Object pose estimation based on stereo vision with improved<scp>K‐D</scp>tree<scp>ICP</scp>algorithm-Reference-Cited by-同舟云学术

Object pose estimation based on stereo vision with improvedK‐DtreeICPalgorithm

Published:2023-04-17 Issue:21 Volume:35 Page:
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency and Computation

Author:

Huang Li¹²,Wang Cheng¹²,Yun Juntong³⁴^ORCID,Tao Bo³⁵⁶^ORCID,Qi Jinxian³⁴,Liu Ying⁵⁶,Ma Hongjie⁷,Yu Hui⁸

Affiliation:

1. College of Computer Science and Technology, Wuhan University of Science and Technology Wuhan 430081 China

2. Hubei Province Key Laboratory of Intelligent Information Processing and Real‐time Industrial System Wuhan University of Science and Technology Wuhan 430081 China

3. Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan 430081 China

4. Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of science and Technology Wuhan 430081 China

5. Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of science and Technology Wuhan 430081 China

6. Hubei Longzhong Laboratory Xiangyang 441000 Hubei China

7. School of Energy and Electronic Engineering, University of Portsmouth Portsmouth PO1 3HF UK

8. School of Creative Technologies, University of Portsmouth Portsmouth PO1 2UP UK

Abstract

SummaryWith the wide application of stereovision in SLAM, object pose estimation has gradually become one of the research hotspots. This article proposes an object pose estimation for robotic grasping based on stereo vision with improved K‐D tree ICP algorithm. The feature points and feature descriptors of the point cloud of the object to be captured are extracted, and the feature template set is established. The SAC‐IA algorithm is used to carry out initial registration of the point cloud of the target, and the ICP algorithm based on K‐D tree is used for fine registration. The experimental results show that the average coincidence degree of the final registration of the proposed object pose estimation method reaches 94.1%, and the accurate 6D pose of the object to be grasped is obtained.

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.7714

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