Positioning System for an Electric Autonomous Vehicle Based on the Fusion of Multi-GNSS RTK and Odometry by Using an Extented Kalman Filter
| Autor: | Miguel Tradacete, P. Revenga, Rafael Barea, Luis M. Bergasa, Elena López-Guillén, Carlos Gómez Huélamo, Álvaro Sáez, Juan Felipe Arango |
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| Rok vydání: | 2018 |
| Předmět: |
business.product_category
010504 meteorology & atmospheric sciences Positioning system Computer science business.industry Real-time computing ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Satellite system Kalman filter 010502 geochemistry & geophysics 01 natural sciences Extended Kalman filter Odometry GNSS applications Electric vehicle Global Positioning System business 0105 earth and related environmental sciences |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783319998848 WAF |
| DOI: | 10.1007/978-3-319-99885-5_2 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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