Robot localization and mapping problem with unknown noise characteristics

Hamzah Ahmad, Toru Namerikawa

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

15 Citations (Scopus)

Abstract

In this paper, we examine the H? ?Filter-based SLAM especially about its convergence properties. In contrast to Kalman Filter approach that considers zero mean gaussian noise, H Filter is more robust and may provide sufficient solutions for SLAM in an environment with unknown statistical behavior. Due to this advantage, H Filter is proposed in this paper, to efficiently estimate the robot and landmarks location under worst case situations. H Filter requires the designer to appropriately choose the noise's covariance with respect to γ to obtain a desired outcome. We show some of the conditions to be satisfy in order to achieve better estimation results than Kalman Filter. From the experimental results, HFilter performs better than Kalman Filter for a case of bigger robot initial uncertainties. Subsequently, this proved that H Filter can provide another available estimation method for especially in SLAM.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Control Applications, CCA 2010
Pages1275-1280
Number of pages6
DOIs
Publication statusPublished - 2010
Event2010 IEEE International Conference on Control Applications, CCA 2010 - Yokohama, Japan
Duration: 2010 Sept 82010 Sept 10

Publication series

NameProceedings of the IEEE International Conference on Control Applications

Other

Other2010 IEEE International Conference on Control Applications, CCA 2010
Country/TerritoryJapan
CityYokohama
Period10/9/810/9/10

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Mathematics(all)

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