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Confidence-based cue integration for visual place recognition
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0002-1396-0102
KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
2007 (English)In: 2007 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, VOLS 1-9, NEW YORK: IEEE , 2007, 2400-2407 p.Conference paper (Refereed)
Abstract [en]

A distinctive feature of intelligent systems is their capability to analyze their level of expertise for a given task; in other words, they know what they know. As a way towards this ambitious goal, this paper presents a recognition algorithm able to measure its own level of confidence and, in case of uncertainty, to seek for extra information so to increase its own knowledge and ultimately achieve better performance. We focus on the visual place recognition problem for topological localization, and we take an SVM approach. We propose a new method for measuring the confidence level of the classification output, based on the distance of a test image and the average distance of training vectors. This method is combined with a discriminative accumulation scheme for cue integration. We show with extensive experiments that the resulting algorithm achieves better performances for two visual cues than the classic single cue SVM on the same task, while minimising the computational load. More important, our method provides a reliable measure of the level of confidence of the decision.

Place, publisher, year, edition, pages
NEW YORK: IEEE , 2007. 2400-2407 p.
Keyword [en]
Arsenic compounds, Evolutionary algorithms, Intelligent robots, Robotics, Support vector machines
National Category
Computer and Information Science
URN: urn:nbn:se:kth:diva-34155DOI: 10.1109/IROS.2007.4399493ISI: 000254073201157ScopusID: 2-s2.0-51349101162ISBN: 978-1-4244-0911-2OAI: diva2:419516
IEEE/RSJ International Conference on Intelligent Robots and Systems. San Diego, CA. OCT 29-NOV 02, 2007
Available from: 2011-05-27 Created: 2011-05-27 Last updated: 2011-09-14Bibliographically 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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Pronobis, AndrzejCaputo, Barbara
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