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Multi-modal Semantic Place Classification
KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0002-1396-0102
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0002-1170-7162
2010 (English)In: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176, Vol. 29, no 2-3, 298-320 p.Article in journal (Refereed) Published
Abstract [en]

The ability to represent knowledge about space and its position therein is crucial for a mobile robot. To this end, topological and semantic descriptions are gaining popularity for augmenting purely metric space representations. In this paper we present a multi-modal place classification system that allows a mobile robot to identify places and recognize semantic categories in an indoor environment. The system effectively utilizes information from different robotic sensors by fusing multiple visual cues and laser range data. This is achieved using a high-level cue integration scheme based on a Support Vector Machine (SVM) that learns how to optimally combine and weight each cue. Our multi-modal place classification approach can be used to obtain a real-time semantic space labeling system which integrates information over time and space. We perform an extensive experimental evaluation of the method for two different platforms and environments, on a realistic off-line database and in a live experiment on an autonomous robot. The results clearly demonstrate the effectiveness of our cue integration scheme and its value for robust place classification under varying conditions.

Place, publisher, year, edition, pages
2010. Vol. 29, no 2-3, 298-320 p.
Keyword [en]
recognition, sensor fusion, localization, multi-modal place, classification, sensor and cue integration, semantic annotation of space, image, representations, vision
National Category
Computer and Information Science
URN: urn:nbn:se:kth:diva-19266DOI: 10.1177/0278364909356483ISI: 000275038200010ScopusID: 2-s2.0-77949376736OAI: diva2:337313
Swedish Research Council, 2005-3600-Complex
QC 20100525Available from: 2010-08-05 Created: 2010-08-05 Last updated: 2011-12-09Bibliographically approved
In thesis
1. Semantic Mapping with Mobile Robots
Open this publication in new window or tab >>Semantic Mapping with Mobile Robots
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

After decades of unrealistic predictions and expectations, robots have finally escaped from industrial workplaces and made their way into our homes,offices, museums and other public spaces. These service robots are increasingly present in our environments and many believe that it is in the area ofservice and domestic robotics that we will see the largest growth within thenext few years. In order to realize the dream of robot assistants performing human-like tasks together with humans in a seamless fashion, we need toprovide them with the fundamental capability of understanding complex, dynamic and unstructured environments. More importantly, we need to enablethem the sharing of our understanding of space to permit natural cooper-ation. To this end, this thesis addresses the problem of building internalrepresentations of space for artificial mobile agents populated with humanspatial semantics as well as means for inferring that semantics from sensoryinformation. More specifically, an extensible approach to place classificationis introduced and used for mobile robot localization as well as categorizationand extraction of spatial semantic concepts from general place appearance andgeometry. The models can be incrementally adapted to the dynamic changesin the environment and employ efficient ways for cue integration, sensor fu-sion and confidence estimation. In addition, a system and representationalapproach to semantic mapping is presented. The system incorporates and in-tegrates semantic knowledge from multiple sources such as the geometry andgeneral appearance of places, presence of objects, topology of the environmentas well as human input. A conceptual map is designed and used for modelingand reasoning about spatial concepts and their relations to spatial entitiesand their semantic properties. Finally, the semantic mapping algorithm isbuilt into an integrated robotic system and shown to substantially enhancethe performance of the robot on the complex task of active object search. Thepresented evaluations show the effectiveness of the system and its underlyingcomponents and demonstrate applicability to real-world problems in realistichuman settings.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2011. xiii, 52 p.
Trita-CSC-A, ISSN 1653-5723 ; 2011:10
National Category
Computer Science
urn:nbn:se:kth:diva-34171 (URN)978-91-7501-039-7 (ISBN)
Public defence
2011-06-10, Sal F3, Lindstedtsvägen 26, KTH, Stockholm, 13:00 (English)
QC 20110527Available from: 2011-05-27 Created: 2011-05-27 Last updated: 2011-05-27Bibliographically approved

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