基于动态差异和双路径功能增强的视觉位置识别.
Guogang Wang1, Yizhen Lv1, Lijie Zhao1
1College of Information Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
本研究引入了一种用于视觉位置识别 (VPR) 的新方法,以在不断变化的条件下提高准确性. 动态差异和双路特征增强 (DD-DPFE) 方法增强了特征歧视和对噪声和外观漂移的稳定性.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 视觉位置识别 (VPR) 系统与外观漂移和噪音作斗争.
- 现有的方法在动态和复杂的环境中缺乏稳定性.
研究的目的:
- 提出一种新的方法,DD-DPFE,以提高VPR的性能.
- 为了提高对外观变化和噪声干扰的强度.
主要方法:
- 开发了一种动态差异和双路径特征增强 (DD-DPFE) 方法.
- 在DINOv2模型中集成了差分注意力和串行并行适配器.
- 实现了全球和当地分支机构的双向功能增强模块.
主要成果:
- 在各种环境测试中,DD-DPFE方法显著提高了性能.
- 在夜间场景模拟中表现出卓越的结果,突出了增强的反干扰能力.
- 在复杂的场景中验证了增加的区分能力和稳定性.
结论:
- 拟议的DD-DPFE方法有效地解决了VPR系统的局限性.
- 实现了增强的稳定性和区分能力,特别是在具有挑战性的条件下,如低光.
- DD-DPFE为可靠的视觉位置识别提供了一个有前途的解决方案.
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