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.
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
Liste complète des métadonnées

Littérature citée [20 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/inria-00201216
Contributeur : Inria Rhône-Alpes Documentation <>
Soumis le : jeudi 27 décembre 2007 - 10:49:45
Dernière modification le : lundi 14 janvier 2008 - 15:12:01
Document(s) archivé(s) le : jeudi 27 septembre 2012 - 13:25:37

Fichier

fsr_75.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : inria-00201216, version 1

Collections

Citation

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〉

Partager

Métriques

Consultations de la notice

106

Téléchargements de fichiers

118