Computing the sensory uncertainty field of a vision-based localization sensor

Amit Adam, Ehud Rivlin, Ilan Shimshoni

Research output: Contribution to journalArticlepeer-review


Recently it has been recognized that robust motion planners should take into account the varying performance of localization sensors across the configuration space. Although a number of works have shown the benefits of using such a performance map, the work on actual computation of such a performance map has been limited and has addressed mostly range sensors. Since vision is an important sensor for localization, it is important to have performance maps of vision sensors. In this paper we present a method for computing the performance map of a vision-based sensor. We compute the map and show that it accurately describes the actual performance of the sensor, both on synthetic and real images. The method we use (following [10]) involves evaluating closed form formulas and hence is very fast. Using the performance map computed by this method for motion planning and for devising sensing strategies will contribute to more robust navigation algorithms.

Original languageEnglish
Pages (from-to)258-267
Number of pages10
JournalIEEE Transactions on Robotics and Automation
Issue number3
StatePublished - Jun 2001
Externally publishedYes


  • Motion planning
  • Performance evaluation
  • Uncertainty
  • Vision-based localization

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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