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Kucner, Tomasz Piotr, PhDORCID iD iconorcid.org/0000-0002-9503-0602
Alternative names
Biography [eng]

Tomasz Piotr Kucner received his B.Sc. in Computer Management Systems in Manufacturing (2011) and M.Sc. in Robotics (2012) at Wroclaw University of Technology. In 2018, he received a tekn. dr. (Ph. D.) degree from Örebro University. During his PhD studies, he was part of KKS research project ALLO and EU FP7 research project SPENCER. His work in these projects was focussed on building spatial models of dynamics. Dr Kucner currently works as Post-doctoral researcher in the Mobile Robotics \& Olfaction lab of AASS at \"{O}rebro University, Sweden. He is mainly involved in the EU H2020 research project ILIAD, where he is working with methods for automatic map quality assessment and building spatio-temporal models of dynamics.

Publications (10 of 28) Show all publications
Kucner, T. P., Luperto, M., Lowry, S., Magnusson, M. & Lilienthal, A. (2021). Robust Frequency-Based Structure Extraction. In: 2021 IEEE International Conference on Robotics and Automation (ICRA): . Paper presented at IEEE International Conference on Robotics and Automation (ICRA 2021), Xi'an, China, May 30 - June 5, 2021 (pp. 1715-1721). IEEE
Open this publication in new window or tab >>Robust Frequency-Based Structure Extraction
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2021 (English)In: 2021 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2021, p. 1715-1721Conference paper, Published paper (Refereed)
Abstract [en]

State of the art mapping algorithms can produce high-quality maps. However, they are still vulnerable to clutter and outliers which can affect map quality and in consequence hinder the performance of a robot, and further map processing for semantic understanding of the environment. This paper presents ROSE, a method for building-level structure detection in robotic maps. ROSE exploits the fact that indoor environments usually contain walls and straight-line elements along a limited set of orientations. Therefore metric maps often have a set of dominant directions. ROSE extracts these directions and uses this information to segment the map into structure and clutter through filtering the map in the frequency domain (an approach substantially underutilised in the mapping applications). Removing the clutter in this way makes wall detection (e.g. using the Hough transform) more robust. Our experiments demonstrate that (1) the application of ROSE for decluttering can substantially improve structural feature retrieval (e.g., walls) in cluttered environments, (2) ROSE can successfully distinguish between clutter and structure in the map even with substantial amount of noise and (3) ROSE can numerically assess the amount of structure in the map.

Place, publisher, year, edition, pages
IEEE, 2021
Series
IEEE International Conference on Robotics and Automation (ICRA), ISSN 1050-4729, E-ISSN 2577-087X
Keywords
Mapping, semantic understanding, indoor environments
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-97000 (URN)10.1109/ICRA48506.2021.9561381 (DOI)000765738801089 ()2-s2.0-85118997794 (Scopus ID)9781728190778 (ISBN)9781728190785 (ISBN)
Conference
IEEE International Conference on Robotics and Automation (ICRA 2021), Xi'an, China, May 30 - June 5, 2021
Projects
ILIAD
Funder
EU, Horizon 2020, 732737
Available from: 2022-01-31 Created: 2022-01-31 Last updated: 2022-04-25Bibliographically approved
Rudenko, A., Kucner, T. P., Swaminathan, C. S., Chadalavada, R. T., Arras, K. O. & Lilienthal, A. (2020). Benchmarking Human Motion Prediction Methods. In: : . Paper presented at HRI 2020, Workshop on Test Methods and Metrics for Effective HRI in Real World Human-Robot Teams, Cambridge, UK,(Conference cancelled).
Open this publication in new window or tab >>Benchmarking Human Motion Prediction Methods
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2020 (English)Conference paper, Oral presentation only (Other academic)
Abstract [en]

In this extended abstract we present a novel dataset for benchmarking motion prediction algorithms. We describe our approach to data collection which generates diverse and accurate human motion in a controlled weakly-scripted setup. We also give insights for building a universal benchmark for motion prediction.

