Integrated position estimation using aerial image sequences

Dong Gyu Sim, Rae Hong Park, Rin Chul Kim, Sang Uk Lee, Ihn Cheol Kim

Research output: Contribution to journalArticlepeer-review

103 Scopus citations

Abstract

This paper presents an integrated system for navigation parameter estimation using sequential aerial images, where navigation parameters represent the position and velocity information of an aircraft for autonomous navigation. The proposed integrated system is composed of two part: relative position estimation and absolute position estimation. Relative position estimation recursively computes the current position of an aircraft by accumulating relative displacement estimates extracted from two successive aerial images. Simple accumulation of parameter values decreases the reliability of the extracted parameter estimates as an aircraft goes on navigating, resulting in a large position error. Therefore, absolute position estimation is required to compensate for the position error generated in relative position estimation. Absolute position estimation algorithms by image matching and digital elevation model (DEM) matching are presented. In image matching, a robust-oriented Hausdorff measure (ROHM) is employed, whereas in DEM matching the algorithm using multiple image pairs is used. Experiments with four real aerial image sequences show the effectiveness of the proposed integrated position estimation algorithm.

Original languageEnglish
Pages (from-to)1-18
Number of pages18
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume24
Issue number1
DOIs
StatePublished - Jan 2002

Keywords

  • Absolute position estimation
  • Aerial image
  • Digital elevation model (DEM)
  • Image matching
  • Navigation
  • Recovered elevation map (REM)
  • Relative position estimation
  • Robust-oriented Hausdorff measure

Fingerprint

Dive into the research topics of 'Integrated position estimation using aerial image sequences'. Together they form a unique fingerprint.

Cite this