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Tightly Coupled GNSS/IMU Hybrid Navigation Using Factor Graph Optimization with NLOS Detection Capability.

Haruki Tanimura1, Toshiaki Tsujii1

  • 1Department of Aerospace Engineering, Graduate School of Engineering, Osaka Metropolitan University, Nakamozu Campus, Osaka 599-8531, Japan.

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Summary

This study enhances autonomous navigation in urban canyons by integrating Global Navigation Satellite Systems (GNSS) with Inertial Measurement Units (IMU) and machine learning. The novel approach significantly improves positioning accuracy and reliability amidst signal interference.

Keywords:
GNSSIMUNLOSfactor graph optimizationmachine learningpositioning accuracytightly coupledurban environment

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Area of Science:

  • Robotics and Autonomous Systems
  • Geomatics Engineering
  • Signal Processing

Background:

  • Autonomous navigation systems require high-precision self-localization.
  • Urban canyons present challenges for Global Navigation Satellite Systems (GNSS) due to multipath and Non-Line-of-Sight (NLOS) signals, causing pseudorange measurement bias and degrading positioning integrity.
  • Existing methods struggle with the reliability demands of autonomous systems in obstructed environments.

Purpose of the Study:

  • To develop a robust GNSS/Inertial Measurement Unit (IMU) tightly coupled integrated navigation system.
  • To mitigate the impact of NLOS signals in urban canyons using machine learning-based detection.
  • To enhance positioning accuracy and reliability for autonomous navigation.

Main Methods:

  • Proposed a novel GNSS/IMU tightly coupled system employing factor graph optimization (FGO).
  • Integrated a machine learning model for Non-Line-of-Sight (NLOS) signal detection, trained using dual-polarized antenna data.
  • Utilized a random forest classifier to identify NLOS signals and dynamically adjusted GNSS pseudorange factor variance within the FGO framework to mitigate outliers.

Main Results:

  • Achieved a 0.89 detection accuracy for NLOS signals using the machine learning model.
  • Experimental evaluations in dense urban environments showed an 84.8% improvement in horizontal positioning accuracy compared to standalone GNSS.
  • The integrated system demonstrated robust positioning performance, meeting stringent reliability requirements under severe signal obscuration.

Conclusions:

  • The proposed GNSS/IMU integrated navigation system effectively addresses the challenges of GNSS signal degradation in urban canyons.
  • The dynamic integration of machine learning-based NLOS detection with tightly coupled FGO provides a highly reliable positioning solution.
  • The method significantly enhances the feasibility of autonomous navigation in complex urban environments.