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2D Map Alignment With Region Decomposition
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
Örebro University, Örebro, Sweden.ORCID iD: 0000-0001-8658-2985
2018 (English)In: Autonomous Robots, ISSN 0929-5593, E-ISSN 1573-7527Article in journal (Refereed) Submitted
Abstract [en]

In many applications of autonomous mobile robots the following problem is encountered. Two maps of the same environment are available, one a prior map and the other a sensor map built by the robot. To benefit from all available information in both maps, the robot must find the correct alignment between the two maps. There exist many approaches to address this challenge, however, most of the previous methods rely on assumptions such as similar modalities of the maps, same scale, or existence of an initial guess for the alignment. In this work we propose a decomposition-based method for 2D spatial map alignment which does not rely on those assumptions. Our proposed method is validated and compared with other approaches, including generic data association approaches and map alignment algorithms. Real world examples of four different environments with thirty six sensor maps and four layout maps are used for this analysis. The maps, along with an implementation of the method, are made publicly available online.

Place, publisher, year, edition, pages
New York, NY: Springer-Verlag New York, 2018.
Keywords [en]
robotics, robotic mapping, map alignment, region decomposition
National Category
Robotics
Identifiers
URN: urn:nbn:se:hh:diva-36719OAI: oai:DiVA.org:hh-36719DiVA, id: diva2:1203632
Funder
Knowledge FoundationAvailable from: 2018-05-03 Created: 2018-05-03 Last updated: 2018-05-04
In thesis
1. 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, Saeed

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