Using the motion perceptibility measure to classify points of interest for visual-based AUV guidance in a reef ecosystem

Lourdes Labastida-Valdes, L. Abril Torres-Mendez, Seth A. Hutchinson

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

Abstract

In this paper we present a novel method for classifying relevant points in a sequence of images of a distant target to autonomously guide an underwater vehicle towards it. Feature points are classified by using a measure called motion perceptibility, which relates the magnitudes of the rate of change between matched feature points at different image frames (in distance), thus inherently considering the change in feature's position. This measure helps to detect which feature points are most likely to leave the field of view of the camera, thus indicating that they do not belong to the target region. By using a visual attention model adapted to underwater images, relevant points are detected and tracked using a visual servoing approach. Preliminary results on sea trials demonstrate the feasibility of our methodology.

Original languageEnglish (US)
Title of host publicationOCEANS 2015 - MTS/IEEE Washington
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780933957435
StatePublished - Feb 8 2016
EventMTS/IEEE Washington, OCEANS 2015 - Washington, United States
Duration: Oct 19 2015Oct 22 2015

Other

OtherMTS/IEEE Washington, OCEANS 2015
CountryUnited States
CityWashington
Period10/19/1510/22/15

ASJC Scopus subject areas

  • Signal Processing
  • Oceanography
  • Ocean Engineering
  • Instrumentation
  • Acoustics and Ultrasonics

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