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Nonlinear Optimization of Multimodal Two-Dimensional Map Alignment With Application to Prior Knowledge Transfer
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 . (Mobile Robotics & Olfaction lab of AASS)ORCID iD: 0000-0001-8658-2985
Massachusetts Institute of Technology, Cambridge, MA, USA. (Robotic Mobility Group)
2018 (English)In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 3, no 3, p. 2040-2047Article in journal (Refereed) Published
Abstract [en]

We propose a method based on a nonlinear transformation for nonrigid alignment of maps of different modalities, exemplified with matching partial and deformed two-dimensional maps to layout maps. For two types of indoor environments, over a dataset of 40 maps, we have compared the method to state-of-the-art map matching and nonrigid image registration methods and demonstrate a success rate of 80.41% and a mean point-to-point alignment error of 1.78 m, compared to 31.9% and 10.7 m for the best alternative method. We also propose a fitness measure that can quite reliably detect bad alignments. Finally, we show a use case of transferring prior knowledge (labels/segmentation), demonstrating that map segmentation is more consistent when transferred from an aligned layout map than when operating directly on partial maps (95.97% vs. 81.56%). © 2018 IEEE.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2018. Vol. 3, no 3, p. 2040-2047
Keywords [en]
mapping
National Category
Robotics
Identifiers
URN: urn:nbn:se:hh:diva-36604DOI: 10.1109/LRA.2018.2806439OAI: oai:DiVA.org:hh-36604DiVA, id: diva2:1197163
Conference
2018 IEEE International Conference on Robotics and Automation, Brisbane, Australia, May 21-25, 2018
Funder
Knowledge FoundationAvailable from: 2018-04-12 Created: 2018-04-12 Last updated: 2018-05-02Bibliographically approved
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, SaeedIagnemma, Karl

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