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Gholami Shahbandi, SaeedORCID iD iconorcid.org/0000-0003-3498-0783
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Publications (10 of 10) Show all publications
Gholami Shahbandi, S. & Magnusson, M. (2019). 2D Map Alignment With Region Decomposition. Autonomous Robots, 43(5), 1117-1136
Open this publication in new window or tab >>2D Map Alignment With Region Decomposition
2019 (English)In: Autonomous Robots, ISSN 0929-5593, E-ISSN 1573-7527, Vol. 43, no 5, p. 1117-1136Article in journal (Refereed) Published
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. © 2018, The Author(s).

Place, publisher, year, edition, pages
New York, NY: Springer-Verlag New York, 2019
Keywords
robotics, robotic mapping, map alignment, region decomposition
National Category
Robotics
Identifiers
urn:nbn:se:hh:diva-36719 (URN)10.1007/s10514-018-9785-7 (DOI)2-s2.0-85050797708 (Scopus ID)
Funder
Knowledge Foundation
Available from: 2018-05-03 Created: 2018-05-03 Last updated: 2019-05-17Bibliographically approved
Gholami Shahbandi, S. (2018). Interpretation and Alignment of 2D Indoor Maps: Towards a Heterogeneous Map Representation. (Doctoral dissertation). Halmstad: Halmstad University Press
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: 2019-04-25Bibliographically approved
Gholami Shahbandi, S., Magnusson, M. & Iagnemma, K. (2018). Nonlinear Optimization of Multimodal Two-Dimensional Map Alignment With Application to Prior Knowledge Transfer. Paper presented at 2018 IEEE International Conference on Robotics and Automation, Brisbane, Australia, May 21-25, 2018. IEEE Robotics and Automation Letters, 3(3), 2040-2047
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
Keywords
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
Mashad Nemati, H., Gholami Shahbandi, S. & Åstrand, B. (2016). Human Tracking in Occlusion based on Reappearance Event Estimation. In: Oleg Gusikhin, Dimitri Peaucelle & Kurosh Madani (Ed.), ICINCO 2016: 13th International Conference on Informatics in Control, Automation and Robotics: Proceedings, Volume 2. Paper presented at 13th International Conference on Informatics in Control, Automation and Robotics, Lisbon, Portugal, 29-31 July, 2016 (pp. 505-512). SciTePress, 2
Open this publication in new window or tab >>Human Tracking in Occlusion based on Reappearance Event Estimation
2016 (English)In: ICINCO 2016: 13th International Conference on Informatics in Control, Automation and Robotics: Proceedings, Volume 2 / [ed] Oleg Gusikhin, Dimitri Peaucelle & Kurosh Madani, SciTePress, 2016, Vol. 2, p. 505-512Conference paper, Published paper (Refereed)
Abstract [en]

Relying on the commonsense knowledge that the trajectory of any physical entity in the spatio-temporal domain is continuous, we propose a heuristic data association technique. The technique is used in conjunction with an Extended Kalman Filter (EKF) for human tracking under occlusion. Our method is capable of tracking moving objects, maintain their state hypothesis even in the period of occlusion, and associate the target reappeared from occlusion with the existing hypothesis. The technique relies on the estimation of the reappearance event both in time and location, accompanied with an alert signal that would enable more intelligent behavior (e.g. in path planning). We implemented the proposed method, and evaluated its performance with real-world data. The result validates the expected capabilities, even in case of tracking multiple humans simultaneously.

Place, publisher, year, edition, pages
SciTePress, 2016
Keywords
Detection and Tracking Moving Objects, Extended Kalman Filter, Human Tracking, Occlusion, Intelligent Vehicles, Mobile Robots
National Category
Robotics Signal Processing Computer Vision and Robotics (Autonomous Systems) Medical Image Processing
Identifiers
urn:nbn:se:hh:diva-31709 (URN)10.5220/0006006805050512 (DOI)000392601900061 ()2-s2.0-85013059501 (Scopus ID)978-989-758-198-4 (ISBN)
Conference
13th International Conference on Informatics in Control, Automation and Robotics, Lisbon, Portugal, 29-31 July, 2016
Available from: 2016-08-04 Created: 2016-08-04 Last updated: 2018-01-10Bibliographically approved
Fan, Y., Aramrattana, M., Shahbandi, S. G., Nemati, H. M. & Åstrand, B. (2016). Infrastructure Mapping in Well-Structured Environments Using MAV. Paper presented at 17th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2016, Sheffield, United Kingdom, 26 June-1 July, 2016. Lecture Notes in Computer Science, 9716, 116-126
Open this publication in new window or tab >>Infrastructure Mapping in Well-Structured Environments Using MAV
Show others...
2016 (English)In: Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349, Vol. 9716, p. 116-126Article in journal (Refereed) Published
Abstract [en]

In this paper, we present a design of a surveying system for warehouse environment using low cost quadcopter. The system focus on mapping the infrastructure of surveyed environment. As a unique and essential parts of the warehouse, pillars from storing shelves are chosen as landmark objects for representing the environment. The map are generated based on fusing the outputs of two different methods, point cloud of corner features from Parallel Tracking and Mapping (PTAM) algorithm with estimated pillar position from a multi-stage image analysis method. Localization of the drone relies on PTAM algorithm. The system is implemented in Robot Operating System(ROS) and MATLAB, and has been successfully tested in real-world experiments. The result map after scaling has a metric error less than 20 cm. © Springer International Publishing Switzerland 2016.

