改进了基于神经网络的多传感器融合动态口径测量
Lishu Luo1, Fulun Peng1, Longhui Dong1
1Xi'an Institute of Applied Optics, Xi'an 710065, China.
Sensors (Basel, Switzerland)
|October 16, 2024
概括
这项研究引入了一种新的动态测距方法,用于强大的机器人导航. 在复杂的环境中,FAST-LIVO通过从传感器数据中删除动态元素来增强同时定位和映射 (SLAM).
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
- 传感器融合式传感器
背景情况:
- 自主系统在动态环境中需要精确的定位和映射 (SLAM).
- 现有的SLAM方法难以应对移动的物体和不断变化的景观所带来的挑战.
- 对于机器人,自动驾驶汽车和无人机而言,可靠的导航至关重要.
研究的目的:
- 为高精度的同时定位和映射 (SLAM) 提出一个动态测距方法.
- 在现实世界的动态环境中提高测距仪的稳定性和可靠性.
- 整合多种传感器模式,以改善环境感知.
主要方法:
- 开发了一种基于FAST-LIVO系统的动态测距方法.
- 集成的激光,摄像头和惯性测量单元 (IMU) 数据.
- 利用轻量级的神经网络从视觉数据中删除动态元素.
- 将动态聚类应用于LiDAR数据,以过移动的物体.
- 构建并行视觉惯性和LiDAR惯性测距子系统.
主要成果:
- 拟议的方法可以在动态环境中实现高精度的姿势估计.
- 证明了公里计系统的高连续性和可靠性.
- 验证了多传感器聚变方法的动态稳定性.
- 从视觉和LiDAR数据流中成功过了动态元素.
结论:
- 多传感器融合动态测距方法在复杂的动态环境中显著提高了SLAM性能.
- FAST-LIVO为可靠的自主导航提供了坚实的基础.
- 神经网络和动态聚类的集成提高了环境数据的可靠性.
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