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HDPL: a hybrid descriptor for points and lines based on graph neural networks

Zirui Guo (Robotics Research Center, National University of Defense Technology, College of Intelligence Science, Changsha, China)
Huimin Lu (Robotics Research Center, National University of Defense Technology, College of Intelligence Science, Changsha, China)
Qinghua Yu (Robotics Research Center, National University of Defense Technology, College of Intelligence Science, Changsha, China)
Ruibin Guo (Robotics Research Center, National University of Defense Technology, College of Intelligence Science, Changsha, China)
Junhao Xiao (Robotics Research Center, National University of Defense Technology, College of Intelligence Science, Changsha, China)
Hongshan Yu (Laboratory for Robot Visual Perception and Control Technology, Hunan University, College of Electrical and Information Engineering, Changsha, China)

Industrial Robot

ISSN: 0143-991x

Article publication date: 22 July 2021

Issue publication date: 21 September 2021

95

Abstract

Purpose

This paper aims to design a novel feature descriptor to improve the performance of feature matching in challenge scenes, such as low texture and wide-baseline scenes. Common descriptors are not suitable for low texture scenes and other challenging scenes mainly owing to encoding only one kind of features. The proposed feature descriptor considers multiple features and their locations, which is more expressive.

Design/methodology/approach

A graph neural network–based descriptors enhancement algorithm for feature matching is proposed. In this paper, point and line features are the primary concerns. In the graph, commonly used descriptors for points and lines constitute the nodes and the edges are determined by the geometric relationship between points and lines. After the graph convolution designed for incomplete join graph, enhanced descriptors are obtained.

Findings

Experiments are carried out in indoor, outdoor and low texture scenes. The experiments investigate the real-time performance, rotation invariance, scale invariance, viewpoint invariance and noise sensitivity of the descriptors in three types of scenes. The results show that the enhanced descriptors are robust to scene changes and can be used in wide-baseline matching.

Originality/value

A graph structure is designed to represent multiple features in an image. In the process of building graph structure, the geometric relation between multiple features is used to establish the edges. Furthermore, a novel hybrid descriptor for points and lines is obtained using graph convolutional neural network. This enhanced descriptor has the advantages of both point features and line features in feature matching.

Keywords

Acknowledgements

This work was supported by the National Natural Science Foundation of China [61773393, U1813205, U1913202].

Citation

Guo, Z., Lu, H., Yu, Q., Guo, R., Xiao, J. and Yu, H. (2021), "HDPL: a hybrid descriptor for points and lines based on graph neural networks", Industrial Robot, Vol. 48 No. 5, pp. 737-744. https://doi.org/10.1108/IR-02-2021-0042

Publisher

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Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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