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Assessing Map Quality using Conditional Random Fields

Abstract : This paper is concerned with assessing the quality of work-space maps. While there has been much work in recent years on building maps of ¯eld settings, little attention has been given to endowing a machine with introspective competencies which would allow assessing the reliability/plausibility of the representation. We classify regions in 3D point-cloud maps into two binary classes|\plausible" or \suspicious". In this paper we concentrate on the classi¯cation of urban maps and use a Conditional Random Fields to model the intrinsic qualities of planar patches and crucially, their relationship to each other. A bipartite labelling of the map is acquired via application of the Graph Cut algorithm. We present results using data gathered by a mobile robot equipped with a 3D laser range sensor while operating in a typical urban setting.
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Submitted on : Thursday, December 27, 2007 - 10:49:45 AM
Last modification on : Monday, January 14, 2008 - 3:12:01 PM
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  • HAL Id : inria-00201216, version 1



Manjari Chandran-Ramesh, Paul Newman. Assessing Map Quality using Conditional Random Fields. 6th International Conference on Field and Service Robotics - FSR 2007, Jul 2007, Chamonix, France. ⟨inria-00201216⟩



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