Abstract
Continuous accurate positioning is a key element for the deployment of many advanced driver assistance systems (ADAS) and autonomous vehicle navigation. To achieve the necessary performance, global navigation satellite systems (GNSS) must be combined with other technologies. A common onboard sensor-set that allows keeping the cost low, features the GNSS unit, odometry, and inertial sensors, such as a gyro. Odometry and inertial sensors compensate for GNSS flaws in many situations and, in normal conditions, their errors can be easily characterized, thus making the whole solution not only more accurate but also with more integrity. However, odometers do not behave properly when friction conditions make the tires slide. If not properly considered, the positioning perception will not be sound. This article introduces a hybridization approach that takes into consideration the sliding situations by means of a multiple model particle filter (MMPF). Tests with real datasets show the goodness of the proposal.
Highlights
Today’s most outstanding technology for road vehicle positioning is global navigation satellite systems (GNSS)
It is recommended that the positioning system features some other sensors that, together with the GNSS, provide an overall better performance
This article presents a multiple-model particle filter (MMPF) based solution that features two different models running in parallel, one that accounts for slides errors, and another one that does not, choosing at any time which model represents better the vehicle behavior
Summary
Today’s most outstanding technology for road vehicle positioning is global navigation satellite systems (GNSS). Odometry errors may be due to mechanical or electrical (quantization) issues; tire wear, which changes the wheel diameter over time; and slippages and slides Among these, the latter is the one that accounts for larger errors in the provision of instant positioning. GNSS/DR fusion algorithms do not usually account for slide errors When it happens, the solution is more inaccurate, and inconsistent, Appl. Sci. 2018, 8, 445 since the errors considered by the algorithm are underestimated This may lead to the malfunction of applications that are based on the confidence of the position solution. This article presents a multiple-model particle filter (MMPF) based solution that features two different models running in parallel, one that accounts for slides errors, and another one that does not, choosing at any time which model represents better the vehicle behavior.
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