Abstract
In this paper, a novel methodology is proposed to solve the simultaneous localization and mapping (SLAM) problem of mobile robot with particle filter (PF) algorithm. Compared with Kalman filter (KF) and extended Kalman filter (EKF), PF has a better performance in non-linear non-Gaussian environments. A close-loop updating scheme is developed in which positions of the robot and landmarks are updated with particle filtering and a weighted averaging algorithm respectively, and are linked through an additional feedback and correction process. An adaptive re-sampling method is used to reduce the computational load. The results of the simulation indicate that the PF-SLAM algorithm can localize the robot and landmarks accurately, and the error of landmarks' estimation converges better than general SLAM algorithms.
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.