Place, publisher, year, edition, pages
Cham, Switzerland: Springer, 2016
Keywords
Robotic mapping, parallel tracking and mapping, MAV
National Category
Robotics
Identifiers
urn:nbn:se:hh:diva-31645 (URN)10.1007/978-3-319-40379-3_12 (DOI)000386324700012 ()2-s2.0-84977496781 (Scopus ID)978-3-319-40378-6 (ISBN)978-3-319-40379-3 (ISBN)
Conference
17th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2016, Sheffield, United Kingdom, 26 June-1 July, 2016
Available from: 2016-07-14 Created: 2016-07-14 Last updated: 2018-03-22Bibliographically approved
Gholami Shahbandi, S. (2016). Semantic Mapping in Warehouses. (Licentiate dissertation). Halmstad University: Halmstad University Press
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. p. 88
Series
Halmstad University Dissertations ; 23
Keywords
Automation, Robotics, Mapping, Semantic Maps, Warehouse Automation
National Category
Robotics Signal Processing
Identifiers
urn:nbn:se:hh:diva-32170 (URN)978-91-87045-48-6 (ISBN)978-91-87045-49-3 (ISBN)
Presentation
2016-09-23, Wigforssalen, Kristian IV:s väg 3, Halmstad, Sweden, 10:15 (English)
Opponent
Supervisors
Projects
Automatic Inventory and Mapping of Stock (AIMS)
Funder
Knowledge Foundation
Available from: 2016-10-13 Created: 2016-10-08 Last updated: 2017-05-16Bibliographically approved
Gholami Shahbandi, S., Åstrand, B. & Philippsen, R. (2015). Semi-Supervised Semantic Labeling of Adaptive Cell Decomposition Maps in Well-Structured Environments. In: 2015 European Conference on Mobile Robots (ECMR): . Paper presented at 7th European Conference on Mobile Robots 2015, Lincoln, United Kingdom, 2-4 September, 2015. Piscataway, NJ: IEEE Press, Article ID 7324207.
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
Keywords
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
Gholami Shahbandi, S. & Åstrand, B. (2014). Modeling of a Large Structured Environment: With a Repetitive Canonical Geometric-Semantic Model. In: Michael Mistry, Aleš Leonardis, Mark Witkowski & Chris Melhuish (Ed.), Advances in Autonomous Robotics Systems: 15th Annual Conference, TAROS 2014, Birmingham, UK, September 1-3, 2014. Proceedings. Paper presented at 15th Annual Conference, TAROS (Towards Autonomous Robotic Systems) 2014, Birmingham, United Kingdom, September 1-3, 2014 (pp. 1-12). Heidelberg: Springer, 8717
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
Gholami Shahbandi, S., Åstrand, B. & Philippsen, R. (2014). Sensor Based Adaptive Metric-Topological Cell Decomposition Method for Semantic Annotation of Structured Environments. In: 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV): . Paper presented at 13th International Conference on Control, Automation, Robotics and Vision, ICARCV 2014, Marina Bay Sands, Singapore, December 10-12, 2014 (pp. 1771-1777). Piscataway, NJ: IEEE Press, Article ID 7064584.
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
Gholami Shahbandi, S. & Lucidarme, P. (2012). Object Recognition Based on Radial Basis Function Neural Networks: experiments with RGB-D camera embedded on mobile robots. Paper presented at 1st International Conference on Systems and Computer Science (ICSCS 2012), Lille, France, August 29-31.
Open this publication in new window or tab >>Object Recognition Based on Radial Basis Function Neural Networks: experiments with RGB-D camera embedded on mobile robots
2012 (English)Conference paper, Oral presentation only (Refereed)
Abstract [en]

An object recognition strategy based on artificial radial basis functions neural networks is presented in this paper. The general context of this work is to recognize object from captures made by a mobile robot. Unlike classical approaches which always select the closest object, our method outputs a set of potential candidates if the input information is not enough discriminant. There are three main steps in our approach: objects segmentation, signature extraction and classification. Segmentation is inspired from previous works and is shortly described. Signature extraction based on global geometric and color features is detailed. Classification based on artificial neural networks is also explained and architecture of the network is justified. Finally a real experiment made with a RGB-D camera mounted on a mobile robot is presented and classification results is criticized.

National Category
Robotics
Identifiers
urn:nbn:se:hh:diva-20920 (URN)
Conference
1st International Conference on Systems and Computer Science (ICSCS 2012), Lille, France, August 29-31
Projects
Cart-O-matic Project
Available from: 2013-01-14 Created: 2013-01-14 Last updated: 2015-08-25Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-3498-0783

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