Robot localization and mapping problem with bounded noise uncertainties

Hamzah Ahmad, Toru Namerikawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper deals with H Filter based SLAM which is also known as minimax filter to estimate robot and landmarks location whose able to stand for non-gaussian noise characteristics. Based on our findings, by selecting appropriate γ and initial state covariance matrix in H Filter, the estimation results can show better performance in comparison to the Kalman Filter approach. From the analysis of convergence properties of H Filter, it is found that the filter is capable to provide a reliable estimation. Besides, from the simulation results, H Filter produces better outcome than the Kalman Filter in the nonlinear case estimation. These condition subsequently provides alternative estimation techniques with the capability to ensure and improve estimation in the robotic mapping problem especially in SLAM.

Original languageEnglish
Title of host publicationISIEA 2012 - 2012 IEEE Symposium on Industrial Electronics and Applications
Pages187-192
Number of pages6
DOIs
Publication statusPublished - 2012 Dec 1
Event2012 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2012 - Bandung, Indonesia
Duration: 2012 Sep 232012 Sep 26

Publication series

NameISIEA 2012 - 2012 IEEE Symposium on Industrial Electronics and Applications

Other

Other2012 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2012
CountryIndonesia
CityBandung
Period12/9/2312/9/26

Keywords

  • Estimation
  • H Filter
  • Kalman Filter
  • Nonlinear
  • SLAM

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Robot localization and mapping problem with bounded noise uncertainties'. Together they form a unique fingerprint.

Cite this