Observability analysis of 2D geometric features using the condition number for SLAM applications

Suyong Yeon, Nakju Lett Doh

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

1 Citation (Scopus)

Abstract

Observability analysis is a very powerful tool for discriminating whether a robot can estimate its own state. However, this method cannot investigate how much of the system is observable. This is a major problem from a state estimation perspective because there is too much noise in real environments. Therefore, although the system (or a mobile robot) is observable, it cannot estimate its own state. To address this problem, we propose an observability analysis method that uses the condition number. Mathematically, the condition number of matrix represents a degree of robustness to noise. We utilize this property of the condition number to investigate the degree of observability. In other words, the condition number of the observability matrix demonstrates the feasibility of state estimation and the robustness of its feasibility for estimation.

Original languageEnglish
Title of host publicationICCAS 2013 - 2013 13th International Conference on Control, Automation and Systems
Pages1540-1543
Number of pages4
DOIs
Publication statusPublished - 2013
Event2013 13th International Conference on Control, Automation and Systems, ICCAS 2013 - Gwangju, Korea, Republic of
Duration: 2013 Oct 202013 Oct 23

Publication series

NameInternational Conference on Control, Automation and Systems
ISSN (Print)1598-7833

Other

Other2013 13th International Conference on Control, Automation and Systems, ICCAS 2013
CountryKorea, Republic of
CityGwangju
Period13/10/2013/10/23

Keywords

  • SLAM
  • condition number
  • localization
  • observability analysis
  • state estimation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Observability analysis of 2D geometric features using the condition number for SLAM applications'. Together they form a unique fingerprint.

  • Cite this

    Yeon, S., & Doh, N. L. (2013). Observability analysis of 2D geometric features using the condition number for SLAM applications. In ICCAS 2013 - 2013 13th International Conference on Control, Automation and Systems (pp. 1540-1543). [6704133] (International Conference on Control, Automation and Systems). https://doi.org/10.1109/ICCAS.2013.6704133