Keywords
human motion prediction, benchmarking, datasets
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-89169 (URN)
Conference
HRI 2020, Workshop on Test Methods and Metrics for Effective HRI in Real World Human-Robot Teams, Cambridge, UK,(Conference cancelled)
Projects
ILIAD
Available from: 2021-02-01 Created: 2021-02-01 Last updated: 2021-02-02Bibliographically approved
Kucner, T. P., Lilienthal, A., Magnusson, M., Palmieri, L. & Swaminathan, C. S. (2020). Closing Remarks. In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots: (pp. 143-151). Springer
Open this publication in new window or tab >>Closing Remarks
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2020 (English)In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, p. 143-151Chapter in book (Refereed)
Abstract [en]

Dynamics is an inherent feature of reality. In spite of that, the domain of maps of dynamics has not received a lot of attention yet. In this book, we present solutions for building maps of dynamics and outline how to make use of them for motion planning. In this chapter, we present discuss related research question that as of yet remain to be answered, and derive possible future research directions. 

Place, publisher, year, edition, pages
Springer, 2020
Series
Cognitive Systems Monographs, ISSN 1867-4925 ; 40
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-81667 (URN)10.1007/978-3-030-41808-3_6 (DOI)2-s2.0-85083964746 (Scopus ID)978-3-030-41807-6 (ISBN)978-3-030-41808-3 (ISBN)
Available from: 2020-05-13 Created: 2020-05-13 Last updated: 2020-05-13Bibliographically approved
Yu, W., Amigoni, F., Kucner, T. P. & Lee, Y.-C. (2020). Exploring the Possibility of Semantic Map Data Representation as an Extension of the IEEE 2D and 3D Map Data Representation Standards. In: 2020 20th International Conference on Control, Automation and Systmes (ICCAS 2020): . Paper presented at 20th International Conference on Control, Automation and Systems (Online ICCAS 2020), Busan, Korea, October 13-16, 2020.
Open this publication in new window or tab >>Exploring the Possibility of Semantic Map Data Representation as an Extension of the IEEE 2D and 3D Map Data Representation Standards
2020 (English)In: 2020 20th International Conference on Control, Automation and Systmes (ICCAS 2020), 2020Conference paper, Published paper (Refereed)
Abstract [en]

This paper describes 2D specifications of the IEEE Map Data Representation (MDR) standard and introduces the ongoing efforts for developing 3D MDR specifications. The 2D MDR standard is well-suited for current robot applications utilizing 2D maps, which may include autonomous road navigation, robotic logistic systems, defense and rescue robots, and service robots for personal or domestic applications. In addition to their original purpose as a common format for representing the spatial environment for robot navigation, the developed MDR standards are expected to contribute to promote development of good experimental methodologies for mobile robotics as a valuable tool for facilitating comparison and evaluation of maps obtained from different systems or methodologies.

Keywords
Robot navigation, Map data representation, Metric map, Topological map, Semantic map
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-88272 (URN)
Conference
20th International Conference on Control, Automation and Systems (Online ICCAS 2020), Busan, Korea, October 13-16, 2020
Available from: 2021-01-03 Created: 2021-01-03 Last updated: 2021-01-19Bibliographically approved
Kucner, T. P., Lilienthal, A., Magnusson, M., Palmieri, L. & Swaminathan, C. S. (2020). Introduction. In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots: (pp. 1-13). Springer
Open this publication in new window or tab >>Introduction
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2020 (English)In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, p. 1-13Chapter in book (Refereed)
Abstract [en]

Change and motion are inherent features of reality. The ability to recognise patterns governing changes has allowed humans to thrive in a dynamic reality. Similarly, dynamics awareness can also improve the performance of robots. Dynamics awareness is an umbrella term covering a broad spectrum of concepts. In this chapter, we present the key aspects of dynamics awareness. We introduce two motivating examples presenting the challenges for robots operating in a dynamic environment. We discuss the benefits of using spatial models of dynamics and analyse the challenges of building such models.

