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Improving Odometric Accuracy for an Autonomous Electric Cart.

Jonay Toledo1, Jose D Piñeiro2, Rafael Arnay3

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|January 13, 2018
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This study presents an odometric system for the Verdino autonomous cart. A neural network model demonstrated superior performance compared to the traditional mathematical model for estimating vehicle position and orientation.

Keywords:
Roboticsautonomous vehiclesneural networksodometry

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Area of Science:

  • Robotics
  • Autonomous Systems
  • Machine Learning

Background:

  • Accurate odometry is crucial for autonomous vehicle navigation.
  • Existing mathematical models for odometry can be limited by parameter accuracy and environmental factors.

Purpose of the Study:

  • To develop and evaluate an odometric system for the Verdino autonomous electric cart.
  • To compare the performance of a mathematical odometric model with a data-driven neural network model.

Main Methods:

  • Derivation of a mathematical odometric model based on cart movement equations.
  • Implementation of least squares minimization for parameter estimation.
  • Integration of real-time wheel diameter measurement to refine the mathematical model.
  • Development and testing of a neural network model trained on odometric data.

Main Results:

  • The mathematical model, enhanced with real-time measurements, improved accuracy.
  • The neural network model achieved higher accuracy in estimating the cart's position and orientation.
  • The neural network model demonstrated superior performance over the initial mathematical model.

Conclusions:

  • Neural network-based odometry offers a promising approach for enhancing autonomous vehicle navigation accuracy.
  • Data-driven models can effectively learn complex odometric behaviors, outperforming traditional physics-based models in certain scenarios.