Feature extraction for target spacecraft in the final approaching phase of rendezvous and docking

  • Wenjing Pei
  • , Yingmin Jia*
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The rendezvous and docking technology is the key issue to accomplish the spacecraft maintenance in-orbit, space station supply, and astronauts visiting. The technology of the vision measurement for spacecraft directly determines that its results are successful or fail in the final approaching phase of rendezvous and docking. Feature extraction for target spacecraft includes edges, lines, circles, and especially motion information. In this paper, image smoothing based on 2-Dimensional Adaptive Wiener Filtering is introduced, then Canny Edge Detector is used for edge detection, finally using Standard Hough Transform to extract lines and the characteristic circle. Experimental results show that the computation is obviously improved, meanwhile, the precision of detection is also improved. What’ s more, lines and circles detection as the fundamental step of extracting motion information, even the vision measurement for spacecraft position and attitude determination, is absolutely necessary.

Original languageEnglish
Title of host publicationProceedings of 2017 Chinese Intelligent Systems Conference
EditorsJunping Du, Weicun Zhang, Yingmin Jia
PublisherSpringer Verlag
Pages763-771
Number of pages9
ISBN (Print)9789811064951
DOIs
StatePublished - 2018
EventChinese Intelligent Systems Conference, CISC 2017 - Mudanjiang, China
Duration: 14 Oct 201715 Oct 2017

Publication series

NameLecture Notes in Electrical Engineering
Volume459
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

ConferenceChinese Intelligent Systems Conference, CISC 2017
Country/TerritoryChina
CityMudanjiang
Period14/10/1715/10/17

Keywords

  • 2-D adaptive wiener filtering
  • Lines and the characteristic circle detection
  • Rendezvous and docking
  • Standard hough transform

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