Quantifying Robot Localization Safety: A New Integrity Monitoring Method for Fixed-Lag Smoothing

Osama Abdul Hafez, Guillermo Duenas Arana, Mathieu Joerger, Matthew Spenko

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Localization safety, or integrity risk, is the probability of undetected localization failures and a common aviation performance metric used to verify a minimum accuracy requirement. As autonomous robots become more common, applying integrity risk metrics will be necessary to verify localization performance. This letter introduces a new method, solution separation, to quantify landmark-based mobile robot localization safety for fixed-lag smoothing estimators and compares it's computation time and fault detection capabilities to a chi-squared integrity monitoring method. Results show that solution separation is more computationally efficient and results in a tighter upper-bound on integrity risk when few measurements are included, which makes it the method of choice for lightweight, safety-critical applications such as UAVs. Conversely, chi-squared requires more computing resources but performs better when more measurements are included, making the method more appropriate for high performance computing platforms such as autonomous vehicles.

Original languageEnglish (US)
Article number9006956
Pages (from-to)3182-3189
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume5
Issue number2
DOIs
StatePublished - Apr 2020
Externally publishedYes

Keywords

  • autonomous vehicle navigation
  • Localization
  • performance evaluation and benchmarking
  • probability and statistical methods
  • robot safety

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence

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