Relative-Absolute Map Filter for Simultaneous Localization and Mapping

Author

Chung, Shu Yun ; Huang, Han Pang

Author_Institution

Dept. of Mech. Eng., Nat. Taiwan Univ., Taipei

fYear

2006

fDate

Oct. 2006

Firstpage

436

Lastpage

441

Abstract

In this paper, a new algorithm, relative-absolute map filter (RAMF), is proposed to solve the simultaneous localization and mapping problem. Compared with FastSLAM, which adopts many absolute maps to describe the relationship between features, RAMF utilizes only one relative map instead. By fusing the information of relative map and absolute map, RAMF can create a more accurate map. Moreover, the embedded particle filter in RAMF can handle robot localization. Simulation results show that RAMF has better performance than FastSLAM and UKF SLAM in the noisy robot motion

Keywords

SLAM (robots); mobile robots; motion control; embedded particle filter; mobile robots; relative-absolute map filter; robot localization; simultaneous localization and mapping; Mechanical engineering; Mobile robots; Particle filters; Robot localization; Robot motion; Robot sensing systems; Robustness; Simultaneous localization and mapping; State estimation; Stochastic processes; Particle Filter (PF); SLAM; absolute map filter (AMF); relative map filter (RMF);

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on

Conference_Location

Beijing

Print_ISBN

1-4244-0259-X

Electronic_ISBN

1-4244-0259-X

Type

conf

DOI

10.1109/IROS.2006.282023

Filename

4059112