TY  - JOUR
AU  - Du, Rui
PY  - 2026
DA  - 2026/09/24
TI  - A Review of Research and Applications of OpenCV in Robotic Arm Grasping Vision Systems
JO  - ICCK Transactions on Intelligent Cyber-Physical Systems
T2  - ICCK Transactions on Intelligent Cyber-Physical Systems
JF  - ICCK Transactions on Intelligent Cyber-Physical Systems
VL  - 1
IS  - 3
SP  - 104
EP  - 112
DO  - 10.62762/TICPS.2026.513798
UR  - https://www.icck.org/article/abs/TICPS.2026.513798
KW  - OpenCV
KW  - robotic arm
KW  - machine vision
KW  - camera calibration
KW  - lightweight embedded deployment
AB  - This paper reviews the OpenCV-based vision system for robotic-arm grasping across three core modules-camera calibration, image preprocessing and recognition, and binocular 3D reconstruction-together with lightweight deployment on embedded platforms. Using the calib3d module, monocular, binocular, and hand-eye calibration procedures are described, which correct lens distortion and unify coordinate frames and thereby address grasping-positioning errors. Image processing algorithms, including grayscale transformation, Gaussian and median filtering, Otsu segmentation, and feature matching, are compared for static and dynamic assembly-line scenarios. Binocular stereo matching generates disparity maps for depth recovery and 6-DoF pose estimation, supporting peg-in-hole assembly and obstacle avoidance. For embedded platforms (Raspberry Pi, TQ210, and STM32-based systems), optimization strategies such as library pruning and reduced image resolution or disparity search range are summarized to improve real-time performance. The review indicates that OpenCV-based solutions are open-source, low-cost, and portable, and are suitable for conventional grasping tasks; however, their robustness remains limited under strong illumination and occlusion. Integrating deep learning, multi-sensor fusion, and online hand--eye calibration are suggested to extend their applicability to complex environments.
SN  - 3071-2947
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Du2026A,
  author = {Rui Du},
  title = {A Review of Research and Applications of OpenCV in Robotic Arm Grasping Vision Systems},
  journal = {ICCK Transactions on Intelligent Cyber-Physical Systems},
  year = {2026},
  volume = {1},
  number = {3},
  pages = {104-112},
  doi = {10.62762/TICPS.2026.513798},
  url = {https://www.icck.org/article/abs/TICPS.2026.513798},
  abstract = {This paper reviews the OpenCV-based vision system for robotic-arm grasping across three core modules-camera calibration, image preprocessing and recognition, and binocular 3D reconstruction-together with lightweight deployment on embedded platforms. Using the calib3d module, monocular, binocular, and hand-eye calibration procedures are described, which correct lens distortion and unify coordinate frames and thereby address grasping-positioning errors. Image processing algorithms, including grayscale transformation, Gaussian and median filtering, Otsu segmentation, and feature matching, are compared for static and dynamic assembly-line scenarios. Binocular stereo matching generates disparity maps for depth recovery and 6-DoF pose estimation, supporting peg-in-hole assembly and obstacle avoidance. For embedded platforms (Raspberry Pi, TQ210, and STM32-based systems), optimization strategies such as library pruning and reduced image resolution or disparity search range are summarized to improve real-time performance. The review indicates that OpenCV-based solutions are open-source, low-cost, and portable, and are suitable for conventional grasping tasks; however, their robustness remains limited under strong illumination and occlusion. Integrating deep learning, multi-sensor fusion, and online hand--eye calibration are suggested to extend their applicability to complex environments.},
  keywords = {OpenCV, robotic arm, machine vision, camera calibration, lightweight embedded deployment},
  issn = {3071-2947},
  publisher = {Institute of Central Computation and Knowledge}
}