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Selection of Landmarks for Visual Landmark Navigation

Kootstra, G. (2002) Selection of Landmarks for Visual Landmark Navigation. Master's Thesis / Essay, Artificial Intelligence.

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Abstract

Insects are remarkably apt in navigating through a complex environment. Honeybees for instance are able to return to a location hundreds of meters away. What is so remarkable is that insects have this ability, although they have a tiny brain. This means that nature has found an accurate and economical way to deal with the navigation problem. The autonomous robots we use today face the same problem as the insects: These systems also need a good navigation ability, despite the fact that their computational power is limited. Robotics can learn a lot from nature. This is the field of biorobotics. In the navigation task of finding back a location, bees use visual landmarks in the surroundings which pinpoint the location. But bees do not just use all the landmarks that are available in the surroundings of the goal location. They use the landmarks close to the goal for detailed navigation, since these landmarks best pinpoint the location. In order to select these nearby landmarks, a bee performs a turn-back-and-look behaviour (TBL). The image motion generated by the TBL provides the bee with information about the three-dimensional structure of the goal's surroundings. This information enables the bee to select reliable landmarks that are close to the goal location. When selecting the landmark, the bee learns the color, shape and size of the landmark, in order to be able to find back the goal location from any given other location from where the landmarks are visible. We modeled this behaviour of using image flow to learn the useful landmarks in the goal's surroundings. To detect the motion flow we used an adapted version of the Elementary Motion Detector (EMD). The model is implemented on a freely flying robot, equipped with a omni-directional camera. The robot selects the reliable landmarks based on the image flow that appears when the robot is in egomot ion.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Artificial Intelligence
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 07:30
Last Modified: 15 Feb 2018 07:30
URI: http://fse.studenttheses.ub.rug.nl/id/eprint/9053

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