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Modular Approach for Odometry Localization Method for Vehicles with Increased Maneuverability.

Chenlei Han1, Michael Frey1, Frank Gauterin1

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Summary
This summary is machine-generated.

This study explores using wheel odometry for vehicle localization during omnidirectional parking. Advanced odometry models incorporating state variables offer more accurate and robust position and orientation estimation.

Keywords:
localizationmodular approachodometryomnidirectionalunscented Kalman filter (UKF)vehicle models

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Area of Science:

  • Robotics
  • Automotive Engineering
  • Sensor Fusion

Background:

  • Accurate vehicle localization is crucial for navigation and control systems.
  • Omnidirectional parking maneuvers present unique challenges for traditional localization methods.
  • Odometry, using wheel rotation data, is a key sensor input for estimating vehicle state.

Purpose of the Study:

  • To investigate the use of odometry for precise vehicle position and orientation estimation during omnidirectional parking.
  • To develop and evaluate novel odometry versions using a modular approach for designing state estimators.
  • To assess the performance of different odometry implementations in both simulation and real-world driving scenarios.

Main Methods:

  • Identification of relevant vehicle models and sensors for omnidirectional maneuvers.
  • Design of several odometry versions based on a modular, state-estimator-focused approach.
  • Implementation and validation of odometry versions through simulation and real driving tests.

Main Results:

  • Odometry versions incorporating more detailed vehicle models demonstrated improved performance.
  • Utilizing state variables within the odometry models led to more accurate estimations.
  • The proposed modular approach facilitates the design and comparison of different odometry algorithms.

Conclusions:

  • Odometry is a viable method for vehicle localization in omnidirectional parking.
  • Enhanced odometry models with state variables significantly improve estimation accuracy and robustness.
  • The developed modular framework aids in creating sophisticated state estimators for vehicle navigation.