室内无GNSS环境的运动细分动态SLAM
Yunhao Wu1, Ziyao Zhang2,3, Haifeng Chen1
1College of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi'an 710021, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
通过使用光流运动细分,OS-SLAM在动态环境中增强了同时定位和映射 (SLAM). 这种强大的系统可显著减少GPS拒绝设置的错误.
科学领域:
- 机器人技术
- 计算机视觉
- 人工智能
背景情况:
- 同时定位和映射 (SLAM) 在没有GPS的环境中至关重要.
- 动态物体和变量会对SLAM的位置精度产生负面影响.
- 在动态场景中创建静态元素地图是一个关键挑战.
研究的目的:
- 为动态环境开发一个强大的SLAM系统.
- 在没有GNSS的环境中提高位置精度和地图的一致性.
- 为了减少动态对象对视觉SLAM的影响.
主要方法:
- 引入了OS-SLAM,集成了光学流动细分.
- 开发了一种轻量级的多尺度光流网络,用于准确的运动细分.
- 提出了一个YOLO-fastest和Rigidmask融合方法来处理非刚性物体.
- 通过过异常点云来生成静态密度点云地图.
主要成果:
- 在动态环境中,OS-SLAM表现出更强的稳定性.
- 该系统显著减少了动态对象对本地化的影响.
- 与TUM数据集中的ORB-SLAM3相比,实验结果显示绝对位置误差 (APE) 和相对位置误差 (RPE) 显著减少.
结论:
- OS-SLAM有效地整合了强大的光流和运动细分.
- 拟议的融合方法减轻了由动态物体引起的错误分割.
- 在动态视觉SLAM任务中,OS-SLAM提供了卓越的性能,特别是在缺乏GNSS的具有挑战性的场景中.
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