Abstract
This paper presents a global positioning system for an autonomous electric vehicle based on a Real-Time Kinematic Global Navigation Satellite System (RTK- GNSS), and an incremental-encoder odometry system. Both elements are fused to a single system by an Extended Kalman Filter (EKF), reaching centimeter accuracy. Some varied experiments have been carried out in a real urban environment to compare the performance of this positioning architectures separately and fused together. The achieved aim was to provide autonomous vehicles with centimeter precision on geolocalization to navigate through a real lane net.
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Acknowledgment
This work has been partially funded by the Spanish MINECO/FEDER through the SmartElderlyCar project (TRA2015-70501-C2-1-R), the DGT through the SERMON project (SPIP2017-02305), and from the RoboCity2030-III-CM project (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos, fase III; S2013/MIT-2748), funded by Programas de actividades I+D (CAM) and cofunded by EU Structural Funds.
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Tradacete, M. et al. (2019). Positioning System for an Electric Autonomous Vehicle Based on the Fusion of Multi-GNSS RTK and Odometry by Using an Extented Kalman Filter. In: Fuentetaja Pizán, R., García Olaya, Á., Sesmero Lorente, M., Iglesias Martínez, J., Ledezma Espino, A. (eds) Advances in Physical Agents. WAF 2018. Advances in Intelligent Systems and Computing, vol 855. Springer, Cham. https://doi.org/10.1007/978-3-319-99885-5_2
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DOI: https://doi.org/10.1007/978-3-319-99885-5_2
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