YPR-SLAM:一个结合对象检测和动态场景的几何约束的SLAM系统
Xukang Kan1, Gefei Shi2, Xuerong Yang2
1School of Aeronautics and Astronautics, Sun Yat-sen University, Shenzhen 518107, China.
Sensors (Basel, Switzerland)
|October 26, 2024
概括
YPR-SLAM系统在现实世界中有效地处理移动的物体. 它使用YOLOv5和几何约束来删除动态特征点,提高同时定位和映射 (SLAM) 的准确性.
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
背景情况:
- 传统的同时定位和映射 (SLAM) 系统由于移动的物体而与动态环境作斗争.
- 移动的物体对SLAM中的图像匹配和相机姿势估计准确性产生负面影响.
研究的目的:
- 提出YPR-SLAM系统,在动态环境中提供强大的SLAM的高效解决方案.
- 为了最大限度地减少移动物体对SLAM性能的不利影响.
主要方法:
- 整合一个轻量级的YOLOv5物体检测网络,用于识别动态和静态物体.
- 使用深度图像和先前的动态对象信息应用几何约束方法.
- 使用深度-PROSAC算法来区分和删除动态特征点.
主要成果:
- 仅使用静态特征点成功构建密集云图.
- 在TUM RGB-D数据集上的动态环境中证明YPR-SLAM的有效性.
- 显著减少运动特征点干扰.
结论:
- YPR-SLAM提供了一种高效,紧密结合的方法,将对象检测和几何约束相结合.
- 该系统有效地消除了运动特征点,在现实世界的动态场景中增强了SLAM的稳定性.
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