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  • 1.
    Almqvist, Håkan
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Kucner, Tomasz Piotr
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Learning to detect misaligned point clouds2018Ingår i: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 35, nr 5, s. 662-677Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Matching and merging overlapping point clouds is a common procedure in many applications, including mobile robotics, three-dimensional mapping, and object visualization. However, fully automatic point-cloud matching, without manual verification, is still not possible because no matching algorithms exist today that can provide any certain methods for detecting misaligned point clouds. In this article, we make a comparative evaluation of geometric consistency methods for classifying aligned and nonaligned point-cloud pairs. We also propose a method that combines the results of the evaluated methods to further improve the classification of the point clouds. We compare a range of methods on two data sets from different environments related to mobile robotics and mapping. The results show that methods based on a Normal Distributions Transform representation of the point clouds perform best under the circumstances presented herein.

  • 2.
    Fan, Hongqi
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik. National Laboratory of Science and Technology on Automatic Target Recognition, National University of Defense Technology, Changsha, China.
    Kucner, Tomasz Piotr
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Li, Tiancheng
    School of Sciences, University of Salamanca, Salamanca, Spain.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    A Dual PHD Filter for Effective Occupancy Filtering in a Highly Dynamic Environment2018Ingår i: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 19, nr 9, s. 2977-2993Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Environment monitoring remains a major challenge for mobile robots, especially in densely cluttered or highly populated dynamic environments, where uncertainties originated from environment and sensor significantly challenge the robot's perception. This paper proposes an effective occupancy filtering method called the dual probability hypothesis density (DPHD) filter, which models uncertain phenomena, such as births, deaths, occlusions, false alarms, and miss detections, by using random finite sets. The key insight of our method lies in the connection of the idea of dynamic occupancy with the concepts of the phase space density in gas kinetic and the PHD in multiple target tracking. By modeling the environment as a mixture of static and dynamic parts, the DPHD filter separates the dynamic part from the static one with a unified filtering process, but has a higher computational efficiency than existing Bayesian Occupancy Filters (BOFs). Moreover, an adaptive newborn function and a detection model considering occlusions are proposed to improve the filtering efficiency further. Finally, a hybrid particle implementation of the DPHD filter is proposed, which uses a box particle filter with constant discrete states and an ordinary particle filter with a time-varying number of particles in a continuous state space to process the static part and the dynamic part, respectively. This filter has a linear complexity with respect to the number of grid cells occupied by dynamic obstacles. Real-world experiments on data collected by a lidar at a busy roundabout demonstrate that our approach can handle monitoring of a highly dynamic environment in real time.

  • 3.
    Fan, Hongqi
    et al.
    National University of Defense Technology, Changsa, P. R. China.
    Lu, Dawei
    National University of Defense Technology, Changsa, P. R. China.
    Kucner, Tomasz Piotr
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    2D Spatial Keystone Transform for Sub-Pixel Motion Extraction from Noisy Occupancy Grid Map2018Ingår i: Proceedings of 21st International Conference on Information Fusion (FUSION), 2018, s. 2400-2406Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper, we propose a novel sub-pixel motion extraction method, called as Two Dimensional Spatial Keystone Transform (2DS-KST), for the motion detection and estimation from successive noisy Occupancy Grid Maps (OGMs). It extends the KST in radar imaging or motion compensation to 2D real spatial case, based on multiple hypotheses about possible directions of moving obstacles. Simulation results show that 2DS-KST has a good performance on the extraction of sub-pixel motions in very noisy environment, especially for those slowly moving obstacles.

