Unscented FastSLAM for UAV

Author

Jianli, Shi ; Shuang, Pan ; Wu Yugiang ; Xibin, Wang

Author_Institution

Dept. of Missile Weapon, Naval Submarine Acad., Qingdao, China

Volume

4

fYear

2011

fDate

24-26 Dec. 2011

Firstpage

2529

Lastpage

2532

Abstract

Simultaneous localization and mapping (SLAM) is a necessary prerequisite to make mobile vehicle truly autonomous, which is a hot research topic today. FastSLAM as a successful SLAM method abstracts many researchers´ attentions. FastSLAM factors the SLAM problem into a localization problem and a mapping problem in which the landmark position is estimated by EKF. A modified FastSLAM is presented for uninhabited aerial vehicle (UAV), using UKF to replace the EKF to estimate the landmark position. So we can improve the estimation precision, at the same time no need to linearize the sensor observation model and to compute its Jacobian matrix.

Keywords

Jacobian matrices; Kalman filters; SLAM (robots); autonomous aerial vehicles; nonlinear filters; EKF; Jacobian matrix; SLAM method; UAV; UKF; autonomous mobile vehicle; landmark position estimation; sensor observation model; simultaneous localization and mapping; uninhabited aerial vehicle; unscented FastSLAM; Boolean functions; Data structures; FastSLAM; extend Kalman filter (EKF); simultaneous localization and mapping (SLAM); uninhabited aerial vehicle; unscented Kalman filter (UKF);

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Network Technology (ICCSNT), 2011 International Conference on

Conference_Location

Harbin

Print_ISBN

978-1-4577-1586-0

Type

conf

DOI

10.1109/ICCSNT.2011.6182484

Filename

6182484