Quantifying Feature Association Error in Camera-based Positioning

Chen Zhu, Mathieu Joerger, Michael Meurer

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

1 Scopus citations

Abstract

Camera-based visual navigation techniques can provide six degrees-of-freedom estimates of position and orientation (or pose), and can be implemented at low cost in applications including autonomous driving, indoor positioning, and drone landing. However, feature matching errors may occur when associating measured features in camera images with mapped features in a landmark database, especially when repetitive patterns are in view. A typical example of repetitive patterns is that of regularly spaced windows on building walls. Quantifying the data association risk and its impact on navigation system integrity is essential in safety critical applications. But, literature on vision-based navigation integrity is sparse. This work aims at quantifying and bounding the integrity risk caused by incorrect associations in visual navigation using extended Kalman filters.

Original languageEnglish (US)
Title of host publication2020 IEEE/ION Position, Location and Navigation Symposium, PLANS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages967-972
Number of pages6
ISBN (Electronic)9781728102443
DOIs
StatePublished - Apr 2020
Externally publishedYes
Event2020 IEEE/ION Position, Location and Navigation Symposium, PLANS 2020 - Portland, United States
Duration: Apr 20 2020Apr 23 2020

Publication series

Name2020 IEEE/ION Position, Location and Navigation Symposium, PLANS 2020

Conference

Conference2020 IEEE/ION Position, Location and Navigation Symposium, PLANS 2020
CountryUnited States
CityPortland
Period4/20/204/23/20

ASJC Scopus subject areas

  • Signal Processing
  • Aerospace Engineering
  • Control and Optimization
  • Instrumentation

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