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    2D spatial keystone transform for sub-pixel motion extraction from noisy occupancy grid map
  • 4.
    Hernandez Bennetts, Victor
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Kamarudin, Kamarulzaman
    Center of Excellence for Advanced Sensor Technology, School of Mechatronics Engineering, Universiti Malaysia Perlis, Arau Perlis, Malaysia.
    Wiedemann, Thomas
    Institute of Communications and Navigation, German Aerospace Center, Oberpfaffenhofen, Germany.
    Kucner, Tomasz Piotr
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Somisetty, Sai Lokesh
    Department of Mechatronics, Sastra University, Thanjavur, India.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Multi-Domain Airflow Modeling and Ventilation Characterization Using Mobile Robots, Stationary Sensors and Machine Learning2019Ingår i: Sensors, E-ISSN 1424-8220, Vol. 19, nr 5, artikel-id E1119Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Ventilation systems are critically important components of many public buildings and workspaces. Proper ventilation is often crucial for preventing accidents, such as explosions in mines and avoiding health issues, for example, through long-term exposure to harmful respirable matter. Validation and maintenance of ventilation systems is thus of key interest for plant operators and authorities. However, methods for ventilation characterization, which allow us to monitor whether the ventilation system in place works as desired, hardly exist. This article addresses the critical challenge of ventilation characterization-measuring and modelling air flow at micro-scales-that is, creating a high-resolution model of wind speed and direction from airflow measurements. Models of the near-surface micro-scale flow fields are not only useful for ventilation characterization, but they also provide critical information for planning energy-efficient paths for aerial robots and many applications in mobile robot olfaction. In this article we propose a heterogeneous measurement system composed of static, continuously sampling sensing nodes, complemented by localized measurements, collected during occasional sensing missions with a mobile robot. We introduce a novel, data-driven, multi-domain airflow modelling algorithm that estimates (1) fields of posterior distributions over wind direction and speed ("ventilation maps", spatial domain); (2) sets of ventilation calendars that capture the evolution of important airflow characteristics at measurement positions (temporal domain); and (3) a frequency domain analysis that can reveal periodic changes of airflow in the environment. The ventilation map and the ventilation calendars make use of an improved estimation pipeline that incorporates a wind sensor model and a transition model to better filter out sporadic, noisy airflow changes. These sudden changes may originate from turbulence or irregular activity in the surveyed environment and can, therefore, disturb modelling of the relevant airflow patterns. We tested the proposed multi-domain airflow modelling approach with simulated data and with experiments in a semi-controlled environment and present results that verify the accuracy of our approach and its sensitivity to different turbulence levels and other disturbances. Finally, we deployed the proposed system in two different real-world industrial environments (foundry halls) with different ventilation regimes for three weeks during full operation. Since airflow ground truth cannot be obtained, we present a qualitative discussion of the generated airflow models with plant operators, who concluded that the computed models accurately depicted the expected airflow patterns and are useful to understand how pollutants spread in the work environment. This analysis may then provide the basis for decisions about corrective actions to avoid long-term exposure of workers to harmful respirable matter.

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    Multi-Domain Airflow Modeling and Ventilation Characterization Using Mobile Robots, Stationary Sensors and Machine Learning
  • 5.
    Hernandez Bennetts, Victor
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Kucner, Tomasz Piotr
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Neumann, Patrick P.
    Bundesanstalt für Materialforschung und -prüfung, Berlin, Germany.
    Fan, Han
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Probabilistic Air Flow Modelling Using Turbulent and Laminar Characteristics for Ground and Aerial Robots2017Ingår i: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 2, nr 2, s. 1117-1123Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    For mobile robots that operate in complex, uncontrolled environments, estimating air flow models can be of great importance. Aerial robots use air flow models to plan optimal navigation paths and to avoid turbulence-ridden areas. Search and rescue platforms use air flow models to infer the location of gas leaks. Environmental monitoring robots enrich pollution distribution maps by integrating the information conveyed by an air flow model. In this paper, we present an air flow modelling<?brk?> algorithm that uses wind data collected at a sparse number of locations to estimate joint probability distributions over wind speed and direction at given query locations. The algorithm uses a novel extrapolation approach that models the air flow as a linear combination of laminar and turbulent components. We evaluated the prediction capabilities of our algorithm with data collected with an aerial robot during several exploration runs. The results show that our algorithm has a high degree of stability with respect to parameter selection while outperforming conventional extrapolation approaches. In addition, we applied our proposed approach in an industrial application, where the characterization of a ventilation system is supported by a ground mobile robot. We compared multiple air flow maps recorded over several months by estimating stability maps using the Kullback&ndash;Leibler divergence between the distributions. The results show that, despite local differences, similar air flow patterns prevail over time. Moreover, we corroborated the validity of our results with knowledge from human experts.

