RGB images, and the feature points of the target crops are determined. Finally, the 3D coordinates of the feature points are displayed on the point cloud images. Compared with other methods, this method of crop identification has high accuracy and small positioning error, which lays a good foundation for the subsequent harvesting of crops using mechanical arms. In summary, the method used in this paper can be considered effective.

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Section: Experimental Process and Analysis Of Resultsmentioning

confidence: 99%

Extraction and Research of Crop Feature Points Based on Computer Vision

Cui

Zhang

Sun

et al. 2019

Sensors

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“…The multiplication between the transformation matrix and the intrinsic matrix is the projection matrix (Proj), which has the 3 x 4 matrix dimension. The formulation is in (5).…”

Section: ) Projection Matrixmentioning

confidence: 99%

“…However, it was shown that the average error is more than 4 mm. Czajewski et al also deployed the Microsoft Kinect sensor to perform the point cloud matching method [5]. Viewpoint Feature Histogram (VFH) and Camera's Roll Histogram (CRH) descriptors matching continued by Iterative Closest Point (ICP) and Hypotheses Verification (HV) algorithms were used in this approach.…”

Section: Introductionmentioning

confidence: 99%

3D Object Pose Estimation Using Chamfer Matching and Flexible CAD File Base

Sari¹,

Putranti²

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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Estimating the object pose is an interesting topic in the industrial robotic vision field. By having an accurate result for detecting object pose, it means the system performs the task as the target in the bin-picking technique. The methods which are developed are varies widely. But the challenge for this paper is estimating a 3D object using mono camera accurately. The object which is used in this paper has the symmetric rotational shape, in this case is the sprayer. In this paper, the camera uses a tool from the Blender Software, such that the ground truth is measurable and it will be the reference for calculating the error. The applied algorithms of this paper are Border Line Extraction Algorithm utilized in the template generation step as the reference template, Directional Chamfer Matching for detecting the coarse pose, and Lavenberg-Marquardt Method to optimize the object pose result. The result achieves the average error of the coarse pose for x and y position (translation pose) are 2.05 mm and 0.71 mm. Meanwhile for the optimized pose, the average error for x and y position (translation pose) are 1.82 mm and 0.24 mm. Regarding the rotational pose, the average error of the rotational coarse pose with respect to x and z axis are 0.01 degree and 0.45 degree. Whereas the average error of the rotational optimized pose with respect to x and z axis are 2.88 degree and 0.82 degree.

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“…Some use algorithms for control [ 13 ] and others use visual images and machine learning to allow robotic arms to perform tasks [ 14 , 15 ]. Other studies convert images into point clouds that use depth distances for better task execution [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. For example, the study in [ 25 ] combines object recognition by Mobile-DasNet and point cloud analysis to generate the coordinates of the arm endpoints for the apple picking task.…”

Section: Introductionmentioning

confidence: 99%

Eye-in-Hand Robotic Arm Gripping System Based on Machine Learning and State Delay Optimization

Chen

2023

Sensors

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This research focused on using RGB-D images and modifying an existing machine learning network architecture to generate predictions of the location of successfully grasped objects and to optimize the control system for state delays. A five-finger gripper designed to mimic the human palm was tested to demonstrate that it can perform more delicate missions than many two- or three-finger grippers. Experiments were conducted using the 6-DOF robot arm with the five-finger and two-finger grippers to perform at least 100 actual machine grasps, and compared to the results of other studies. Additionally, we investigated state time delays and proposed a control method for a robot manipulator. Many studies on time-delay systems have been conducted, but most focus on input and output delays. One reason for this emphasis is that input and output delays are the most commonly occurring delays in physical or electronic systems. An additional reason is that state delays increase the complexity of the overall control system. Finally, it was demonstrated that our network can perform as well as a deep network architecture with little training data and omitting steps, such as posture evaluation, and when combined with the hardware advantages of the five-finger gripper, it can produce an automated system with a gripping success rate of over 90%. This paper is an extended study of the conference paper.

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3D Object Detection and Recognition for Robotic Grasping Based on RGB-D Images and Global Features

Cited by 16 publications

References 35 publications

Extraction and Research of Crop Feature Points Based on Computer Vision

Extraction and Research of Crop Feature Points Based on Computer Vision

3D Object Pose Estimation Using Chamfer Matching and Flexible CAD File Base

Eye-in-Hand Robotic Arm Gripping System Based on Machine Learning and State Delay Optimization

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