Monocular simultaneous localization and mapping with a modified covariance Extended Kalman Filter

Author

Meng, Xujiong ; Hong, Feng ; Chen, Yaowu

Author_Institution

Inst. of Adv. Digital Technol. & Instrum., Zhejiang Univ., Hangzhou, China

Volume

2

fYear

2009

Firstpage

900

Lastpage

903

Abstract

A modified covariance extended Kalman filter (MVEKF) algorithm is proposed to the monocular simultaneous localization and mapping (SLAM) in this paper. Recent literatures have shown that it is possible to solve the monocular SLAM using the Extended Kalman Filter (EKF) and the inverse-depth parameterization. However, the EKF algorithm has its intrinsic disadvantage such as the divergence. Here we propose the use of MVEKF algorithm to improve the performance of monocular SLAM. Experiments were carried out on indoor image sequences and result show that the MVEKF algorithm could improve the convergence of landmarks.

Keywords

Kalman filters; SLAM (robots); robot vision; SLAM; covariance extended Kalman filter; image sequence; inverse-depth parameterization; landmark convergence; monocular simultaneous localization and mapping; Angular velocity; Cameras; Convergence; Image sequences; Instruments; Inverse problems; Laser modes; Optical filters; Robustness; Simultaneous localization and mapping; Extended Kalman Filter; modified covariance Extended Kalman Filter; monocular SLAM;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on

Print_ISBN

978-1-4244-4754-1

Electronic_ISBN

978-1-4244-4738-1

Type

conf

DOI

10.1109/ICICISYS.2009.5358248

Filename

5358248