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Modeling of a Large Structured Environment: With a Repetitive Canonical Geometric-Semantic Model
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0003-3498-0783
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
2014 (English)In: Advances in Autonomous Robotics Systems: 15th Annual Conference, TAROS 2014, Birmingham, UK, September 1-3, 2014. Proceedings / [ed] Michael Mistry, Aleš Leonardis, Mark Witkowski & Chris Melhuish, Heidelberg: Springer, 2014, Vol. 8717, p. 1-12Conference paper, Published paper (Refereed)
Abstract [en]

AIMS project attempts to link the logistic requirements of an intelligent warehouse and state of the art core technologies of automation, by providing an awareness of the environment to the autonomous systems and vice versa. In this work we investigate a solution for modeling the infrastructure of a structured environment such as warehouses, by the means of a vision sensor. The model is based on the expected pattern of the infrastructure, generated from and matched to the map. Generation of the model is based on a set of tools such as closed-form Hough transform, DBSCAN clustering algorithm, Fourier transform and optimization techniques. The performance evaluation of the proposed method is accompanied with a real world experiment. © 2014 Springer International Publishing.

Place, publisher, year, edition, pages
Heidelberg: Springer, 2014. Vol. 8717, p. 1-12
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 8717
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-26316DOI: 10.1007/978-3-319-10401-0_1Scopus ID: 2-s2.0-84906729072ISBN: 978-3-319-10400-3 (print)ISBN: 978-3-319-10401-0 (electronic)OAI: oai:DiVA.org:hh-26316DiVA, id: diva2:741585
Conference
15th Annual Conference, TAROS (Towards Autonomous Robotic Systems) 2014, Birmingham, United Kingdom, September 1-3, 2014
Projects
AIMS
Funder
Knowledge Foundation
Note

This work as a part of AIMS project, is supported by the Swedish Knowledge Foundation and industry partners Kollmorgen, Optronic, and Toyota Material Handling Europe.

Available from: 2014-08-28 Created: 2014-08-28 Last updated: 2018-05-02Bibliographically approved
In thesis
1. Semantic Mapping in Warehouses
Open this publication in new window or tab >>Semantic Mapping in Warehouses
2016 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis and appended papers present the process of tacking the problem of environment modeling for autonomous agent. More specifically, the focus of the work has been semantic mapping of warehouses. A semantic map for such purpose is expected to be layout-like and support semantics of both open spaces and infrastructure of the environment. The representation of the semantic map is required to be understandable by all involved agents (humans, AGVs and WMS.) And the process of semantic mapping is desired to lean toward full-autonomy, with minimum input requirement from human user. To that end, we studied the problem of semantic annotation over two kinds of spatial map from different modalities. We identified properties, structure, and challenges of the problem. And we have developed representations and accompanied methods, while meeting the set criteria. The overall objective of the work is “to develop and construct a layer of abstraction (models and/or decomposition) for structuring and facilitate access to salient information in the sensory data. This layer of abstraction connects high level concepts to low-level sensory pattern.” Relying on modeling and decomposition of sensory data, we present our work on abstract representation for two modalities (laser scanner and camera) in three appended papers. Feasibility and the performance of the proposed methods are evaluated over data from real warehouse. The thesis conclude with summarizing the presented technical details, and drawing the outline for future work.

Place, publisher, year, edition, pages
Halmstad University: Halmstad University Press, 2016. p. 88
Series
Halmstad University Dissertations ; 23
Keywords
Automation, Robotics, Mapping, Semantic Maps, Warehouse Automation
National Category
Robotics Signal Processing
Identifiers
urn:nbn:se:hh:diva-32170 (URN)978-91-87045-48-6 (ISBN)978-91-87045-49-3 (ISBN)
Presentation
2016-09-23, Wigforssalen, Kristian IV:s väg 3, Halmstad, Sweden, 10:15 (English)
Opponent
Supervisors
Projects
Automatic Inventory and Mapping of Stock (AIMS)
Funder
Knowledge Foundation
Available from: 2016-10-13 Created: 2016-10-08 Last updated: 2017-05-16Bibliographically approved
2. Interpretation and Alignment of 2D Indoor Maps: Towards a Heterogeneous Map Representation
Open this publication in new window or tab >>Interpretation and Alignment of 2D Indoor Maps: Towards a Heterogeneous Map Representation
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Mobile robots are increasingly being used in automation solutions with notable examples in service robots, such as home-care, and warehouses. Autonomy of mobile robots is particularly challenging, since their work space is not deterministic, known a priori, or fully predictable. Accordingly, the ability to model the work space, that is robotic mapping, is among the core technologies that are the backbone of autonomous mobile robots. However, for some applications the abilities of mapping and localization do not meet all the requirements, and robots with an enhanced awareness of their surroundings are desired. For instance, a map augmented with semantic labels is instrumental to support Human-Robot Interaction and high-level task planning and reasoning.This thesis addresses this requirement through an interpretation and integration of multiple input maps into a semantically annotated heterogeneous representation. The heterogeneity of the representation should to contain different interpretations of an input map, establish and maintain associations among different input sources, and construct a hierarchy of abstraction through model-based representation. The structuring and construction of this representation are at the core of this thesis, and the main objectives are: a) modeling, interpretation, semantic annotation, and association of the different data sources into a heterogeneous representation, and b) improving the autonomy of the aforementioned processes by curtailing the dependency of the methods on human input, such as domain knowledge.This work proposes map interpretation techniques, such as abstract representation through modeling and semantic annotation, in an attempt to enrich the final representation. In order to associate multiple data sources, this work also proposes a map alignment method. The contributions and general observations that result from the studies included in this work could be summarized as: i) manner of structuring the heterogeneous representation, ii) underlining the advantages of modeling and abstract representations, iii) several approaches to semantic annotation, and iv) improved extensibility of methods by lessening their dependency on human input.The scope of the work has been focused on 2D maps of well-structured indoor environments, such as warehouses, home, and office buildings.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2018. p. 180
Series
Halmstad University Dissertations ; 46
Keywords
Robotics, Mobile Robots, Autonomous Robots, Robot Perception, Robotic Mapping, Map Interpretation, Semantic Mapping, Place Categorization, Place Labeling, Semantic Annotation, Map Alignment, Region Segmentation, Region Decomposition, Map Representation, Heterogeneous Representation
National Category
Robotics
Identifiers
urn:nbn:se:hh:diva-36699 (URN)978-91-87045-96-7 (ISBN)978-91-87045-97-4 (ISBN)
Public defence
2018-06-14, O103, Linjegatan 12, Halmstad, 10:15 (English)
Opponent
Supervisors
Available from: 2018-05-04 Created: 2018-04-27 Last updated: 2018-05-04

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Gholami Shahbandi, SaeedÅstrand, Björn

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