EM-EKF based visual SLAM for simple robot localization

Author

Minxiang Liu ; Leung, Henry

Author_Institution

Dept. of Electr. & Comput. Eng., Univ. of Calgary, Calgary, AB, Canada

fYear

2014

fDate

5-8 Oct. 2014

Firstpage

3121

Lastpage

3125

Abstract

This paper presents a novel SLAM method based on the filter that fuses EM algorithm in EKF. Due to the visual SLAM mostly depends on the sensor information that is hard to be obtained in high-level accuracy, the proposed filter is designed to deal with this problem since it can estimate the unknown parameter from the known information in every frame. Realtime experimental results also prove the advantages of SLAM method based on the proposed filter. Compared to the regular EKF, SLAM based on EM-EKF is suggested to have up to 60 percent improvement in the accuracy. It also shows the advantage in the convergence speed and the stability of the system.

Keywords

Kalman filters; SLAM (robots); expectation-maximisation algorithm; image sensors; mobile robots; nonlinear filters; robot vision; EM algorithm; EM-EKF based visual SLAM; convergence speed; expectation-maximisation algorithm; extended Kalman filter; high-level accuracy; robot localization; sensor information; stability; visual SLAM; Accuracy; Cameras; Nickel; Robot kinematics; Simultaneous localization and mapping; 3D-SLAM; EM-EKF; Kinect; robot navigation;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics (SMC), 2014 IEEE International Conference on

Conference_Location

San Diego, CA

Type

conf

DOI

10.1109/SMC.2014.6974407

Filename

6974407