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Autonomous Learning of Object Models on a Mobile Robot
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.
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2017 (English)In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 2, no 1, 26-33 p., 7393491Article in journal (Refereed) Published
Abstract [en]

In this article we present and evaluate a system which allows a mobile robot to autonomously detect, model and re-recognize objects in everyday environments. Whilst other systems have demonstrated one of these elements, to our knowledge we present the first system which is capable of doing all of these things, all without human interaction, in normal indoor scenes. Our system detects objects to learn by modelling the static part of the environment and extracting dynamic elements. It then creates and executes a view plan around a dynamic element to gather additional views for learning. Finally these views are fused to create an object model. The performance of the system is evaluated on publicly available datasets as well as on data collected by the robot in both controlled and uncontrolled scenarios.

Place, publisher, year, edition, pages
IEEE Press, 2017. Vol. 2, no 1, 26-33 p., 7393491
Keyword [en]
Object Model, Robot
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-183494DOI: 10.1109/LRA.2016.2522086ISI: 000413732300005Scopus ID: 2-s2.0-85020019001OAI: oai:DiVA.org:kth-183494DiVA: diva2:911777
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Funder
EU, FP7, Seventh Framework Programme, 600623
Note

QC 20160411

Available from: 2016-03-14 Created: 2016-03-14 Last updated: 2017-11-20Bibliographically approved

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Folkesson, JohnJensfelt, Patric

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