  • 6.
    Hernandez Bennetts, Victor
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Fan, Han
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Kucner, Tomasz Piotr
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Andersson, Lena
    Department of Occupational and Environmental Medicine, Örebro University Hospital, Örebro, Sweden.
    Johansson, Anders
    Department of Occupational and Environmental Medicine, Örebro University Hospital, Örebro, Sweden.
    Towards occupational health improvement in foundries through dense dust and pollution monitoring using a complementary approach with mobile and stationary sensing nodes2016Ingår i: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2016, s. 131-136, artikel-id 7759045Konferensbidrag (Refereegranskat)
    Abstract [en]

    In industrial environments, such as metallurgic facilities, human operators are exposed to harsh conditions where ambient air is often polluted with quartz, dust, lead debris and toxic fumes. Constant exposure to respirable particles can cause irreversible health damages and thus it is of high interest for occupational health experts to monitor the air quality on a regular basis. However, current monitoring procedures are carried out sparsely, with data collected in single day campaigns limited to few measurement locations. In this paper we explore the use and present first experimental results of a novel heterogeneous approach that uses a mobile robot and a network of low cost sensing nodes. The proposed system aims to address the spatial and temporal limitations of current monitoring techniques. The mobile robot, along with standard localization and mapping algorithms, allows to produce short term, spatially dense representations of the environment where dust, gas, ambient temperature and airflow information can be modelled. The sensing nodes on the other hand, can collect temporally dense (and usually spatially sparse) information during long periods of time, allowing in this way to register for example, daily variations in the pollution levels. Using data collected with the proposed system in an steel foundry, we show that a heterogeneous approach provides dense spatio-temporal information that can be used to improve the working conditions in industrial facilities.

  • 7.
    Kucner, Tomasz
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Hernandez Bennetts, Victor
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Tell me about dynamics!: Mapping velocity fields from sparse samples with Semi-Wrapped Gaussian Mixture Models2016Ingår i: Robotics: Science and Systems Conference (RSS 2016), 2016Konferensbidrag (Refereegranskat)
    Abstract [en]

    Autonomous mobile robots often require informa-tion about the environment beyond merely the shape of thework-space. In this work we present a probabilistic method formappingdynamics, in the sense of learning and representingstatistics about the flow of discrete objects (e.g., vehicles, people)as well as continuous media (e.g., air flow). We also demonstratethe capabilities of the proposed method with two use cases. Onerelates to motion planning in populated environments, whereinformation about the flow of people can help robots to followsocial norms and to learn implicit traffic rules by observingthe movements of other agents. The second use case relates toMobile Robot Olfaction (MRO), where information about windflow is crucial for most tasks, including e.g. gas detection, gasdistribution mapping and gas source localisation. We representthe underlying velocity field as a set of Semi-Wrapped GaussianMixture Models (SWGMM) representing the learnt local PDF ofvelocities. To estimate the parameters of the PDF we employ aformulation of Expectation Maximisation (EM) algorithm specificfor SWGMM. We also describe a data augmentation methodwhich allows to build a dense dynamic map based on a sparseset of measurements. In case only a small set of observations isavailable we employ a hierarchical sampling method to generatevirtual observations from existing mixtures.

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  • 8.
    Kucner, Tomasz Piotr
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots2018Doktorsavhandling, monografi (Övrigt vetenskapligt)
    Abstract [en]

    To bring robots closer to real-world autonomy, it is necessary to equip them with tools allowing them to perceive, model and behave adequately to dynamic changes in the environment. The idea of incorporating information about dynamics not only in the robots reactive behaviours but also in global planning process stems from the fact that dynamic changes are typically not completely random and follow spatiotemporal patterns. The overarching idea behind the work presented in this thesis is to investigate methods allowing to represent the variety of the real-world spatial motion patterns in a compact, yet expressive way. The primary focus of the presented work is on building maps capturing the motion patterns of dynamic objects and/or the flow of continuous media.

