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- W4386821900 abstract "As a low-cost localization solution with great application potential, visual localization has attracted a lot of attention from academia and industry in recent years. Currently, a hierarchical visual localization pipeline, from global to local, has been established and validated through numerous robotic experiments and applications. However, the problem of robust visual localization under long-term changing conditions has not yet been solved, and the upper bound of visual localization performance still needs to be explored. This requires an analysis of the possible enhancement space in each stage of the pipeline. In this paper, we review the hierarchical visual localization pipeline and discuss possible strategies to improve localization performance in each stage. Additionally, for the pose estimation stage, we propose a unified 2-correspondence RANSAC framework that maximizes the feature matching set and adapts well to changing environmental characteristics. Finally, we design a comprehensive experimental evaluation using a public real-world localization dataset to investigate the impact of these strategies on the final localization performance. This evaluation can provide experimental guidance for future work." @default.
- W4386821900 created "2023-09-19" @default.
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- W4386821900 date "2023-07-24" @default.
- W4386821900 modified "2023-09-26" @default.
- W4386821900 title "Long-Term Map-Based Visual Localization: Analysis of Individual Components of a Hierarchical Pipeline" @default.
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- W4386821900 doi "https://doi.org/10.23919/ccc58697.2023.10239767" @default.
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