DocumentCode :
3186592
Title :
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
Link To Document :
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