一个增强的,实时的,低成本的GNSS/INS集成导航算法及其平台设计.
Pengcheng Wang1, Yuting Gao1, Qingzhi Zhao1
1College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
本研究介绍了全球导航卫星系统 (GNSS) 和惯性导航系统 (INS) 集成的新实时算法,通过低成本设备提高无人机 (UAV) 定位精度.
科学领域:
- 导航系统 导航系统
- 机器人技术 机器人技术 机器人技术
- 航空航天工程 航空航天工程
背景情况:
- 全球导航卫星系统 (GNSS) 和惯性导航系统 (INS) 的整合对于准确定位至关重要,特别是在无人机 (UAV) 中.
- 具有成本效益的无人机经常受到实时处理需求和低质量的传感器测量的影响,从而阻碍了精确的导航.
- 现有的集成导航系统在实时性能和准确性方面面临着挑战.
研究的目的:
- 开发和验证一个速度受限,增强,实时,低成本的GNSS/INS集成导航算法和平台.
- 为了提高无人机使用集成导航的定位准确性和稳定性.
- 解决无人机导航中低成本传感器和实时处理的局限性.
主要方法:
- 提出了一种速度受限,增强,实时,低成本的GNSS/INS集成导航算法.
- 设计了一个基于开源软件KF_GINS的算法平台.
- 实现了GNSS位置和原始惯性测量单元 (IMU) 数据的松散合集成,使用4G数据传输单元 (DTU) 进行实时数据传输和计算.
主要成果:
- 该算法平台已成功应用于低成本的集成导航设备,包括无人机.
- 用车载和无人机数据集进行测试,在各种条件下证明了有效的计算.
- 实现了单点定位 (SPP) 精度的提高,水平定位高达15.38%,垂直定位高达6.78%.
结论:
- 开发的算法平台显著提高了无人机集成导航定位的准确性和稳定性.
- 速度受限制的方法有效地减轻了与低成本传感器和实时处理相关的挑战.
- 该解决方案提供了一种可行的方法,用于提高成本敏感的无人机应用中的导航系统性能.
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