Abstract
Autonomous vehicles leverage the data provided by a suite of sensors, combining measurements in order to provide precise and robust position estimation to localization and navigation systems. In this paper, an Adaptive Monte Carlo Localization algorithm is applied to an autonomous golf car, where data from wheel odometry, an inertial measurement unit, a Global Positioning System (GPS) and laser scanning is combined to estimate the pose of a vehicle in an outdoor environment. Monte Carlo Localization techniques allow the compensation of the technical flaws of different sensors by fusing the information delivered by each one. However, one of the main problems of fusing GPS data are sudden decreases of accuracy and sudden jumps on positions due to phenomenons like multi–path signal reception. In this paper, a particle weighting MCL model which integrates GPS measurements is proposed, and its performance is compared in several experiments with a particle generation approach when a GPS sensor suddenly provides erroneous data.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
