- author = "Martin C. Martin",
- title = "Evolving visual sonar: Depth from monocular images",
- journal = "Pattern Recognition Letters",
- year = "2006",
- volume = "27",
- number = "11",
- pages = "1174--1180",
- month = aug,
- note = "Evolutionary Computer Vision and Image Understanding",
- keywords = "genetic algorithms, genetic programming, Robotics, Visual navigation, Monocular vision",
-
URL = "
http://martincmartin.com/papers/EvolvingVisualSonarPatternRecognitionLetters2006.pdf",
-
DOI = "
10.1016/j.patrec.2005.07.015",
- size = "7 pages",
-
abstract = "To recover depth from images, the human visual system
uses many monocular depth cues, which vision research
has only begun to explore. Because a given image can
have many possible interpretations, constraints are
needed to eliminate ambiguity, and the most powerful
constraints are domain specific. As an experiment in
the automatic discovery and exploitation of
constraints, genetic programming was used to find
algorithms for obstacle detection. The algorithms are
designed to be a replacement for sonar, returning the
location of the nearest obstacle in a given
direction.
The evolved algorithms worked surprisingly well. Errors were largely transient. The algorithms generalised to both novel views of the office environment and to unseen obstacles. They were combined with a simple reactive wandering program originally written for sonar. The result exhibited good performance in an office environment, colliding only with obstacles outside the robot's field of view.
Time to collision results and failure modes are presented. Code is available for download.",
