Abstract
Continuous precise navigation is essential for many intelligent transportation systems (ITS) applications. It is a common understanding that for the challenges of advanced applications, global navigation satellite systems (GNSS) based solutions must be complemented with additional sensors. A very simple and still very competent option is the inclusion of one odometer and one gyroscope in the onboard sensing unit. This choice has the benefits that its errors can be easily characterized in normal conditions, and keeps the costs low. However, the intrinsic nature of the odometry system causes important diminutions of its performance in unusual friction conditions, such as slides and slips. This paper proposes a method to detect and compensate the errors made in the odometry in unusual friction conditions by means of a multiple model particle filter (MMPF) based hybridization. In concrete, we focus on slides since these appear more often in road vehicles. The theoretical contributions, along with the good results obtained in real trials are presented in the paper.
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Toledo-Moreo, R., Colodro-Conde, C., Toledo-Moreo, F.J. (2015). A Multiple-Model Particle Filter Based Method for Slide Detection and Compensation in Road Vehicles. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Bioinspired Computation in Artificial Systems. IWINAC 2015. Lecture Notes in Computer Science(), vol 9108. Springer, Cham. https://doi.org/10.1007/978-3-319-18833-1_17
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DOI: https://doi.org/10.1007/978-3-319-18833-1_17
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