Place, publisher, year, edition, pages
Springer, 2020
Series
Cognitive Systems Monographs, ISSN 1867-4925 ; 40
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-81665 (URN)10.1007/978-3-030-41808-3_1 (DOI)2-s2.0-85083992773 (Scopus ID)978-3-030-41807-6 (ISBN)978-3-030-41808-3 (ISBN)
Available from: 2020-05-13 Created: 2020-05-13 Last updated: 2020-05-13Bibliographically approved
Kucner, T. P., Lilienthal, A., Magnusson, M., Palmieri, L. & Swaminathan, C. S. (2020). Maps of Dynamics. In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots: (pp. 15-32). Springer
Open this publication in new window or tab >>Maps of Dynamics
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2020 (English)In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, p. 15-32Chapter in book (Refereed)
Abstract [en]

The task of building maps of dynamics is the key focus of this book, as well as how to use them for motion planning. In this chapter, we present a categorisation and overview of different types of maps of dynamics. Furthermore, we give an overview of approaches to motion planning in dynamic environments, with a focus on motion planning over maps of dynamics. 

Place, publisher, year, edition, pages
Springer, 2020
Series
Cognitive Systems Monographs, ISSN 1867-4925
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-81670 (URN)10.1007/978-3-030-41808-3_2 (DOI)2-s2.0-85083956964 (Scopus ID)978-3-030-41807-6 (ISBN)978-3-030-41808-3 (ISBN)
Available from: 2020-05-13 Created: 2020-05-13 Last updated: 2020-05-13Bibliographically approved
Kucner, T. P., Lilienthal, A., Magnusson, M., Palmieri, L. & Swaminathan, C. S. (2020). Modelling Motion Patterns with Circular-Linear Flow Field Maps. In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots: (pp. 65-113). Springer
Open this publication in new window or tab >>Modelling Motion Patterns with Circular-Linear Flow Field Maps
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2020 (English)In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, p. 65-113Chapter in book (Refereed)
Abstract [en]

The shared feature of the flow of discrete objects and continuous media is that they both can be represented as velocity vectors encapsulating direction and speed of motion. In this chapter, we present a method for modelling the flow of discrete objects and continuous media as continuous Gaussian mixture fields. The proposed model associates to each part of the environment a Gaussian mixture model describing the local motion patterns. We also present a learning method, designed to build the model from a set of sparse, noisy and incomplete observations. 

Place, publisher, year, edition, pages
Springer, 2020
Series
Cognitive Systems Monographs, ISSN 1867-4925 ; 40
National Category
Fluid Mechanics and Acoustics
Identifiers
urn:nbn:se:oru:diva-81664 (URN)10.1007/978-3-030-41808-3_4 (DOI)2-s2.0-85084011370 (Scopus ID)978-3-030-41807-6 (ISBN)978-3-030-41808-3 (ISBN)
Available from: 2020-05-12 Created: 2020-05-12 Last updated: 2020-05-12Bibliographically approved
Kucner, T. P., Lilienthal, A., Magnusson, M., Palmieri, L. & Swaminathan, C. S. (2020). Modelling Motion Patterns with Conditional Transition Map. In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots: (pp. 33-64). Springer
Open this publication in new window or tab >>Modelling Motion Patterns with Conditional Transition Map
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2020 (English)In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, p. 33-64Chapter in book (Refereed)
Abstract [en]

The key idea of modelling flow of discrete objects is to capture the way they move through the environment. One method to capture the flow is to observe changes in occupancy caused by the motion of discrete objects. In this chapter, we present a method to model and learn occupancy shifts caused by an object moving through the environment. The key idea is observe temporal changes changes in the occupancy of adjacent cells, and based on the temporal offset infer the direction of the occupancy flow.

