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Semantic Mapping in Warehouses
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0003-3498-0783
2016 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Halmstad University: Halmstad University Press, 2016. , s. 88
Serie
Halmstad University Dissertations ; 23
Emneord [en]
Automation, Robotics, Mapping, Semantic Maps, Warehouse Automation
HSV kategori
Identifikatorer
URN: urn:nbn:se:hh:diva-32170Libris ID: 20019143ISBN: 978-91-87045-48-6 (tryckt)ISBN: 978-91-87045-49-3 (tryckt)OAI: oai:DiVA.org:hh-32170DiVA, id: diva2:1033695
Presentation
2016-09-23, Wigforssalen, Kristian IV:s väg 3, Halmstad, Sweden, 10:15 (engelsk)
Opponent
Veileder
Prosjekter
Automatic Inventory and Mapping of Stock (AIMS)
Forskningsfinansiär
Knowledge FoundationTilgjengelig fra: 2016-10-13 Laget: 2016-10-08 Sist oppdatert: 2017-05-16bibliografisk kontrollert
Delarbeid
1. Modeling of a Large Structured Environment: With a Repetitive Canonical Geometric-Semantic Model
Åpne denne publikasjonen i ny fane eller vindu >>Modeling of a Large Structured Environment: With a Repetitive Canonical Geometric-Semantic Model
2014 (engelsk)Inngår i: 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, s. 1-12Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Heidelberg: Springer, 2014
Serie
Lecture Notes in Computer Science, ISSN 0302-9743 ; 8717
HSV kategori
Identifikatorer
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)
Konferanse
15th Annual Conference, TAROS (Towards Autonomous Robotic Systems) 2014, Birmingham, United Kingdom, September 1-3, 2014
Prosjekter
AIMS
Forskningsfinansiär
Knowledge Foundation
Merknad

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.

Tilgjengelig fra: 2014-08-28 Laget: 2014-08-28 Sist oppdatert: 2018-05-02bibliografisk kontrollert
2. Sensor Based Adaptive Metric-Topological Cell Decomposition Method for Semantic Annotation of Structured Environments
Åpne denne publikasjonen i ny fane eller vindu >>Sensor Based Adaptive Metric-Topological Cell Decomposition Method for Semantic Annotation of Structured Environments
2014 (engelsk)Inngår i: 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), Piscataway, NJ: IEEE Press, 2014, s. 1771-1777, artikkel-id 7064584Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Piscataway, NJ: IEEE Press, 2014
HSV kategori
Identifikatorer
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)
Konferanse
13th International Conference on Control, Automation, Robotics and Vision, ICARCV 2014, Marina Bay Sands, Singapore, December 10-12, 2014
Forskningsfinansiär
Knowledge Foundation
Merknad

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

Tilgjengelig fra: 2014-09-26 Laget: 2014-09-26 Sist oppdatert: 2018-05-02bibliografisk kontrollert
3. Semi-Supervised Semantic Labeling of Adaptive Cell Decomposition Maps in Well-Structured Environments
Åpne denne publikasjonen i ny fane eller vindu >>Semi-Supervised Semantic Labeling of Adaptive Cell Decomposition Maps in Well-Structured Environments
2015 (engelsk)Inngår i: 2015 European Conference on Mobile Robots (ECMR), Piscataway, NJ: IEEE Press, 2015, artikkel-id 7324207Konferansepaper, Publicerat paper (Fagfellevurdert)
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

sted, utgiver, år, opplag, sider
Piscataway, NJ: IEEE Press, 2015
Emneord
Continuous wavelet transforms, Feature extraction, Labeling, Robot sensing systems, Robustness, Semantics
HSV kategori
Identifikatorer
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)
Konferanse
7th European Conference on Mobile Robots 2015, Lincoln, United Kingdom, 2-4 September, 2015
Prosjekter
AIMS
Forskningsfinansiär
Knowledge Foundation
Merknad

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

Tilgjengelig fra: 2015-09-01 Laget: 2015-09-01 Sist oppdatert: 2018-05-02bibliografisk kontrollert

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