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Improving Odometric Accuracy for an Autonomous Electric Cart

Computer Science and System Department, Universidad de La Laguna, 38200 Santa Cruz de Tenerife, Spain
Author to whom correspondence should be addressed.
Sensors 2018, 18(1), 200;
Received: 13 December 2017 / Revised: 9 January 2018 / Accepted: 10 January 2018 / Published: 12 January 2018
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Spain 2017)
PDF [11639 KB, uploaded 12 January 2018]


In this paper, a study of the odometric system for the autonomous cart Verdino, which is an electric vehicle based on a golf cart, is presented. A mathematical model of the odometric system is derived from cart movement equations, and is used to compute the vehicle position and orientation. The inputs of the system are the odometry encoders, and the model uses the wheels diameter and distance between wheels as parameters. With this model, a least square minimization is made in order to get the nominal best parameters. This model is updated, including a real time wheel diameter measurement improving the accuracy of the results. A neural network model is used in order to learn the odometric model from data. Tests are made using this neural network in several configurations and the results are compared to the mathematical model, showing that the neural network can outperform the first proposed model. View Full-Text
Keywords: autonomous vehicles; odometry; neural networks; Robotics autonomous vehicles; odometry; neural networks; Robotics

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Toledo, J.; Piñeiro, J.D.; Arnay, R.; Acosta, D.; Acosta, L. Improving Odometric Accuracy for an Autonomous Electric Cart. Sensors 2018, 18, 200.

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