无融合:在具有挑战性的环境中为急救人员提供多模式定位
Dennis Dahlke1, Petros Drakoulis2, Anaida Fernández García3
1German Aerospace Center (DLR), 12489 Berlin, Germany.
Sensors (Basel, Switzerland)
|May 11, 2024
概括
本研究介绍了第一响应者的一种融合本地化系统,它结合了视觉,利略和惯性数据. 强大的解决方案可确保在没有基础设施的情况下准确的室内/室外定位,提高响应者的安全性和运营效率.
科学领域:
- 机器人和传感器融合技术
- 地理学工程 工程地质学
- 应急响应技术 应急响应技术
背景情况:
- 在动态环境中,准确地定位第一响应者至关重要.
- 现有的本地化方法经常在室内失败或依赖外部基础设施.
- 响应员的安全性和运营效率取决于可靠的定位.
研究的目的:
- 开发和验证一个新的,无基础设施的融合本地化系统,用于第一响应者.
- 整合视觉,利略 (GNSS) 和惯性传感器数据,以实现可靠的定位.
- 为了实现准确和无的室内和室外定位能力.
主要方法:
- 基于视觉的本地化使用RGB摄像头和ORB-SLAM2.
- 基于利略的本地化与高精度GNSS接收器原型.
- 基于惯性定位通过智能手机的惯性测量单元 (IMU) 进行传感器融合.
- 三种模式的融合,创建一个全面的本地化工具.
主要成果:
- 融合本地化工具在各种环境 (室内/室外) 中提供连续定位.
- 该系统表现出稳健性,在不依赖现有基础设施的情况下运行.
- 实现了总体平均定位误差为1.73米.
结论:
- 拟议的融合本地化系统为急救人员提供了可靠和准确的解决方案.
- 这项技术在不可预测的场景中提高了应急人员的安全性和运营效率.
- 没有基础设施的方法可以确保无论环境条件如何,都能保持一致的性能.
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