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Mathematical Problems in Engineering
Volume 2017, Article ID 4197635, 9 pages
Research Article

A FastSLAM Algorithm Based on Nonlinear Adaptive Square Root Unscented Kalman Filter

1School of Electrical and Control Engineering, Xi’an University of Science and Technology, Shaanxi, Xi’an 710054, China
2Key Laboratory of Embedded System and Service Computing, Tongji University, Ministry of Education, Shanghai 201804, China
3713th Institute of China Shipbuilding Industry Corporation, Zhengzhou, China
4Naval Representative Office in Zhengzhou Region, Zhengzhou, China

Correspondence should be addressed to Yu-feng Zhang; moc.361@gnefuygnahzdkx

Received 26 August 2016; Revised 15 December 2016; Accepted 6 February 2017; Published 15 March 2017

Academic Editor: Francisco Valero

Copyright © 2017 Yu-feng Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


For fast simultaneous localization and mapping (FastSLAM) problem, to solve the problems of particle degradation, the error introduced by linearization and inconsistency of traditional algorithm, an improved algorithm is described in the paper. In order to improve the accuracy and reliability of algorithm which is applied in the system with lower measurement frequency, a new decomposition strategy is adopted for a posteriori estimation. In proposed decomposition strategy, the problem of solving a 3-dimensional state vector and N 2-dimensional state vectors in traditional FastSLAM algorithm is transformed to the problem of solving N 5-dimensional state vectors. Furthermore, a nonlinear adaptive square root unscented Kalman filter (NASRUKF) is used to replace the particle filter and Kalman filter employed by traditional algorithm to reduce the model linearization error and avoid solving Jacobian matrices. Finally, the proposed algorithm is experimentally verified by vehicle in indoor environment. The results prove that the positioning accuracy of proposed FastSLAM algorithm is less than 1 cm and the azimuth angle error is 0.5 degrees.