SGDO-SLAM:一个语义 RGB-D SLAM 系统,具有粗细的动态拒绝和静态加权优化
Qiming Hu1, Shuwen Wang1, Nanxing Chen1
1School of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
SGDO-SLAM通过准确感知移动物体,提高了机器人在动态环境中的导航. 这种语义意识的SLAM框架显著提高了定位准确性,并为机器人创建了详细的3D地图.
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) 对移动机器人至关重要.
- 传统的视觉SLAM在动态环境中扎,导致准确性损失.
- 现有的系统往往无法有效地处理移动的物体.
研究的目的:
- 为动态场景开发一个实时RGB-D语义意识的SLAM框架 (SGDO-SLAM).
- 为了提高机器人定位和映射在非静态环境中的准确性和稳定性.
- 为了创建周围环境的密集,可视化的点云地图.
主要方法:
- 一种多约束的动态拒绝方法 (粗细) 使用语义,几何和深度信息.
- 基于深度一致性的静态质量权重的量化.
- 一种位置优化方法,优先考虑高质量的静态特征,以提高姿势估计.
主要成果:
- SGDO-SLAM实现了精确的动态场景感知.
- 该框架显著提高了机器人定位的准确性.
- 实验结果显示,与ORB-SLAM2相比,绝对轨迹误差减少了95%.
- 保持了实时效率.
结论:
- 在动态场景中,SGDO-SLAM为SLAM提供了最先进的准确性.
- 提出的方法有效地解决了非静态环境的挑战.
- 该框架为移动机器人导航和绘图提供了强大的解决方案.
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