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TSG-SLAM:SLAM在复杂的动态环境中使用实例分割和几何约束的紧密合
Yongchao Zhang1, Yuanming Li2,3, Pengzhan Chen1,3
1School of Intelligent Manufacturing, Taizhou University, Taizhou 318000, China.
Sensors (Basel, Switzerland)
|December 23, 2023
概括
本研究介绍了一种用于动态环境的新型视觉SLAM方法. 该方法有效地使用实例细分和几何约束去除动态特征,提高机器人本地化的准确性.
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
- 人工智能的人工智能
背景情况:
- 同时定位和映射 (SLAM) 系统在复杂的动态环境中面临着挑战.
- 管理移动物体和环境理解是当前SLAM技术的关键困难.
研究的目的:
- 为复杂的动态环境量身定制的视觉SLAM方法.
- 在动态场景中增强机器人的感知和理解.
主要方法:
- 提出了一个动态功能删除模块,紧密合实例分割和多视图几何约束 (TSG).
- 实例细分识别和删除动态特征,保留静态特征,用于基本的矩阵获取.
- 多视图几何约束用于识别和删除真正的动态特征,保持用于跟踪和映射的静态特征.
- 一个实例级的语义地图被构建用于增强环境理解.
主要成果:
- TSG-SLAM方法在检测和消除动态特征点方面表现出卓越的性能.
- 该系统在动态环境中实现了良好的定位精度.
- 根据TUM数据集和现实世界的场景进行评估,拟议的方法显示出有效性.
结论:
- 在复杂的动态环境中,TSG-SLAM方法为视觉SLAM提供了强大的解决方案.
- 将语义信息与几何约束集成,可以显著提高SLAM性能.
- 该方法增强了机器人的感知和导航动态设置的能力.
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