On the reduction of raw image data in the context of the appearance-based approach to robot navigation

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dc.contributor.author Labrosse, Frederic
dc.contributor.author Hülse, Martin
dc.contributor.author Lee, Mark
dc.date.accessioned 2010-09-22T15:43:08Z
dc.date.available 2010-09-22T15:43:08Z
dc.date.issued 2010-08
dc.identifier.citation Labrosse , F , Hülse , M & Lee , M 2010 , ' On the reduction of raw image data in the context of the appearance-based approach to robot navigation ' pp. 76 - 82 . en
dc.identifier.other PURE: 151181
dc.identifier.other dspace: 2160/5700
dc.identifier.uri http://hdl.handle.net/2160/5700
dc.description Huelse, M., Labrosse, F., Lee, M.: On the reduction of raw image data in the context of the appearance-based approach to robot navigation. In: Belpaeme, T. et al (Eds.): TAROS 2010, Uni. of Plymouth, UK, 31. August - 2. September 2010, pp. 76 - 82. Sponsorship: EPSRC en
dc.description.abstract The appearance-based approach towards robot navigation is based on a pixel-wise comparison of images. Recent research has shown that the Euclidean distance in image space provides a robust method for robot homing, navigation along routes and topological mapping. The objective of this paper is to investigate how image data can be reduced in order to minimise the computational cost for the image distance calculation without loosing the robustness of the method. A simple 1-D scenario is used to test three different types of reduction methods: one focuses on specific and predefined image regions, one uses fractal sets, while the last is based on a stochastic process. We show that with less than 10% of the data a similar performance can be achieved with the stochastic method, which is then used on a real case study (the visual compass) to assess its performance in a real situation. en
dc.format.extent 7 en
dc.language.iso eng
dc.relation.ispartof en
dc.title On the reduction of raw image data in the context of the appearance-based approach to robot navigation en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Intelligent Robotics Group en
dc.description.status Non peer reviewed en


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