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相关概念视频

Field Application of Global Positioning System01:28

Field Application of Global Positioning System

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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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Errors in Global Positioning System01:26

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,...
61
Introduction to Global Positioning System01:30

Introduction to Global Positioning System

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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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Types of Global Positioning System Surveys01:30

Types of Global Positioning System Surveys

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GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
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Inertial Frames of Reference01:03

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Newton’s first law is usually considered to be a statement about reference frames. It provides a method for identifying a special type of reference frame: the inertial reference frame. In principle, we can make the net force on a body zero. If its velocity relative to a given frame is constant, then that frame is said to be inertial. So, by definition, an inertial reference frame is a reference frame where Newton's first law holds valid. Newton's first law applies to objects with...
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Non-inertial Frames of Reference01:27

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A reference frame accelerating or decelerating relative to an inertial frame is a non-inertial frame. To help understand this, consider what taking off in an airplane, turning a corner in a car, riding a merry-go-round, and the circular motion of a tropical cyclone all have in common. All these systems are accelerating, decelerating, or rotating relative to the Earth; hence, they all are non-inertial frames. All these systems exhibit inertial forces, which merely seem to arise from motion,...
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A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
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在极其资源有限的平台上惯性导航:方法,机遇和挑战

Swapnil Sayan Saha1, Yayun Du2, Sandeep Singh Sandha3

  • 1University of California, Los Angeles, Los Angeles, CA, USA.

IEEE/ION Position Location and Navigation Symposium : [proceedings]. IEEE/ION Position Location and Navigation Symposium
|September 22, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了AI增强的惯性导航,用于资源有限的物联网 (IoT) 平台. 它引入了轻量级的,基于物理的模型,用于在没有GPS的环境中准确地定位,克服了传统方法的局限性.

关键词:
贝叶斯语 贝叶斯语 贝叶斯语 贝叶斯语在TinyML中使用TinyML.死亡计数的死亡计数.惯性 惯性 惯性的卡尔曼过器可以过.神经架构搜索神经架构搜索神经网络的神经网络的神经网络这是神经符号神经符号.测距器 (odometry) 是一个测距器.这是一个平台意识的平台.顺序学习学习是指学习的顺序.

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科学领域:

  • 机器人技术和嵌入式系统
  • 人工智能的人工智能
  • 传感器融合式传感器

背景情况:

  • 惯性导航为GPS拒绝地区的物联网 (IoT) 设备提供低成本的本地化.
  • 传统的方法与环境复杂性和漂移作斗争.
  • 基于人工智能的方法看起来很有前途,但资源密集,缺乏物理保证.

研究的目的:

  • 探索在物联网平台上部署实时AI增强惯性导航的方法.
  • 开发用于惯性测距的轻量级,高效和准确的AI模型.
  • 为解决AI驱动导航领域适应和数据效率方面的挑战.

主要方法:

  • 平台意识的神经架构寻找超轻的深度神经惯性测距模型.
  • 神经符号AI技术用于创建基于物理的可解释导航模型.
  • 有效的数据收集和标签策略,用于特定领域的模型微调.

主要成果:

  • 开发了神经惯性测距模型,比最先进的AI方法小31-134×,具有可比或更高的本地化分辨率.
  • 生成适用于各种平台的模型,包括人类,动物,水下传感器,飞行器和机器人.
  • 使用最小标记数据 (1分钟) 证明有效的域名适应.

结论:

  • 人工智能增强的惯性导航可以在资源有限的物联网平台上有效地部署.
  • 轻量级,神经符号和域适应模型为强大的本地化提供了可行的途径.
  • 需要进一步的研究,以解决现实世界人工智能驱动的导航系统的公开挑战.