Runway obstacle detection by controlled spatiotemporal image flow disparity

Sanghoon Sull, Banavar Sridhar

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


This paper proposes a method for detecting obstacles on a runway by controlling their expected flow disparities. The runway is modeled as a planar surface. By approximating the runway by a planar surface, the initial model flow field (MFF) corresponding to an obstacle-free runway is described by the data from on-board sensors (OBS). The initial residual flow field (RFF) is obtained after warping (or stabilizing) the image using the initial MFF. The error variance of the initial MFF is estimated. The initial RFF and the error variance are first used to identify the pixels corresponding to the obstacle-free runway and then to noniteratively estimate the MFF and RFF. Obstacles are detected by comparing the expected residual flow disparities with the RFF. Expected temporal and spatial residual disparities are obtained from the use of the OBS. This allows us to control the residual disparities by increasing the temporal baseline and/or by utilizing the spatial baseline if distant objects cannot be detected for a given temporal baseline. Experimental results for two real flight image sequences are presented.

Original languageEnglish
Pages (from-to)537-547
Number of pages11
JournalIEEE Transactions on Robotics and Automation
Issue number3
Publication statusPublished - 1999

Bibliographical note

Funding Information:
Manuscript received May 16, 1996; revised January 12, 1999. This paper was recommended for publication by Associate Editor R. Chatila and Editor S. Salcudean upon evaluation of the reviewers’ comments. This work was supported in part by a National Research Council Research Associateship at NASA Ames Research Center and the Korea Research Foundation.

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


Dive into the research topics of 'Runway obstacle detection by controlled spatiotemporal image flow disparity'. Together they form a unique fingerprint.

Cite this