A landmark vector approach using gray-colored information

Changmin Lee, Dae Eun Kim

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

1 Citation (Scopus)


Homing navigation is an important aspect in navigation behaviours of animals. There has been many types of navigation but we focus on the vision-based landmark navigation to return home. Visual navigation is involved with image matching process over snapshot images. Landmark vector methods simplify the environmental information into a set of landmark vectors, and then compare the landmark vectors obtained from each snapshot. In this paper, we encode landmark vectors using the gray-colored values as the length of vectors. Then we apply the landmark arrangement method to those landmark vectors. Using the gray-colored information, we can estimate the homing direction at a given position. We show that the suggested method is effective in homing navigation.

Original languageEnglish
Title of host publicationFrom Animals to Animats - 14th International Conference on Simulation of Adaptive Behavior, SAB 2016, Proceedings
EditorsJohn Hallam, Elio Tuci, Alexandros Giagkos, Myra Wilson
PublisherSpringer Verlag
Number of pages7
ISBN (Print)9783319434872
Publication statusPublished - 2016
Event14th International Conference on Simulation of Adaptive Behavior, SAB 2016 - Aberystwyth, United Kingdom
Duration: 2016 Aug 232016 Aug 26

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9825 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other14th International Conference on Simulation of Adaptive Behavior, SAB 2016
Country/TerritoryUnited Kingdom

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2014R1A2A1A11053839).

Publisher Copyright:
© Springer International Publishing Switzerland 2016.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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