通过结合刚性特征点集建模和YOLO,实现动态SLAM.
Pengchao Ding1, Weidong Wang1, Xian Wu1
1State Key Laboratory of Robotics and Systems, Harbin University of Technology, Harbin 150001, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
本研究介绍了一种实时动态视觉惯性SLAM算法,用于在不断变化的环境中准确定位. 它有效地划分物体并利用它们的运动来提高性能.
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:
- 在动态环境中准确定位对于自主系统至关重要.
- 现有的同时定位和映射 (SLAM) 算法与移动的物体进行斗争.
研究的目的:
- 开发一个实时动态视觉惯性SLAM算法,能够处理动态环境.
- 为了提高SLAM在移动物体存在时的准确性和稳定性.
主要方法:
- 结合YOLO-V5和深度值,实现实时像素级对象细分.
- K-意味着对象深度提取的聚类,以处理阻塞.
- 因素图的优化,包括刚性和非刚性的动态物体运动.
- 卡尔曼波器用于对象匹配和跟踪.
- 适应性刚性点集建模,以增强刚性对象检测.
主要成果:
- 在动态环境中展示实时性能.
- 成功细分和跟踪动态对象,即使被封闭.
- 通过有效利用动态物体运动信息,提高了定位准确性.
结论:
- 拟议的动态视觉惯性SLAM算法有效地应对动态环境中的挑战.
- 对象检测,深度提取和高级优化的集成显著提高了SLAM性能.
- 通过对公共和自建数据集的实验进行验证.
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