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强大的多传感器融合用于在危险环境中使用热,LiDAR和GNSS数据定位
Lukas Schichler1, Karin Festl1, Selim Solmaz1
1Virtual Vehicle Research GmbH, 8010 Graz, Austria.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
这项研究介绍了一种强大的传感器融合算法,用于在危险地区的自主机器人导航. 该系统可靠地使用热摄像头,LiDAR和GNSS定位机器人,即使有传感器故障.
科学领域:
- 机器人技术和自主系统
- 传感器融合和定位
- 危险环境 导航 导航
背景情况:
- 自主机器人对于在道和灾区等危险环境中进行搜救至关重要.
- 传感器故障和错误在这些苛刻的条件下极大地挑战了定位准确性.
- 现有的本地化方法经常与复杂地形中的单个传感器的不可靠性作斗争.
研究的目的:
- 开发一个强大的传感器融合算法,用于在危险环境中可靠的自主机器人定位.
- 整合来自热摄像头,LiDAR和全球导航卫星系统 (GNSS) 的数据,以提高定位.
- 为了确保连续的本地化性能,尽管单个传感器中断或数据妥协.
主要方法:
- 为热传感器和LiDAR传感器实施了不同的同时定位和绘图 (SLAM) 和测距技术.
- 使用扩展卡尔曼波器 (EKF) 来融合来自热摄像头,LiDAR和GNSS的数据.
- 适应不同传感器采样率和模拟单个传感器在现场测试期间的故障.
主要成果:
- 拟议的传感器融合算法在具有挑战性的城市环境中展示了可靠的本地化性能.
- 该系统有效地弥补了单个传感器故障,保持了定位准确度.
- 现场测试验证了算法的稳定性在模拟的真实条件下与传感器中断.
结论:
- 强大的传感器融合对于在危险和不可预测的环境中可靠的自主导航至关重要.
- 通过EKF集成热,LiDAR和GNSS数据提供了一个弹性本地化解决方案.
- 开发的算法为提高自主机器人在关键操作中的安全性和有效性提供了一个有希望的方法.
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