PLY-SLAM:语义视觉SLAM集成点线特征与动态场景中的YOLOv8-seg
Huan Mao1, Jingwen Luo1,2
1School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
本研究介绍了使用点线特征和YOLOv8-seg. 的语义视觉同时定位和映射 (vSLAM) 系统. 它通过有效地处理动态对象,在具有挑战性的环境中提高了稳定性和准确性.
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:
- 传统的基于点特征的视觉SLAM (vSLAM) 在动态和低纹理环境中难以实现稳定性和准确性.
- 现有的方法往往无法充分解决动态物体和稀疏的环境特征所带来的挑战.
研究的目的:
- 为动态和低纹理环境开发一个强大而准确的语义vSLAM系统.
- 通过将点线特征与来自YOLOv8-seg.的语义信息合并来提高本地化准确性.
主要方法:
- 使用采样点和RANSAC.实施了高性能3D线段提取和装配方法.
- 通过分析拓变化,利用Delaunay三角法进行几何映射和动态特征点检测.
- 集成YOLOv8-seg实例标签以准确删除动态特征点,并采用循环关闭机制,将点线特征与实例级匹配融合.
主要成果:
- 拟议的语义vSLAM方法在模拟和实验中表现出卓越的性能.
- 点线特征和语义细分的有效融合提高了稳定性和本地化准确性.
- 实现了精确的移除动态特征点和可靠的闭环检测.
结论:
- 与传统方法相比,语义vSLAM方法在具有挑战性的环境中显著提高了性能.
- 将几何特征 (点线) 与语义理解 (YOLOv8-seg) 融合在一起,对于强大的vSLAM至关重要.
- 开发的系统为复杂的现实场景中准确可靠的导航提供了有希望的解决方案.
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