    The contribution of this thesis is twofold. First, I introduce Conditional-Transition Map: a representation for modelling motion patterns of dynamic objects as a multimodal flow of occupancy over a grid map. Furthermore, in this thesis I also propose an extension (Temporal Conditional-Transition Map), which models the speed of said flow. The proposed representations connect the changes of occupancy among adjacent cells. Namely, they build conditional models of the direction to where occupancy is heading given the direction from which the occupancy arrived. Previously, all of the representations modelling dynamics in grid maps assumed cell independence. The representations assuming cell independence are substantially less expressive and store only information about the observed levels of dynamics (i.e. how frequent changes are at a certain location). In contrast, the proposed representations also encode information about the direction of motion. Furthermore, the multimodal and conditional character of the representations allows to distinguish and correctly model intersecting flows. The capabilities of the introduced grid-based representations are demonstrated with experiments performed on real-world data sets.

    In the second part of this thesis, I introduce Circular Linear Flow Field map modelling flow of continuous media and discrete objects. This representation, in contrast to the work presented in the first part of this thesis, does not model occupancy changes directly. Instead, it employs a field of Gaussian Mixture Models, whose local elements are probability distributions of (instantaneous) velocities, to describe motion patterns. Since it assumes only velocity measurements, the proposed representation have been used to model a broad spectrum of dynamics including motion patterns of people and airflow. Using a Gaussian Mixture Model allows to capture the multimodal character of real-world dynamics (e.g. intersecting flows) and also to account for flow variability. In addition to the basic learning algorithms, I present solutions (sampling-based and kernel-based approach) for the problem of building a dense Circular Linear Flow Field map using spatially sparse but temporally dense sets of measurements. In the end, I present how to use the Circular Linear Flow Field map in motion planning to achieve flow compliant trajectories. The capabilities of Circular Linear Flow Field maps are presented and evaluated using simulated and real-world datasets.

    The spectrum of applications for the representations and approaches presented in this thesis is very broad. Among others, the results of this thesis can be used by service robots providing help for passengers in crowded airports or drones surveying landfills to detect leakages of greenhouse gases. In the case of a service robot interacting with passengers in a populated airport, the information about the flow of passengers allows to build not only the shortest path between points “A” and “B” but also enables the robot to behave seamlessly, unobtrusively and safely. In the case of a drone patrolling a landfill the impact of airflow, is equally significant. In this scenario, information about airflow allows harnessing the energy of airstreams to lower the energy consumption of a drone. Another way to utilise information about the wind flow is to use it to improve localisation of sources of gas leakage.

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    Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots
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  • 9.
    Kucner, Tomasz Piotr
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Palmieri, L.
    Corporate Research, Robert Bosch GmbH, Renningen, Germany.
    Swaminathan, Chittaranjan Srinivas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Closing Remarks2020Ingår i: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, s. 143-151Kapitel i bok, del av antologi (Refereegranskat)
    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. 

  • 10.
    Kucner, Tomasz Piotr
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Palmieri, L.
    Corporate Research, Robert Bosch GmbH, Renningen, Germany .
    Swaminathan, Chittaranjan Srinivas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Introduction2020Ingår i: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, s. 1-13Kapitel i bok, del av antologi (Refereegranskat)
    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.

  • 11.
    Kucner, Tomasz Piotr
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Palmieri, L.
    Corporate Research, Robert Bosch GmbH, Renningen, Germany.
    Swaminathan, Chittaranjan Srinivas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Maps of Dynamics2020Ingår i: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, s. 15-32Kapitel i bok, del av antologi (Refereegranskat)
    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. 

  • 12.
    Kucner, Tomasz Piotr
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Palmieri, L.
    Corporate Research, Robert Bosch GmbH, Renningen, Germany.
    Swaminathan, Chittaranjan Srinivas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Modelling Motion Patterns with Circular-Linear Flow Field Maps2020Ingår i: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, s. 65-113Kapitel i bok, del av antologi (Refereegranskat)
    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. 

