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GO-INO: Graph Optimization MEMS-IMU/NHC/Odometer Integration for Ground Vehicle Positioning.

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Summary
This summary is machine-generated.

Factor graph optimization (FGO) enhances Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) integration for ground vehicles. FGO improves navigation accuracy, especially in GNSS-denied environments, outperforming traditional Kalman filters.

Keywords:
GNSSINSfactor graph optimizationsignal outage

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Area of Science:

  • Robotics and Autonomous Systems
  • Navigation and Positioning
  • Sensor Fusion

Background:

  • Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) are crucial for vehicle navigation.
  • Kalman Filter (KF) is commonly used for GNSS/INS integration but struggles with GNSS signal loss.
  • Inertial Navigation System (INS) errors diverge rapidly without external aiding, particularly in urban canyons or tunnels.

Purpose of the Study:

  • To implement and evaluate Factor Graph Optimization (FGO) for integrated GNSS/INS navigation.
  • To assess FGO's performance against traditional Kalman Filter (KF) methods.
  • To investigate the benefits of incorporating nonholonomic constraints (NHC) and odometer data within an FGO framework for GNSS-denied scenarios.

Main Methods:

  • Factor Graph Optimization (FGO) framework was developed for sensor fusion.
  • GNSS, INS, nonholonomic constraints (NHC), and odometer measurements were modeled as constraints within the FGO graph.
  • An iterative optimization process was used to estimate the vehicle's state.
  • Comparative experiments were conducted between FGO and KF under varying GNSS availability.

Main Results:

  • FGO demonstrated superior performance compared to KF in GNSS/INS integrated navigation.
  • In GNSS-denied environments, FGO with NHC and odometer constraints significantly reduced position errors.
  • The experimental results validated the effectiveness of FGO for robust vehicle navigation.

Conclusions:

  • Factor Graph Optimization offers a more advanced and effective approach to sensor fusion for ground vehicle navigation.
  • Integrating NHC and odometer data into FGO provides significant improvements in GNSS-denied conditions.
  • FGO presents a promising alternative to KF for reliable and accurate navigation solutions.