Abstract
The use of certain measures of flow field divergence is investigated as a qualitative cue for obstacle avoidance during visual navigation. It is shown that a quantity termed the directional divergence of the 2-D motion field can be used as a reliable indicator of the presence of obstacles in the visual field of an observer undergoing generalized rotational and translational motion. The necessary measurements can be robustly obtained from real image sequences. Experimental results are presented showing that the system responds as expected to divergence in real-world image sequences, and the use of the system to navigate between obstacles is demonstrated.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Keywords
Artificial intelligenceObstacle avoidanceComputer visionOptical flowDivergence (linguistics)ObstacleObserver (physics)Computer scienceField (mathematics)Visual fieldImage (mathematics)MathematicsGeographyRobotPhysicsPure mathematicsMobile robot
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Publication Info
- Year
- 1989
- Type
- article
- Volume
- 11
- Issue
- 10
- Pages
- 1102-1106
- Citations
- 222
- Access
- Closed
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Cite This
Randal C. Nelson,
John Aloimonos
(1989).
Obstacle avoidance using flow field divergence.
IEEE Transactions on Pattern Analysis and Machine Intelligence
, 11
(10)
, 1102-1106.
https://doi.org/10.1109/34.42840
Identifiers
- DOI
- 10.1109/34.42840