  • 13.
    Kucner, Tomasz Piotr
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Palmieri, L.
    Corporate Research, Robert Bosch GmbH, Renningen, Germany.
    Swaminathan, Chittaranjan Srinivas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Modelling Motion Patterns with Conditional Transition Map2020Ingår i: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, s. 33-64Kapitel i bok, del av antologi (Refereegranskat)
    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.

  • 14.
    Kucner, Tomasz Piotr
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Palmieri, L.
    Corporate Research, Robert Bosch GmbH, Renningen, Germany.
    Swaminathan, Chittaranjan Srinivas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Motion Planning Using MoDs2020Ingår i: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, s. 115-141Kapitel i bok, del av antologi (Refereegranskat)
    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. 

  • 15.
    Kucner, Tomasz Piotr
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Palmieri, Luigi
    Corporate Research, Robert Bosch GmbH, Renningen, Germany.
    Swaminathan, Chittaranjan Srinivas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots2020Bok (Övrigt vetenskapligt)
    Abstract [en]

    This book describes how robots can make sense of motion in their surroundings and use the patterns they observe to blend in better in dynamic environments shared with humans.The world around us is constantly changing. Nonetheless, we can find our way and aren’t overwhelmed by all the buzz, since motion often follows discernible patterns. Just like humans, robots need to understand the patterns behind the dynamics in their surroundings to be able to efficiently operate e.g. in a busy airport. Yet robotic mapping has traditionally been based on the static world assumption, which disregards motion altogether. In this book, the authors describe how robots can instead explicitly learn patterns of dynamic change from observations, store those patterns in Maps of Dynamics (MoDs), and use MoDs to plan less intrusive, safer and more efficient paths. The authors discuss the pros and cons of recently introduced MoDs and approaches to MoD-informed motion planning, and provide an outlook on future work in this emerging, fascinating field. 

  • 16.
    Kucner, Tomasz Piotr
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Luperto, Matteo
    Applied Intelligent System Lab (AISLab), Università degli Studi di Milano, Milano, Italy.
    Lowry, Stephanie
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Robust Frequency-Based Structure Extraction2021Ingår i: 2021 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2021, s. 1715-1721Konferensbidrag (Refereegranskat)
    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.

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    Robust Frequency-Based Structure Extraction
  • 17.
    Kucner, Tomasz Piotr
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Where am I?: An NDT-based prior for MCL2015Ingår i: 2015 European Conference on Mobile Robots (ECMR), New York: IEEE conference proceedings , 2015Konferensbidrag (Refereegranskat)
    Abstract [en]

    One of the key requirements of autonomous mobile robots is a robust and accurate localisation system. Recent advances in the development of Monte Carlo Localisation (MCL) algorithms, especially the Normal Distribution Transform Monte Carlo Localisation (NDT-MCL), provides memory-efficient reliable localisation with industry-grade precision. We propose an approach for building an informed prior for NDT-MCL (in fact for any MCL algorithm) using an initial observation of the environment and its map. Leveraging on the NDT map representation, we build a set of poses using partial observations. After that we construct a Gaussian Mixture Model (GMM) over it. Next we obtain scores for each distribution in GMM. In this way we obtain in an efficient way a prior for NDT-MCL. Our approach provides a more focused then uniform initial distribution, concentrated in states where the robot is more likely to be, by building a Gaussian mixture model over potential poses. We present evaluations and quantitative results using real-world data from an indoor environment. Our experiments show that, compared to a uniform prior, the proposed method significantly increases the number of successful initialisations of NDT-MCL and reduces the time until convergence, at a negligible initial cost for computing the prior.

  • 18.
    Kucner, Tomasz Piotr
    et al.
    Mobile Robotics Group, School of Electrical Engineering, Aalto University, Finland; Finnish Center for Artificial Intelligence, Finland.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Mghames, Sariah
    L-CAS, School of Computer Science, University of Lincoln, Lincoln, UK.
    Palmieri, Luigi
    BOSCH Corporate Research, Renningen, Germany.
    Verdoja, Francesco
    Intelligent Robotics Group, School of Electrical Engineering, Aalto University, Finland.
    Swaminathan, Chittaranjan Srinivas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Krajnik, Tomas
    Artificial Intelligence Center, Czech Technical University, Praha, Czechia.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Bellotto, Nicola
    L-CAS, School of Computer Science, University of Lincoln, Lincoln, UK; Department of Information Engineering, Univeristy of Padua, Padova, Italy.
    Hanheide, Marc
    L-CAS, School of Computer Science, University of Lincoln, Lincoln, UK.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik. Technical Univeristy of Munich, Munich, Germany.
    Survey of maps of dynamics for mobile robots2023Ingår i: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176, Vol. 42, nr 11, s. 977-1006Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area.

