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在动态场景中提高ORB-SLAM3准确度,使用YOLO11细分.

Renata Raffaine Villegas1, Anselmo Rafael Cukla2, Gabriel Alejandro Tarnowski3

  • 1Faculdade de Engenharia Mecânica, Universidade Estadual de Campinas (UNICAMP), Rua Mendenleyv, 200, Cidade Universitária, Campinas, São Paulo 13083-860, Brazil.

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本研究将ORB-SLAM3增强为动态环境的YOLOv11实例细分. 改进后的系统大大减少了现实世界机器人和数据集中的视觉SLAM错误.

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科学领域:

  • 机器人和计算机视觉 机器人和计算机视觉
  • 同时定位和绘制 (SLAM)

背景情况:

  • 传统的视觉SLAM系统在动态环境中难以准确.
  • 一个受欢迎的SLAM系统ORB-SLAM3容易因移动的物体而导致性能下降.

研究的目的:

  • 增强ORB-SLAM3以在动态环境中提供强大的性能.
  • 在SLAM中集成实例细分以实现动态特征排除.

主要方法:

  • 通过将YOLOv11实例细分模块集成到ORB-SLAM3.3中,开发了YOLOv11-ORB-SLAM3.
  • 在TUM RGB-D数据集和现实世界移动机器人实验中使用立体声和RGB-D摄像头测试了该系统.

主要成果:

  • 在动态场景中,YOLOv11-ORB-SLAM3显著超过了原来的ORB-SLAM3.
  • 在TUM RGB-D数据集上实现了93%的错误减少.
  • 保持了计算效率.

结论:

  • 拟议的YOLOv11-ORB-SLAM3系统为动态环境中的视觉SLAM提供了强大而高效的解决方案.
  • 对于需要在移动物体中准确定位的现实世界机器人应用程序的可行性.