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Journal of System Simulation

Abstract

Abstract: Visual relocalization tasks have important application value in fields such as digital twin and augmented reality. The current mainstream methods still face challenges such as mismatch between coordinate regression scale and receptive field and insufficient attention to local information. A visual relocalization method that combines region classification and local feature enhancement is proposed. The coordinate regression problem in large space is transformed into a multi-region classification problem and a coordinate regression problem inside a small scene, which significantly reduces the uncertainty of coordinate regression and makes the network globally have a large receptive field. A conditioning layer using deep feature fusion introduces the results of the upper classification layer into the lower network. Feature learning and fusion within a local region through the graph attention mechanism allows the network to learn both global and local feature information, which combined with the hierarchical regression framework, improves the stability of relocalization. Comparative experiments and analyses of the proposed method with mainstream visual relocalization methods are conducted on a publicly available multi-scene dataset. The experimental results show that the visual relocalization method proposed in this paper achieves more precise relocalization results with higher relocalization accuracy.

First Page

45

Last Page

57

CLC

TP391.9

Recommended Citation

Wang Yining, Liu Yanli, Xing Guanyu. Visual Relocalization Method Combining Region Classification and Local Feature Enhancement[J]. Journal of System Simulation, 2026, 38(1): 45-57.

Corresponding Author

Liu Yanli

DOI

10.16182/j.issn1004731x.joss.25-0830

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