  • 19.
    Kucner, Tomasz Piotr
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Schaffernicht, Erik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Hernandez Bennetts, Victor Manuel
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Enabling Flow Awareness for Mobile Robots in Partially Observable Environments2017Ingår i: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 2, nr 2, s. 1093-1100Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Understanding the environment is a key requirement for any autonomous robot operation. There is extensive research on mapping geometric structure and perceiving objects. However, the environment is also defined by the movement patterns in it. Information about human motion patterns can, e.g., lead to safer and socially more acceptable robot trajectories. Airflow pattern information allow to plan energy efficient paths for flying robots and improve gas distribution mapping. However, modelling the motion of objects (e.g., people) and flow of continuous media (e.g., air) is a challenging task. We present a probabilistic approach for general flow mapping, which can readily handle both of these examples. Moreover, we present and compare two data imputation methods allowing to build dense maps from sparsely distributed measurements. The methods are evaluated using two different data sets: one with pedestrian data and one with wind measurements. Our results show that it is possible to accurately represent multimodal, turbulent flow using a set of Gaussian Mixture Models, and also to reconstruct a dense representation based on sparsely distributed locations.

    Ladda ner fulltext (pdf)
    Enabling Flow Awareness for Mobile Robots in Partially Observable Environments
  • 20.
    Kucner, Tomasz Piotr
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Swaminathan, Chittaranjan Srinivas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Palmieri, L.
    Corporate Research Robert Bosch GmbH, Renningen, Germany.
    Preface2020Ingår i: Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots, Springer, 2020, s. vii-xKapitel i bok, del av antologi (Refereegranskat)
  • 21.
    Kucner, Tomasz
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Sarinen, Jari
    Aalto university, Helsinki, Finland.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Conditional transition maps: learning motion patterns in dynamic environments2013Ingår i: IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, 2013, s. 1196-1201Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper we introduce a method for learning motion patterns in dynamic environments. Representations of dynamic environments have recently received an increasing amount of attention in the research community. Understanding dynamic environments is seen as one of the key challenges in order to enable autonomous navigation in real-world scenarios. However, representing the temporal dimension is a challenge yet to be solved. In this paper we introduce a spatial representation, which encapsulates the statistical dynamic behavior observed in the environment. The proposed Conditional Transition Map (CTMap) is a grid-based representation that associates a probability distribution for an object exiting the cell, given its entry direction. The transition parameters are learned from a temporal signal of occupancy on cells by using a local-neighborhood cross-correlation method. In this paper, we introduce the CTMap, the learning approach and present a proof-of-concept method for estimating future paths of dynamic objects, called Conditional Probability Propagation Tree (CPPTree). The evaluation is done using a real-world data-set collected at a busy roundabout.

