Schoute, Albert

van der Heijden, Ferdinand

Zivkovic, Zoran

English

The problem of updating the global position of an autonomous vehicle is considered. An iterative procedure is proposed to fit a map to a set of noisy measurements. The procedure is inspired by a non-parametric procedure for probability density function mode searching. We show how this could be used to solve the global position update problem in an efficient and robust way. The simple procedure combines the a priori knowledge from the known map of the environment, the extracted information from calibrated camera images and the information from the vehicle wheels rotation measurements. Because it is very simple the procedure is appropriate to be performed in real-time

University of Twente Research Information

Combining A Priori Knowledge and Sensor Information for Updating the Global Position of an Autonomous Vehicle

https://ris.utwente.nl/ws/files/5374717/aschoute.pdf

Combining A Priori Knowledge and Sensor Information for Updating the Global Position of an Autonomous Vehicle

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University of Twente Research Information