Title :
Cubature MCL: Mobile robot Monte Carlo Localization based on Cubature Particle Filter
Author :
Li Qingling ; Song Yu
Author_Institution :
Sch. of Mech. Electron. & Inf. Eng., China Univ. of Min. & Technol., Beijing, China
Abstract :
Particle Filter is the key issue in mobile robot MCL (Monte Carlo Loclaization, MCL). To overcome particle set degeneracy phenomenon of the traditional MCL algorithm, a new Cubature MCL algorithm is proposed in this paper. The proposed Cubature MCL algorithm utilizes Cubature Kalman filter to generate more accuracy proposal distribution, which introduce most recent measurements into Sequential Importance Sampling (SIS) routine of the particle filter. The performance of the Cubature MCL algorithm is presented and analyzed in simulations. The results verify the effectiveness of the proposed Cubature MCL algorithm. The Cubature MCL provides a valuable reference for the mobile robot localization algorithm research.
Keywords :
Kalman filters; importance sampling; mobile robots; particle filtering (numerical methods); Cubature Kalman filter; Cubature MCL algorithm; Cubature particle filter; SIS; mobile robot MCL; mobile robot Monte Carlo localization; particle set degeneracy phenomenon; sequential importance sampling routine; Educational institutions; Electronic mail; Mobile robots; Monte Carlo methods; Particle filters; Robustness; Cubature rule; Gaussian weighted integral; Mobile robot; Monte Carlo localization; Particle filter;
Conference_Titel :
Control Conference (CCC), 2012 31st Chinese
Conference_Location :
Hefei
Print_ISBN :
978-1-4673-2581-3