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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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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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Local attraction refers to disturbances in compass readings caused by magnetic influences from nearby objects such as metal fences, buried pipes, vehicles, buildings, power lines, or natural iron ore deposits. Small items like wristwatches, steel tools, or belt buckles can also interfere with the compass by creating local magnetic fields that distort the Earth's natural magnetic field. These distortions lead to inaccurate readings, posing navigation and land surveying challenges.Local...
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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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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...
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A Semantic-Associated Factor Graph Model for LiDAR-Assisted Indoor Multipath Localization.

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  • 1School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.

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Summary
This summary is machine-generated.

This study introduces a LiDAR-assisted method for precise indoor positioning, improving accuracy by 32.1%. It effectively uses environmental data to correct errors from wireless signal reflections, enhancing robustness in complex spaces.

Keywords:
LiDARfactor graph optimizationindoor positionmultipathvirtual anchor

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Area of Science:

  • Robotics and Autonomous Systems
  • Wireless Communication and Signal Processing
  • Geomatics Engineering

Background:

  • Global Navigation Satellite System (GNSS) is unavailable indoors.
  • Wireless signals like 5G and Ultra-Wideband (UWB) are used for indoor positioning.
  • Multipath effects from complex indoor structures degrade positioning accuracy.

Purpose of the Study:

  • To develop a LiDAR-assisted method for multipath error estimation and high-precision indoor positioning.
  • To integrate environmental semantic information for improved multipath correction.
  • To enhance positioning robustness in complex indoor environments.

Main Methods:

  • A tightly coupled perception-positioning framework using LiDAR and wireless signals.
  • A semantic-feature-based neural network for reflective surface detection from LiDAR point clouds.
  • A unified factor graph model for joint inference of states and reflector information.

Main Results:

  • Accurate extraction of geometric parameters of reflectors using LiDAR.
  • Dynamic discrimination and utilization of both line-of-sight (LOS) and non-line-of-sight (NLOS) paths.
  • 32.1% improvement in root mean square error (RMSE) compared to traditional methods.

Conclusions:

  • The proposed method effectively addresses multipath effects in complex indoor environments.
  • LiDAR-assisted semantic information significantly enhances indoor positioning accuracy and robustness.
  • Provides a viable solution for high-precision localization in challenging indoor settings.