Place, publisher, year, edition, pages
Springer, 2020
Series
Cognitive Systems Monographs, ISSN 1867-4925 ; 40
National Category
Oceanography, Hydrology and Water Resources
Identifiers
urn:nbn:se:oru:diva-81669 (URN)10.1007/978-3-030-41808-3_3 (DOI)2-s2.0-85083960053 (Scopus ID)978-3-030-41807-6 (ISBN)978-3-030-41808-3 (ISBN)
Available from: 2020-05-13 Created: 2020-05-13 Last updated: 2020-05-13Bibliographically approved
Kucner, T. P., Lilienthal, A., Magnusson, M., Palmieri, L. & Swaminathan, C. S. (2020). Motion Planning Using MoDs. In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots: (pp. 115-141). Springer
Open this publication in new window or tab >>Motion Planning Using MoDs
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2020 (English)In: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, p. 115-141Chapter in book (Refereed)
Abstract [en]

Maps of dynamics can be beneficial for motion planning. Information about motion patterns in the environment can lead to finding flow-aware paths, allowing robots to align better to the expected motion: either of other agents in the environment or the flow of air or another medium. The key idea of flow-aware motion planning is to include adherence to the flow represented in the MoD into the motion planning algorithm’s sub-units (i.e. cost function, sampling mechanism), thereby biasing the motion planner into obeying local and implicit traffic rules. 

Place, publisher, year, edition, pages
Springer, 2020
Series
Cognitive Systems Monographs, ISSN 1867-4925 ; 40
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-81668 (URN)10.1007/978-3-030-41808-3_5 (DOI)2-s2.0-85083963960 (Scopus ID)978-3-030-41807-6 (ISBN)978-3-030-41808-3 (ISBN)
Available from: 2020-05-13 Created: 2020-05-13 Last updated: 2020-05-13Bibliographically approved
Vintr, T., Yan, Z., Eyisoy, K., Kubis, F., Blaha, J., Ulrich, J., . . . Krajnik, T. (2020). Natural Criteria for Comparison of Pedestrian Flow Forecasting Models. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): . Paper presented at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA (Virtual), October 25-29, 2020 (pp. 11197-11204). IEEE Press
Open this publication in new window or tab >>Natural Criteria for Comparison of Pedestrian Flow Forecasting Models
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2020 (English)In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE Press, 2020, p. 11197-11204Conference paper, Published paper (Refereed)
Abstract [en]

Models of human behaviour, such as pedestrian flows, are beneficial for safe and efficient operation of mobile robots. We present a new methodology for benchmarking of pedestrian flow models based on the afforded safety of robot navigation in human-populated environments. While previous evaluations of pedestrian flow models focused on their predictive capabilities, we assess their ability to support safe path planning and scheduling. Using real-world datasets gathered continuously over several weeks, we benchmark state-of-theart pedestrian flow models, including both time-averaged and time-sensitive models. In the evaluation, we use the learned models to plan robot trajectories and then observe the number of times when the robot gets too close to humans, using a predefined social distance threshold. The experiments show that while traditional evaluation criteria based on model fidelity differ only marginally, the introduced criteria vary significantly depending on the model used, providing a natural interpretation of the expected safety of the system. For the time-averaged flow models, the number of encounters increases linearly with the percentage operating time of the robot, as might be reasonably expected. By contrast, for the time-sensitive models, the number of encounters grows sublinearly with the percentage operating time, by planning to avoid congested areas and times.

Place, publisher, year, edition, pages
IEEE Press, 2020
Series
IEEE International Conference on Intelligent Robots and Systems. Proceedings, ISSN 2153-0858, E-ISSN 2153-0866
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-89146 (URN)10.1109/IROS45743.2020.9341672 (DOI)000724145800132 ()2-s2.0-85102405451 (Scopus ID)9781728162133 (ISBN)9781728162126 (ISBN)
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA (Virtual), October 25-29, 2020
Funder
EU, Horizon 2020, 732737
Note

Funding agencies:

OP VVV CZ.02.101/0.0/0.0/16 019/0000765

CSF projects GA18-18858S GC20-27034J SGS19/176/OHK3/3T/13 FR-8J18FR018

PHC Barrande programme 40682ZH

Toyota Partner Robot joint research project (MACPOLO)

Available from: 2021-01-31 Created: 2021-01-31 Last updated: 2021-12-21Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9503-0602

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