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Semantic Mapping in Warehouses
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
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. , 88 p.
Series
Halmstad University Dissertations, 23
Keyword [en]
Automation, Robotics, Mapping, Semantic Maps, Warehouse Automation
National Category
Robotics Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-32170Libris ID: 20019143ISBN: 978-91-87045-48-6 (print)ISBN: 978-91-87045-49-3 (print)OAI: oai:DiVA.org:hh-32170DiVA: diva2:1033695
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
List of papers
1. Modeling of a Large Structured Environment: With a Repetitive Canonical Geometric-Semantic Model
Open this publication in new window or tab >>Modeling of a Large Structured Environment: With a Repetitive Canonical Geometric-Semantic Model
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, 1-12 p.Conference 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
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 8717
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-26316 (URN)10.1007/978-3-319-10401-0_1 (DOI)2-s2.0-84906729072 (Scopus ID)978-3-319-10400-3 (ISBN)978-3-319-10401-0 (ISBN)
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: 2017-03-22Bibliographically approved
2. Sensor Based Adaptive Metric-Topological Cell Decomposition Method for Semantic Annotation of Structured Environments
Open this publication in new window or tab >>Sensor Based Adaptive Metric-Topological Cell Decomposition Method for Semantic Annotation of Structured Environments
2014 (English)In: 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), Piscataway, NJ: IEEE Press, 2014, 1771-1777 p., 7064584Conference paper, (Refereed)
Abstract [en]

A fundamental ingredient for semantic labeling is a reliable method for determining and representing the relevant spatial features of an environment. We address this challenge for planar metric-topological maps based on occupancy grids. Our method detects arbitrary dominant orientations in the presence of significant clutter, fits corresponding line features with tunable resolution, and extracts topological information by polygonal cell decomposition. Real-world case studies taken from the target application domain (autonomous forklift trucks in warehouses) demonstrate the performance and robustness of our method, while results from a preliminary algorithm to extract corridors, and junctions, demonstrate its expressiveness. Contribution of this work starts with the formulation of metric-topological surveying of environment, and a generic n-direction planar representation accompanied with a general method for extracting it from occupancy map. The implementation also includes some semantic labels specific to warehouse like environments. © 2014 IEEE.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Press, 2014
National Category
Signal Processing Robotics
Identifiers
urn:nbn:se:hh:diva-26597 (URN)10.1109/ICARCV.2014.7064584 (DOI)000393395800306 ()2-s2.0-84949925965 (Scopus ID)978-1-4799-5199-4 (ISBN)
Conference
13th International Conference on Control, Automation, Robotics and Vision, ICARCV 2014, Marina Bay Sands, Singapore, December 10-12, 2014
Funder
Knowledge Foundation
Note

This work was supported by the Swedish Knowledge Foundation and industry partners Kollmorgen, Optronic, and Toyota Material Handling Europe.

Available from: 2014-09-26 Created: 2014-09-26 Last updated: 2017-03-21Bibliographically approved
3. Semi-Supervised Semantic Labeling of Adaptive Cell Decomposition Maps in Well-Structured Environments
Open this publication in new window or tab >>Semi-Supervised Semantic Labeling of Adaptive Cell Decomposition Maps in Well-Structured Environments
2015 (English)In: 2015 European Conference on Mobile Robots (ECMR), Piscataway, NJ: IEEE Press, 2015, 7324207Conference paper, (Refereed)
Abstract [en]

We present a semi-supervised approach for semantic mapping, by introducing human knowledge after unsupervised place categorization has been combined with an adaptive cell decomposition of an occupancy map. Place categorization is based on clustering features extracted from raycasting in the occupancy map. The cell decomposition is provided by work we published previously, which is effective for the maps that could be abstracted by straight lines. Compared to related methods, our approach obviates the need for a low-level link between human knowledge and the perception and mapping sub-system, or the onerous preparation of training data for supervised learning. Application scenarios include intelligent warehouse robots which need a heightened awareness in order to operate with a higher degree of autonomy and flexibility, and integrate more fully with inventory management systems. The approach is shown to be robust and flexible with respect to different types of environments and sensor setups. © 2015 IEEE

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Press, 2015
Keyword
Continuous wavelet transforms, Feature extraction, Labeling, Robot sensing systems, Robustness, Semantics
National Category
Robotics
Identifiers
urn:nbn:se:hh:diva-29343 (URN)10.1109/ECMR.2015.7324207 (DOI)000380213600041 ()2-s2.0-84962293280 (Scopus ID)978-1-4673-9163-4 (ISBN)978-1-4673-9163-15 (ISBN)
Conference
7th European Conference on Mobile Robots 2015, Lincoln, United Kingdom, 2-4 September, 2015
Projects
AIMS
Funder
Knowledge Foundation
Note

This work was supported by the Swedish Knowledge Foundation and industry partners Kollmorgen, Optronic, and Toyota Material Handling Europe.

Available from: 2015-09-01 Created: 2015-09-01 Last updated: 2016-11-30Bibliographically approved

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