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相关概念视频

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
377
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...
313
Types of Global Positioning System Surveys01:30

Types of Global Positioning System Surveys

341
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...
341
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
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Introduction to Global Positioning System01:30

Introduction to Global Positioning System

457
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,...
457
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
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硬件实现了改进的定向快速和旋转简报-同时定位和映射版本2的硬件实现.

Ji-Long He1, Ying-Hua Chen2, Wenny Ramadha Putri2

  • 1School of Computer Science and Technology, Shandong University of Technology, Zibo 255000, China.

Sensors (Basel, Switzerland)
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PubMed
概括
此摘要是机器生成的。

这项研究增强了ORB-SLAM2算法,以在像Raspberry Pi 3这样的资源有限的设备上实现高效的自主导航. 改进后的系统在同时定位和映射 (SLAM) 中实现了更高的准确性,同时保持了实时性能.

关键词:
在ORB-SLAM2中使用ORB.拉斯伯雷皮3是什么意思斯拉姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯兰姆斯机器视觉 机器视觉 机器视觉空间场景的建设空间场景的建设.

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科学领域:

  • 机器人和计算机视觉 机器人和计算机视觉
  • 自主系统工程 自主系统工程

背景情况:

  • 在自动驾驶中实现高水平的自动化需要准确的映射,特别是在未知的环境中.
  • 同时定位和映射 (SLAM) 算法对于导航至关重要,但往往需要大量的计算能力.
  • 现有的SLAM方法在资源有限的平台中难以实现效率.

研究的目的:

  • 增强ORB-SLAM2算法,以提高树派3上的映射性能.
  • 为应对嵌入式系统在SLAM中平衡精度和计算效率的挑战.
  • 在具有有限计算资源的环境中实现实时自主导航.

主要方法:

  • 在Raspberry Pi 3 (ARM A53 CPU) 上实现一个增强的ORB-SLAM2算法.
  • 优化功能描述符,以提高立体声功能匹配率.
  • 调整闭环参数以最大限度地减少映射中的累积错误.
  • 使用机器人操作系统 (ROS) 进行系统集成和性能监控.

主要成果:

  • 在单眼SLAM中显著减少错误:RMSE下降了18.11%,平均错误为22.97%,中位数错误为29.41%,最大错误为17.18%.
  • 立体SLAM中小误差的减少:RMSE下降了0.30%,平均误差减少了0.38%.
  • 稳定的ROS主题频率为10Hz,四核CPU利用率约为90%,表明实时能力.

结论:

  • 增强的ORB-SLAM2系统为资源有限的平台提供了准确和高效的实时SLAM的可行解决方案.
  • 优化成功地提高了映射精度,同时保持了嵌入式自主系统的计算可行性.
  • 这项工作展示了一种实际的方法,用于在自动驾驶边缘计算场景中部署先进的SLAM功能.