Abstract
Position estimation is a fundamental problem for an autonomous mobile robot. Extended Kalman filter is an efficient tool for mobile robot pose tracking, but it suffers from linearization errors due to linear approximation of nonlinear system equations. In this paper we describe a position estimation method with linear system models. The position of mobile robot is indirectly represented with an augmented system state vector. The coordinate of landmark is considered as observation information. In this way, motion model and observation model are linear. The position of mobile robot is estimated recursively based on optimal KF. It avoids linear approximation of nonlinear system equations and is free of linearization error. All these techniques have been implemented on our mobile robot ATRVII equipped with 2D laser rangefinder SICK
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.
