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Interpretation and Alignment of 2D Indoor Maps: Towards a Heterogeneous Map Representation
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
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
Keyword [en]
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: urn:nbn:se:hh:diva-36699ISBN: 978-91-87045-96-7 (print)ISBN: 978-91-87045-97-4 (electronic)OAI: oai:DiVA.org:hh-36699DiVA, id: diva2:1202090
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
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, p. 1-12Conference paper, Published 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: 2018-05-02Bibliographically 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, p. 1771-1777, article id 7064584Conference paper, Published 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: 2018-05-02Bibliographically 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, article id 7324207Conference paper, Published 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: 2018-05-02Bibliographically approved
4. 2D Map Alignment With Region Decomposition
Open this publication in new window or tab >>2D Map Alignment With Region Decomposition
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
Keyword
robotics, robotic mapping, map alignment, region decomposition
National Category
Robotics
Identifiers
urn:nbn:se:hh:diva-36719 (URN)
Funder
Knowledge Foundation
Available from: 2018-05-03 Created: 2018-05-03 Last updated: 2018-05-04
5. Nonlinear Optimization of Multimodal Two-Dimensional Map Alignment With Application to Prior Knowledge Transfer
Open this publication in new window or tab >>Nonlinear Optimization of Multimodal Two-Dimensional Map Alignment With Application to Prior Knowledge Transfer
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
Keyword
mapping
National Category
Robotics
Identifiers
urn:nbn:se:hh:diva-36604 (URN)10.1109/LRA.2018.2806439 (DOI)
Conference
2018 IEEE International Conference on Robotics and Automation, Brisbane, Australia, May 21-25, 2018
Funder
Knowledge Foundation
Available from: 2018-04-12 Created: 2018-04-12 Last updated: 2018-05-02Bibliographically approved

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Gholami Shahbandi, Saeed

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