Title :
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
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;
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
DOI :
10.1109/ICICISYS.2009.5358248
