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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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A Study of a GNSS/IMU System for Object Localization and Spatial Position Estimation.

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  • 1Faculty of Telecommunication, Technical University of Sofia, 1000 Sofia, Bulgaria.

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Summary

This study integrates Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensors for precise navigation. Sensor fusion using Kalman filters enhances accuracy for autonomous systems, improving position and orientation tracking.

Keywords:
Allan VarianceGNSSIMUKalman filterquaternions

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Area of Science:

  • Robotics and Autonomous Systems
  • Geomatics Engineering
  • Sensor Fusion

Background:

  • Navigation systems are crucial for autonomous vehicles, VR/AR, and object tracking.
  • Accurate 3D position and orientation tracking are essential for these applications.
  • Inertial Navigation Systems (INS) provide motion data but require fusion with other sensors for precise navigation.

Purpose of the Study:

  • To integrate Global Navigation Satellite System (GNSS) with a 10 Degrees of Freedom (10DoF) Inertial Measurement Unit (IMU) system.
  • To calculate object position, attitude, and heading accurately.
  • To evaluate sensor data fusion techniques for improved navigation performance.

Main Methods:

  • Sensor data fusion using two Kalman filters (KF) for position and attitude calculations.
  • Detailed analysis of Allan Variance and normal distribution parameters for three MEMS IMU sensors.
  • Performance evaluation of GNSS systems using commercial and proposed antennas.
  • Experimental validation comparing KF heading angle output with other sources.

Main Results:

  • The proposed sensor fusion approach enhances the accuracy of navigation tasks.
  • Allan Variance and distribution analysis provide insights into MEMS IMU sensor characteristics.
  • GNSS antenna performance is assessed for optimal system integration.
  • Experimental results validate the effectiveness of the Kalman filter-based fusion for heading estimation.

Conclusions:

  • The integration of GNSS and IMU systems with Kalman filter-based sensor fusion offers a robust solution for 3D navigation.
  • Understanding sensor characteristics is vital for optimizing filter performance.
  • The study demonstrates improved accuracy in position, attitude, and heading calculations for autonomous applications.