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3D Object Shape Categorization in Domestic Environments

  • In service robotics, tasks without the involvement of objects are barely applicable, like in searching, fetching or delivering tasks. Service robots are supposed to capture efficiently object related information in real world scenes while for instance considering clutter and noise, and also being flexible and scalable to memorize a large set of objects. Besides object perception tasks like object recognition where the object’s identity is analyzed, object categorization is an important visual object perception cue that associates unknown object instances based on their e.g. appearance or shape to a corresponding category. We present a pipeline from the detection of object candidates in a domestic scene over the description to the final shape categorization of detected candidates. In order to detect object related information in cluttered domestic environments an object detection method is proposed that copes with multiple plane and object occurrences like in cluttered scenes with shelves. Further a surface reconstruction method based on Growing Neural Gas (GNG) in combination with a shape distribution-based descriptor is proposed to reflect shape characteristics of object candidates. Beneficial properties provided by the GNG such as smoothing and denoising effects support a stable description of the object candidates which also leads towards a more stable learning of categories. Based on the presented descriptor a dictionary approach combined with a supervised shape learner is presented to learn prediction models of shape categories. Experimental results, of different shapes related to domestically appearing object shape categories such as cup, can, box, bottle, bowl, plate and ball, are shown. A classification accuracy of about 90% and a sequential execution time of lesser than two seconds for the categorization of an unknown object is achieved which proves the reasonableness of the proposed system design. Additional results are shown towards object tracking and false positive handling to enhance the robustness of the categorization. Also an initial approach towards incremental shape category learning is proposed that learns a new category based on the set of previously learned shape categories.

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Metadaten
Document Type:Master's Thesis
Language:English
Author:Christian Atanas Müller
Pagenumber:110
ISBN:978-3-96043-009-4
ISSN:1869-5272
URN:urn:nbn:de:hbz:1044-opus-112
DOI:https://doi.org/10.18418/978-3-96043-009-4
Referee:Paul G. Ploeger, Gerhard K. Kraetzschmar, Nico Hochgeschwender
Advisor:Paul G. Ploeger
Publishing Institution:Hochschule Bonn-Rhein-Sieg
Granting Institution:Hochschule Bonn-Rhein-Sieg, Fachbereich Informatik
Date of first publication:2012/03/15
Note:
A thesis submitted to the University of Applied Sciences Bonn-Rhein-Sieg for the degree of Master of Science in Autonomous Systems.
Series (Volume):Technical Report / Hochschule Bonn-Rhein-Sieg University of Applied Sciences, Department of Computer Science (01-2012)
GND Keyword:3D-Scanner
Departments, institutes and facilities:Fachbereich Informatik
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Theses:Fachbereich / Informatik
Entry in this database:2012/03/15
Licence (Multiple languages):License LogoIn Copyright (Urheberrechtsschutz)