Online 3-D trajectory estimation of a flying object from a monocular image sequence

R. Herrejon, S. Kagami, K. Hashimoto

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

5 Citations (Scopus)

Abstract

The problem considered here involves the design and application of a recursive algorithm to extract and predict the position of an object in a 3D environment from one feature correspondence from a monocular image sequence. Translational model involves an object moving in a parabolic path using projectile physics. A state-space model is constructed incorporating kinematic states, and recursive techniques are used to estimate the state vector as a function of time. The measured data are the noisy image plane coordinates of object match taken from image in the sequence. Image plane noise levels are allowed and investigated. The problem is formulated as a tracking problem, which can use an arbitrary large number of images in a sequence. The recursive estimation is done using Recursive Least Squares (RLS). Results on both synthetic and real imagery illustrate the performance of the estimator.

Original languageEnglish
Title of host publication2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009
Pages2496-2501
Number of pages6
DOIs
Publication statusPublished - 2009 Dec 11
Event2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009 - St. Louis, MO, United States
Duration: 2009 Oct 112009 Oct 15

Publication series

Name2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009

Other

Other2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009
CountryUnited States
CitySt. Louis, MO
Period09/10/1109/10/15

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Control and Systems Engineering

Fingerprint Dive into the research topics of 'Online 3-D trajectory estimation of a flying object from a monocular image sequence'. Together they form a unique fingerprint.

Cite this