    Ladda ner fulltext (pdf)
    ctmap.pdf
  • 22.
    Luperto, Matteo
    et al.
    University of Milan, Milan, Italy.
    Kucner, Tomasz Piotr
    Örebro University, Örebro, Sweden; Aalto University, Espoo, Finland .
    Tassi, Andrea
    Politecnico di Milano, Milan, Italy.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Amigoni, Francesco
    Politecnico di Milano, Milan, Italy.
    Robust Structure Identification and Room Segmentation of Cluttered Indoor Environments From Occupancy Grid Maps2022Ingår i: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 7, nr 3, s. 7974-7981Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Identifying the environment's structure, through detecting core components such as rooms and walls, can facilitate several tasks fundamental for the successful operation of indoor autonomous mobile robots, including semantic environment understanding. These robots often rely on 2D occupancy maps for core tasks such as localisation and motion and task planning. However, reliable identification of structure and room segmentation from 2D occupancy maps is still an open problem due to clutter (e.g., furniture and movable objects), occlusions, and partial coverage. We propose a method for the RObust StructurE identification and ROom SEgmentation (ROSE2) of 2D occupancy maps thatmay be cluttered and incomplete. ROSE2 identifies the main directions of walls and is resilient to clutter and partial observations, allowing to extract a clean, abstract geometrical floor-plan-like description of the environment, which is used to segment, i.e., to identify rooms in, the original occupancy grid map. ROSE2 is tested in several real-world publicly available cluttered maps obtained in different conditions. The results show that it can robustly identify the environment structure in 2D occupancy maps suffering fromclutter and partial observations, while significantly improving room segmentation accuracy. Thanks to the combination of clutter removal and robust room segmentation, ROSE2 consistently achieves higher performance than the state-of-the-art methods, against which it is compared.

  • 23.
    Magnusson, Martin
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Kucner, Tomasz
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Quantitative Evaluation of Coarse-To-Fine Loading Strategies for Material Rehandling2015Ingår i: Proceedings of the IEEE International Conference on Automation Science and Engineering (CASE), New York: IEEE conference proceedings , 2015, s. 450-455Konferensbidrag (Refereegranskat)
    Abstract [en]

    Autonomous handling of piled materials is an emerging topic in automation science and engineering. A central question for material rehandling tasks (transporting materials that have been assembled in piles) is “where to dig, in order to optimise performance”? In particular, we are interested in the application of autonomous wheel loaders to handle piles of gravel. Still, the methodology proposed in this paper relates to granular materials in other applications too. Although initial work on suggesting strategies for where to dig has been done by a few other groups, there has been a lack of structured evaluation of the usefulness of the proposed strategies. In an attempt to further the field, we present a quantitative evaluation of loading strategies; both coarse ones, aiming to maintain a good pile shape over long-term operation; and refined ones, aiming to detect the locally best attack pose for acquiring a good fill grade in the bucket. Using real-world data from a semi-automated test platform, we present an assessment of how previously proposed pile shape measures can be mapped to the amount of material in the bucket after loading. We also present experimental data for long-term strategies, using simulations based on real-world 3D scan data from a production site.

  • 24.
    Magnusson, Martin
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Kucner, Tomasz Piotr
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Gholami Shahbandi, Saeed
    IS lab, Halmstad University, Halmstad, Sweden.
    Andreasson, Henrik
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Semi-Supervised 3D Place Categorisation by Descriptor Clustering2017Ingår i: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2017, s. 620-625Konferensbidrag (Refereegranskat)
    Abstract [en]

    Place categorisation; i. e., learning to group perception data into categories based on appearance; typically uses supervised learning and either visual or 2D range data.

    This paper shows place categorisation from 3D data without any training phase. We show that, by leveraging the NDT histogram descriptor to compactly encode 3D point cloud appearance, in combination with standard clustering techniques, it is possible to classify public indoor data sets with accuracy comparable to, and sometimes better than, previous supervised training methods. We also demonstrate the effectiveness of this approach to outdoor data, with an added benefit of being able to hierarchically categorise places into sub-categories based on a user-selected threshold.

    This technique relieves users of providing relevant training data, and only requires them to adjust the sensitivity to the number of place categories, and provide a semantic label to each category after the process is completed.

    Ladda ner fulltext (pdf)
    fulltext
  • 25.
    Palmieri, Luigi
    et al.
    Computer Science Department, University of Freiburg, Freiburg im Breisgau, Germany.
    Kucner, Tomasz
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Arras, Kai
    Bosch Corporate Research, Stuttgart, Germany.
    Kinodynamic Motion Planning on Gaussian Mixture Fields2017Ingår i: IEEE International Conference on Robotics and Automation (ICRA 2017), IEEE, 2017, s. 6176-6181, artikel-id 7989731Konferensbidrag (Refereegranskat)
    Abstract [en]

