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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

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 served as...
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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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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...
Depth Perception and Spatial Vision01:15

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Light Acquisition02:16

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Related Experiment Video

Updated: May 16, 2026

Assessing Human Spatial Navigation in a Virtual Space and its Sensitivity to Exercise
06:17

Assessing Human Spatial Navigation in a Virtual Space and its Sensitivity to Exercise

Published on: January 26, 2024

Context-aided sensor fusion for enhanced urban navigation.

Enrique David Martí1, David Martín, Jesús García

  • 1Applied Artificial Intelligence Group, Universidad Carlos III de Madrid, Avda de la Universidad Carlos III 22, 28270 Colmenarejo, Spain. emarti@inf.uc3m.es

Sensors (Basel, Switzerland)
|December 11, 2012
PubMed
Summary

This study presents an advanced Global Navigation Satellite System (GNSS)/Inertial Measurement Unit (IMU) fusion system for intelligent vehicles. The context-aided filter enhances urban navigation accuracy by adapting to driving conditions and correcting sensor errors.

Related Experiment Videos

Last Updated: May 16, 2026

Assessing Human Spatial Navigation in a Virtual Space and its Sensitivity to Exercise
06:17

Assessing Human Spatial Navigation in a Virtual Space and its Sensitivity to Exercise

Published on: January 26, 2024

Area of Science:

  • Intelligent Transportation Systems (ITS)
  • Robotics and Autonomous Systems
  • Geospatial Navigation

Background:

  • Urban environments pose significant challenges for vehicle positioning due to signal obstruction and multipath effects.
  • Reliable and precise positioning is critical for the safe deployment of Intelligent Vehicles (IVs) and advanced driver-assistance systems (ADAS).
  • Existing Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) systems often struggle with accuracy in complex urban canyons.

Purpose of the Study:

  • To develop and evaluate an advanced GNSS/IMU fusion system for robust urban navigation.
  • To improve the accuracy and reliability of vehicle positioning in challenging urban settings.
  • To create a system that adapts to varying sensor quality and driving contexts.

Main Methods:

  • Implementation of a context-aided Unscented Kalman Filter (UKF) for GNSS/IMU data fusion.
  • Development of a contextual knowledge module to assess sensor quality and driving scenarios.
  • Continuous estimation and correction of Inertial Navigation System (INS) drift errors.
  • Exhaustive sensor behavior analysis and characterization using available data.

Main Results:

  • The proposed fusion system demonstrates enhanced positioning accuracy in urban environments.
  • The context-aided approach effectively adapts the filter's performance to specific driving situations.
  • Continuous INS drift error correction significantly improves long-term navigation stability.
  • Validation with an extensive dataset confirms the system's robustness in representative urban scenarios.

Conclusions:

  • The developed context-aided GNSS/IMU fusion system offers a reliable solution for intelligent vehicle navigation in urban areas.
  • This approach is well-suited for the deployment of Intelligent Transport Systems (ITS) requiring precise real-time positioning.
  • Adaptive filtering based on contextual information is key to overcoming urban navigation challenges.