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Building an Enhanced Vocabulary of the Robot Environment with a Ceiling Pointing Camera
Instituto de Investigación en Ingeniería de Aragón, Deptartmento de Informática e Ingeniería de Sistemas, University of Zaragoza, Zaragoza, Spain.
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems)ORCID iD: 0000-0002-2953-1564
Instituto de Investigación en Ingeniería de Aragón, Deptartmento de Informática e Ingeniería de Sistemas, University of Zaragoza, Zaragoza, Spain.
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems)ORCID iD: 0000-0003-0217-9326
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2016 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 16, no 4, 493Article in journal (Refereed) Published
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Text
Abstract [en]

Mobile robots are of great help for automatic monitoring tasks in different environments. One of the first tasks that needs to be addressed when creating these kinds of robotic systems is modeling the robot environment. This work proposes a pipeline to build an enhanced visual model of a robot environment indoors. Vision based recognition approaches frequently use quantized feature spaces, commonly known as Bag of Words (BoW) or vocabulary representations. A drawback using standard BoW approaches is that semantic information is not considered as a criteria to create the visual words. To solve this challenging task, this paper studies how to leverage the standard vocabulary construction process to obtain a more meaningful visual vocabulary of the robot work environment using image sequences. We take advantage of spatio-temporal constraints and prior knowledge about the position of the camera. The key contribution of our work is the definition of a new pipeline to create a model of the environment. This pipeline incorporates (1) tracking information to the process of vocabulary construction and (2) geometric cues to the appearance descriptors. Motivated by long term robotic applications, such as the aforementioned monitoring tasks, we focus on a configuration where the robot camera points to the ceiling, which captures more stable regions of the environment. The experimental validation shows how our vocabulary models the environment in more detail than standard vocabulary approaches, without loss of recognition performance. We show different robotic tasks that could benefit of the use of our visual vocabulary approach, such as place recognition or object discovery. For this validation, we use our publicly available data-set.

Place, publisher, year, edition, pages
Basel: MDPI AG , 2016. Vol. 16, no 4, 493
Keyword [en]
visual vocabulary, computer vision, bag of words, robotics, place recognition, environment description
National Category
Chemical Sciences Computer Science
Research subject
Chemistry; Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-50502DOI: 10.3390/s16040493ISI: 000375153700073Scopus ID: 2-s2.0-84962921139OAI: oai:DiVA.org:oru-50502DiVA: diva2:931985
Note

Funding Agencies:

Spanish Government 

European Union DPI2015-65962-R

Available from: 2016-05-31 Created: 2016-05-31 Last updated: 2017-10-17Bibliographically approved

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