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.
Type de document :
Communication dans un congrès
6th International Conference on Field and Service Robotics - FSR 2007, Jul 2007, Chamonix, France. Springer, 42, 2007, Springer Tracts in Advanced Robotics
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. Springer, 42, 2007, Springer Tracts in Advanced Robotics. 〈inria-00201216〉
