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A robust in-motion attitude alignment method for odometer-aided strapdown inertial navigation system.

Yiding Sun1, Gongliu Yang1, Qingzhong Cai1

  • 1School of Instrumentation and Optoelectronics Engineering, Beihang University, 37 Xueyuan Road, Beijing 100191, China.

The Review of Scientific Instruments
|December 31, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a robust method for in-motion attitude alignment in odometer (OD)-aided strapdown inertial navigation systems (SINS). The new approach effectively mitigates errors from wheel slipping and skidding, improving navigation precision.

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Area of Science:

  • Navigation Systems Engineering
  • Robotics and Autonomous Systems
  • Geomatics Engineering

Background:

  • In-motion attitude alignment is crucial for land vehicle navigation using odometer (OD)-aided strapdown inertial navigation systems (SINS).
  • Consecutive odometer outliers, caused by wheel slipping/skidding during maneuvers, degrade navigation robustness and precision.
  • Existing methods struggle to effectively handle these outliers, necessitating advanced alignment techniques.

Purpose of the Study:

  • To develop a robust in-motion attitude alignment method for OD-aided SINS that addresses the challenge of consecutive odometer outliers.
  • To enhance the precision and reliability of attitude estimation in land vehicles, particularly during dynamic maneuvers.
  • To validate the proposed method through simulations and real-world vehicle testing.

Main Methods:

  • The proposed method comprises two stages: in-motion coarse alignment and in-motion fine alignment.
  • In-motion coarse alignment utilizes a Huber's M-estimation and integral formula based robust Kalman filter (HRKF/IF-CA) to mitigate outlier interference on observation vectors.
  • In-motion fine alignment employs a HRKF-based approach (HRKF-FA) with a summed measurement model under a repeated backtracking scheme to refine attitude and suppress outliers.

Main Results:

  • The HRKF/IF-CA effectively restrains the interference of consecutive odometer outliers during the coarse alignment phase.
  • HRKF-FA successfully refines attitude alignment while simultaneously mitigating the impact of outliers in the fine alignment stage.
  • Both simulation and vehicle test results demonstrate that the proposed method achieves reasonable attitude alignment and significantly restrains outlier interference.

Conclusions:

  • The developed robust in-motion attitude alignment method significantly improves the performance of OD-aided SINS in the presence of wheel slipping and skidding.
  • The Huber's M-estimation and integral formula based robust Kalman filter offers a viable solution for handling outlier data in navigation systems.
  • The method enhances navigation accuracy and reliability for land vehicles operating in challenging dynamic conditions.