Goal seeking in honeybees: matching of optic flow snapshots?
Author(s) -
Laura Dittmar,
Wolfgang Stürzl,
Emily Baird,
Norbert Boeddeker,
Martin Egelhaaf
Publication year - 2010
Publication title -
journal of experimental biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.367
H-Index - 185
eISSN - 1477-9145
pISSN - 0022-0949
DOI - 10.1242/jeb.043737
Subject(s) - landmark , computer vision , artificial intelligence , computer science , matching (statistics) , scheme (mathematics) , contrast (vision) , robot , sensory cue , representation (politics) , texture (cosmology) , pattern recognition (psychology) , image (mathematics) , mathematics , mathematical analysis , statistics , politics , political science , law
Visual landmarks guide humans and animals including insects to a goal location. Insects, with their miniature brains, have evolved a simple strategy to find their nests or profitable food sources; they approach a goal by finding a close match between the current view and a memorised retinotopic representation of the landmark constellation around the goal. Recent implementations of such a matching scheme use raw panoramic images ('image matching') and show that it is well suited to work on robots and even in natural environments. However, this matching scheme works only if relevant landmarks can be detected by their contrast and texture. Therefore, we tested how honeybees perform in localising a goal if the landmarks can hardly be distinguished from the background by such cues. We recorded the honeybees' flight behaviour with high-speed cameras and compared the search behaviour with computer simulations. We show that honeybees are able to use landmarks that have the same contrast and texture as the background and suggest that the bees use relative motion cues between the landmark and the background. These cues are generated on the eyes when the bee moves in a characteristic way in the vicinity of the landmarks. This extraordinary navigation performance can be explained by a matching scheme that includes snapshots based on optic flow amplitudes ('optic flow matching'). This new matching scheme provides a robust strategy for navigation, as it depends primarily on the depth structure of the environment.
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