SRCKF based simultaneous localization and mapping of mobile robots

被引：0

作者：

Wang, Hongjian ^{[1
]}

Fu, Guixia ^{[1
]}

Bian, Xinqian ^{[1
]}

Li, Juan ^{[1
]}

机构：

[1] College of Automation, Harbin Engineering University

来源：

Jiqiren/Robot | 2013年 / 35卷 / 02期

关键词：

Mobile robot; Simultaneous localization and mapping; Square root cubature Kalman filter;

D O I：

10.3724/SP.J.1218.2013.00200

中图分类号：

学科分类号：

摘要：

In order to solve the large computing cost and numerical instabilities of simultaneous localization and mapping (SLAM), a square root cubature Kalman filter (SRCKF) based SLAM algorithm (SRCKF-SLAM) for mobile robots is designed according to cubature Kalman filter (CKF) principle. The SRCKF-SLAM algorithm accomplishes prediction and observation through motion model and observation model, and it is updated iteratively by propagating square root factors of the mean and covariance of the state variable, which guarantees the symmetry and positive semi-definiteness of the covariance matrix and therefore improves numerical accuracy and stability. The simulation experiments show that, compared with the CKF-SLAM algorithm, the root-mean square error of the SRCKF-SLAM algorithm decreases 14%, and the percentage of points in consistency area increases 36%, therefore the SRCKF-SLAM algorithm effectively satisfies the requirement of SLAM navigation of mobile robots.

引用

页码：200 / 207

页数：7

共 22 条

[1]

Durrant-Whyte H.F., Bailey T., Simultaneous localization and mapping (SLAM): Part I, The essential algorithms, IEEE Robotics and Automation Magazine, 13, 2, pp. 99-108, (2006)

[2]

Bailey T., Durrant-Whyte H.F., Simultaneous localization and mapping (SLAM): Part II, State of the art, IEEE Robotics and Automation Magazine, 13, 3, pp. 108-117, (2006)

[3]

Smith R.C., Cheeseman P., On the representation and estimation of spatial uncertainty, International Journal of Robotics Research, 5, 4, pp. 56-68, (1986)

[4]

Thrun S., Liu Y.F., Koller D., Et al., Simultaneous localization and mapping with sparse extended information filters, International Journal of Robotics Research, 23, 7-8, pp. 693-716, (2004)

[5]

Julier S.J., Uhlmann J.K., Unscented filtering and nonlinear estimation, Proceedings of the IEEE, 92, 3, pp. 401-422, (2004)

[6]

Bharani Chandra K.P., Gu D.W., Postlethwaite I., Cubature Kalman filter based localization and mapping, 18th IFAC World Congress, pp. 2121-2125, (2011)

[7]

Doucet A., De Freitas N., Murphy K., Et al., Rao-Blackwellised particle filtering for dynamic Bayesian networks, 16th Conference on Uncertainty in Artificial Intelligence, pp. 176-183, (2000)

[8]

Doucet A., Godsill S., Andrieu C., On sequential Monte Carlo sampling methods for Bayesian filtering, Statistics and Computing, 10, 3, pp. 197-208, (2000)

[9]

Song Y., Li Q.L., Kang Y.F., Et al., Square-root cubature Fast-SLAM algorithm for mobile robot simultaneous localization and mapping, IEEE International Conference on Mechatronics and Automation, pp. 1162-1167, (2012)

[10]

Arasaratnam I., Haykin S., Cubature Kalman filters, IEEE Transactions on Automatic Control, 54, 6, pp. 1254-1269, (2009)

← 1 2 3 →