Research on the Industrial Robot Grasping Method Based on Multisensor Data Fusion and Binocular Vision

Xie, Shangyu

doi:10.1155/2022/4443100

Cited by 5 publications

(1 citation statement)

References 19 publications

(18 reference statements)

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“…6D object pose estimation is one of the important tasks when working with robots. It can be used in a variety of important scenarios such as robotic grasping [1] [2], autonomous driving [3] and so on.…”

Section: Introductionmentioning

confidence: 99%

DON6D: Decoupled One-stage Network for 6D Pose Estimation

Wang,

Tu,

Qian

et al. 2023

Preprint

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6D pose object estimation is a key task in robotic manipulation and grasping scenes. Many prior two-stage works with slower inference speeds require extra refinement to deal with challenge problems such as variations of lighting, sensor noise, object occlusion, and truncation. To address these problems, this work proposes a decoupled one-stage network (DON6D) for 6D pose estimation, which improves inference speed, on the premise of maintaining accuracy. Specifically, since RGB images are aligned with RGB-D images, DON6D first utilizes a 2D detection network to locate the interested objects in RGB-D images. Second, DON6D includes a module of feature extraction and fusion to fully extract color and geometric features. Furthermore, dual data augmentation is implemented to enhance the generalizability of the model. Third, DON6D fuses them and applies an attention residual encoder-decoder which can boost pose estimation performance to obtain accurate 6D pose. DON6D is a real-time method that is evaluated on the LINEMOD dataset and YCB-Video dataset. The results demonstrate that DON6D is superior to several state-of-the-art methods under the ADD(-S) metric and ADD(-S) AUC metric.

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Section: Introductionmentioning

confidence: 99%

DON6D: Decoupled One-stage Network for 6D Pose Estimation

Wang,

Tu,

Qian

et al. 2023

Preprint

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show abstract

DON6D: a decoupled one-stage network for 6D pose estimation

Wang,

Tu,

Qian

et al. 2024

Sci Rep

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The six-dimensional (6D) pose object estimation is a key task in robotic manipulation and grasping scenes. Many existing two-stage solutions with a slow inference speed require extra refinement to handle the challenges of variations in lighting, sensor noise, object occlusion, and truncation. To address these challenges, this work proposes a decoupled one-stage network (DON6D) model for 6D pose estimation that improves inference speed on the premise of maintaining accuracy. Particularly, since the RGB images are aligned with the RGB-D images, the proposed DON6D first uses a two-dimensional detection network to locate the interested objects in RGB-D images. Then, a module of feature extraction and fusion is used to extract color and geometric features fully. Further, dual data augmentation is performed to enhance the generalization ability of the proposed model. Finally, the features are fused, and an attention residual encoder–decoder, which can improve the pose estimation performance to obtain an accurate 6D pose, is introduced. The proposed DON6D model is evaluated on the LINEMOD and YCB-Video datasets. The results demonstrate that the proposed DON6D is superior to several state-of-the-art methods regarding the ADD(-S) and ADD(-S) AUC metrics.

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An accurate and flexible image clamping center locating algorithm for micro-gripper

Zhang,

Wang

et al. 2023

Review of Scientific Instruments

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In the process of microassembly, aligning the end effectors with the micro-parts using image information is the basis of automated assembly. In order to realize the flexible and accurate clamping center locating of the micro-gripper with various shapes of jaws, this paper proposes an iterative-based processing algorithm. First, the locating problem is transformed into a multi-parameter optimization problem through the geometric analysis of the clamping process. Second, an iterative optimal algorithm based on the block coordinate descent is developed, in which a scaling golden section (SGS) scheme is proposed to calculate the iteration scaling parameters. Third, the lookup table and variable threshold iteration techniques are utilized to further improve the performance of the SGS scheme. Simulation results show that the proposed algorithm can efficiently locate the clamping center for various types of jaws with sub-pixel accuracy. Finally, a microassembly experiment is carried out to demonstrate the effectiveness of the algorithm.

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Research on the Industrial Robot Grasping Method Based on Multisensor Data Fusion and Binocular Vision

Cited by 5 publications

References 19 publications

DON6D: Decoupled One-stage Network for 6D Pose Estimation

DON6D: Decoupled One-stage Network for 6D Pose Estimation

DON6D: a decoupled one-stage network for 6D pose estimation

An accurate and flexible image clamping center locating algorithm for micro-gripper

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