An Improved Localization Method for Indoor Service Robot

Author

Lili, Dong ; Zhengda, Meng ; Xiao, Lu

Author_Institution

Sch. of Autom., Southeast Univ., Nanjing, China

fYear

2010

fDate

25-27 June 2010

Firstpage

395

Lastpage

399

Abstract

In order to improve the stability of localization results and particle convergence, four of the particle filter resampling algorithms for indoor service robot localization are described in this paper. They are Multinomial Resampling, Residual Resampling, Stratified Resampling and Systematic Resampling. The simulation and comparison is also presented. Regarding the ranging accuracy of HHR-0303 service robot, the data of sonar and infrared are fused to extract the environmental features, and then combined with known map so as to increase the richness of posterior probability matching-data in particle filter algorithm. The experiment has been done on the robot. The experiment proved that the localization plan which adopts the Residual Resampling algorithm with sensor data fusion can achieve the localization aim.

Keywords

particle filtering (numerical methods); position control; sampling methods; sensor fusion; service robots; HHR-0303 service robot; indoor service robot; infrared sensor data; localization method; multinomial resampling algorithm; particle filter resampling algorithms; posterior probability matching-data; residual resampling algorithm; sensor data fusion; sonar sensor data; stratified resampling algorithm; systematic resampling algorithm; Algorithm design and analysis; Data models; Particle filters; Robot sensing systems; Service robots; Sonar; localization; particle filter; resampling algorithm; sensor data fusion; service robot;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Control Engineering (ICECE), 2010 International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-6880-5

Type

conf

DOI

10.1109/iCECE.2010.103

Filename

5631889