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Errors in Global Positioning System

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Global Positioning System (GPS) technology has revolutionized navigation and positioning, but its accuracy is often compromised by various errors. These errors, stemming from environmental, satellite, and receiver-related factors, require careful mitigation to ensure reliable performance across applications.Atmospheric ErrorsGPS signals travel through the Earth’s ionosphere and troposphere, introducing delays which affect accuracy. The ionosphere is strongly influenced by charged particles,...
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The Global Positioning System (GPS) revolutionized positioning on Earth, providing precise location data through satellite ranging. The GPS system was developed in 1978 by the U.S. Department of Defense  for military use, and it became available for civilian applications in 1983, transforming fields including navigation, fleet management, and time synchronization for telecommunications systems.GPS consists of satellites in medium Earth orbit, about 20,200 kilometers above the surface,...
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The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
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一个同时定位和绘图系统,使用代错误状态卡尔曼波器判断算法,用于全球导航卫星系统.

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概括
此摘要是机器生成的。

本研究介绍了一种强大的自主机器人同时定位和映射 (SLAM) 系统,通过集成LiDAR,IMU和过的GPS数据来提高户外定位的准确性,特别是在GPS信号丢失时.

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科学领域:

  • 机器人技术和自主系统
  • 传感器融合式传感器
  • 同时定位和绘制 (SLAM)

背景情况:

  • 户外自主移动机器人依赖GPS进行定位,但城市环境和信号损失会导致显著的偏差.
  • 现有的SLAM系统与GPS不准确性和信号中断作斗争,限制了可靠的户外导航.
  • 准确的定位对于在各种环境中安全高效的自主机器人操作至关重要.

研究的目的:

  • 开发一个改进的SLAM系统,解决GPS不准确性和外部自主机器人的信号损失.
  • 在具有不可靠GPS数据的具有挑战性的环境中增强定位准确性和稳定性.
  • 为在城市和无GPS区域运行的自主移动机器人提供可靠的导航解决方案.

主要方法:

  • 提出了一个SLAM系统,结合了Iterated Extended Kalman Filter (IESKF) 和因子图优化.
  • 使用LiDAR和惯性测量单元 (IMU) 数据通过IESKF进行前端姿势估计.
  • 实现了基于状态和信心的GPS信号过方法,并将处理的GPS,IMU和循环闭合因子集成到后端因子图中.

主要成果:

  • 与FAST-LIO和LIO-SAM相比,在KITTI数据集和定制平台上,拟议的SLAM系统显示出更高的本地化准确性.
  • 观察到显著的性能改善,特别是在间歇性或丢失GPS信号的场景中.
  • 整合过的GPS数据和传感器融合提高了定位系统的整体稳定性.

结论:

  • 开发的SLAM系统有效地克服了GPS的局限性,为户外自主机器人提供了更高的定位精度和可靠性.
  • 拟议的传感器融合和GPS过方法为在具有挑战性的环境中导航提供了强大的解决方案.
  • 这项工作有助于在GPS通常不可靠的现实世界应用中推进自主导航能力.