Fuzzy Fusion of Stereo Vision, Odometer, and GPS for Tracking Land Vehicles

The incorporation of high precision vehicle positioning systems has been demanded by the autonomous electric vehicle (AEV) industry. For this reason, research on visual odometry (VO) and Artificial Intelligence (AI) to reduce positioning errors automatically has become essential in this field. In th...

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Published inMathematics (Basel) Vol. 10; no. 12; p. 2052
Main Authors Villaseñor-Aguilar, Marcos J., Peralta-López, José E., Lázaro-Mata, David, García-Alcalá, Carlos E., Padilla-Medina, José A., Perez-Pinal, Francisco J., Vázquez-López, José A., Barranco-Gutiérrez, Alejandro I.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2022
Subjects
Algorithms
Approximation
Artificial intelligence
autonomous electric vehicle (AEV)
Cameras
Data acquisition
Data analysis
Data integration
Electric vehicles
Error reduction
Food science
Fuzzy logic
fuzzy logic (FL)
Global positioning systems
GPS
Methods
Navigation systems
Neural networks
odometer
Odometers
Sensors
Vehicles
visual odometry (VO)
ZED camera
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Summary:The incorporation of high precision vehicle positioning systems has been demanded by the autonomous electric vehicle (AEV) industry. For this reason, research on visual odometry (VO) and Artificial Intelligence (AI) to reduce positioning errors automatically has become essential in this field. In this work, a new method to reduce the error in the absolute location of AEV using fuzzy logic (FL) is presented. The cooperative data fusion of GPS, odometer, and stereo camera signals is then performed to improve the estimation of AEV localization. Although the most important challenge of this work focuses on the reduction in the odometry error in the vehicle, the defiance of synchrony and the information fusion of sources of different nature is solved. This research is integrated by three phases: data acquisition, data fusion, and statistical evaluation. The first one is data acquisition by using an odometer, a GPS, and a ZED camera in AVE’s trajectories. The second one is the data analysis and fuzzy fusion design using the MatLab 2019® fuzzy logic toolbox. The last is the statistical evaluation of the positioning error of the different sensors. According to the obtained results, the proposed model with the lowest error is that which uses all sensors as input (stereo camera, odometer, and GPS). It can be highlighted that the best proposed model manages to reduce the positioning mean absolute error (MAE) up to 25% with respect to the state of the art.
ISSN:2227-7390
2227-7390
DOI:10.3390/math10122052