The leveraging of a VGGNet-19 and a K-means cluster in visual loop closure detection tasks

Linlin Xia; Yu Wang; Zhuo Wang; Yue Meng

doi:10.1117/12.2689495

10 August 2023 The leveraging of a VGGNet-19 and a K-means cluster in visual loop closure detection tasks

Linlin Xia, Yu Wang, Zhuo Wang, Yue Meng

Proceedings Volume 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023); 1274824 (2023) https://doi.org/10.1117/12.2689495
Event: 5th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2023), 2023, Wuhan, China

Abstract

This study is devoted to a description of a loop closure detection framework, in which the leveraging of a VGGNet-19 and a K-means cluster enables a practical, autonomous feature learning-based detecting. The principal components analysis (PCA) for dimension reduction is also investigated, guaranteeing the algorithm optimization in both accuracy and efficiency. In terms of benchmark dataset tests, the results are compared against bag-of-words (BoW) model, AlexNet and VGGNet-16, revealing our proposed design significantly outperforms others in Precision-Recall. The calculated cosine similarities and the detected closed-loop frames are simultaneously provided.

Citation Download Citation

Linlin Xia, Yu Wang, Zhuo Wang, and Yue Meng "The leveraging of a VGGNet-19 and a K-means cluster in visual loop closure detection tasks", Proc. SPIE 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023), 1274824 (10 August 2023); https://doi.org/10.1117/12.2689495

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KEYWORDS

Visualization

Design and modelling

Feature extraction

Matrices

Principal component analysis

Dimension reduction

Image processing

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