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Sensor Based Adaptive Metric-Topological Cell Decomposition Method for Semantic Annotation of Structured Environments
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.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0003-3513-8854
2014 (English)In: 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), Piscataway, NJ: IEEE Press, 2014, 1771-1777 p.Conference 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. 1771-1777 p.
National Category
Signal Processing Robotics
URN: urn:nbn:se:hh:diva-26597DOI: 10.1109/ICARCV.2014.7064584ScopusID: 2-s2.0-84946687398ISBN: 978-147995199-4OAI: diva2:750189
13th International Conference on Control, Automation, Robotics and Vision, ICARCV 2014, Marina Bay Sands, Singapore, December 10-12, 2014
Knowledge Foundation

This work was supported by the Swedish Knowledge Foundation and industry partners Kollmorgen, Optronic, and Toyota Material Handling Europe. Article number: 7064584

Available from: 2014-09-26 Created: 2014-09-26 Last updated: 2016-10-08Bibliographically 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. 88 p.
Halmstad University Dissertations, 23
Automation, Robotics, Mapping, Semantic Maps, Warehouse Automation
National Category
Robotics Signal Processing
urn:nbn:se:hh:diva-32170 (URN)978-91-87045-48-6 (ISBN)978-91-87045-49-3 (ISBN)
2016-09-23, Wigforssalen, Kristian IV:s väg 3, Halmstad, Sweden, 10:15 (English)
Automatic Inventory and Mapping of Stock (AIMS)
Knowledge Foundation
Available from: 2016-10-13 Created: 2016-10-08 Last updated: 2016-10-13Bibliographically approved

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