YDD-SLAM:室内动态视觉SLAM 融合YOLOv5与深度信息
Peichao Cong1, Junjie Liu1, Jiaxing Li1
1School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
本研究介绍了YDD-SLAM,这是一个视觉同步定位和映射 (VSLAM) 算法,可以改善机器人在动态环境中的导航. 它通过有效识别和移除动态对象来提高定位准确性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 视觉同步定位和映射 (VSLAM) 对机器人导航至关重要.
- 现有的VSLAM算法在动态环境中与准确性和实时性能作斗争,当动态对象普遍存在时,通常会失败.
研究的目的:
- 提出YDD-SLAM,一个增强的室内动态VSLAM算法.
- 为了提高VSLAM在动态环境中的定位精度和稳定性.
主要方法:
- YDD-SLAM将YOLOv5对象检测与ORB-SLAM3.3集成在一起.
- 物体按运动和深度进行分类;使用深度信息识别和消除动态特征.
- 对于动态环境,使用多个特征点优化策略.
主要成果:
- 在测试中,与ORB-SLAM3相比,YDD-SLAM的准确性明显提高.
- 该算法有效地处理具有高比例动态对象的环境.
- 强大的表现在公共数据集和现实世界的动态场景中得到了验证.
结论:
- YDD-SLAM为动态室内环境中的VSLAM提供了强大的解决方案.
- 拟议的方法提高了自主机器人的导航精度和可靠性.
- 这项研究为实际动态VSLAM应用奠定了基础.
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