    We present a mobile robot motion planning ap-proach under kinodynamic constraints that exploits learnedperception priors in the form of continuous Gaussian mixturefields. Our Gaussian mixture fields are statistical multi-modalmotion models of discrete objects or continuous media in theenvironment that encode e.g. the dynamics of air or pedestrianflows. We approach this task using a recently proposed circularlinear flow field map based on semi-wrapped GMMs whosemixture components guide sampling and rewiring in an RRT*algorithm using a steer function for non-holonomic mobilerobots. In our experiments with three alternative baselines,we show that this combination allows the planner to veryefficiently generate high-quality solutions in terms of pathsmoothness, path length as well as natural yet minimum controleffort motions through multi-modal representations of Gaussianmixture fields.

    Ladda ner fulltext (pdf)
    Kinodynamic Motion Planning on Gaussian Mixture Fields
  • 26.
    Rudenko, Andrey
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik. Robotics Research, Bosch Corporate Research, Stuttgart, Germany.
    Kucner, Tomasz Piotr
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Swaminathan, Chittaranjan Srinivas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Chadalavada, Ravi Teja
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Arras, Kai O.
    Robotics Research, Bosch Corporate Research, Stuttgart, Germany.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    THÖR: Human-Robot Navigation Data Collection and Accurate Motion Trajectories Dataset2020Ingår i: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 5, nr 2, s. 676-682Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Understanding human behavior is key for robots and intelligent systems that share a space with people. Accordingly, research that enables such systems to perceive, track, learn and predict human behavior as well as to plan and interact with humans has received increasing attention over the last years. The availability of large human motion datasets that contain relevant levels of difficulty is fundamental to this research. Existing datasets are often limited in terms of information content, annotation quality or variability of human behavior. In this paper, we present THÖR, a new dataset with human motion trajectory and eye gaze data collected in an indoor environment with accurate ground truth for position, head orientation, gaze direction, social grouping, obstacles map and goal coordinates. THÖR also contains sensor data collected by a 3D lidar and involves a mobile robot navigating the space. We propose a set of metrics to quantitatively analyze motion trajectory datasets such as the average tracking duration, ground truth noise, curvature and speed variation of the trajectories. In comparison to prior art, our dataset has a larger variety in human motion behavior, is less noisy, and contains annotations at higher frequencies.

  • 27.
    Rudenko, Andrey
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Kucner, Tomasz Piotr
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Swaminathan, Chittaranjan Srinivas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Chadalavada, Ravi Teja
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Arras, Kai Oliver
    Bosch Corporate Research, Renningen, Germany.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Benchmarking Human Motion Prediction Methods2020Konferensbidrag (Övrigt vetenskapligt)
    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.

    Ladda ner fulltext (pdf)
    Benchmarking Human Motion Prediction Methods
  • 28.
    Swaminathan, Chittaranjan Srinivas
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Kucner, Tomasz Piotr
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Quantitative Metrics for Execution-Based Evaluation of Human-Aware Global Motion Planning2020Ingår i: HRI 2020 Workshop on Test Methods and Metrics for Effective HRI in Real World Human-Robot Teams, 2020Konferensbidrag (Övrigt vetenskapligt)
  • 29.
    Swaminathan, Chittaranjan Srinivas
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Kucner, Tomasz Piotr
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Palmieri, Luigi
    Robert Bosch, GmbH Corporate Research, Germany.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Down the CLiFF: Flow-Aware Trajectory Planning under Motion Pattern Uncertainty2018Ingår i: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2018, s. 7403-7409Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper we address the problem of flow-aware trajectory planning in dynamic environments considering flow model uncertainty. Flow-aware planning aims to plan trajectories that adhere to existing flow motion patterns in the environment, with the goal to make robots more efficient, less intrusive and safer. We use a statistical model called CLiFF-map that can map flow patterns for both continuous media and discrete objects. We propose novel cost and biasing functions for an RRT* planning algorithm, which exploits all the information available in the CLiFF-map model, including uncertainties due to flow variability or partial observability. Qualitatively, a benefit of our approach is that it can also be tuned to yield trajectories with different qualities such as exploratory or cautious, depending on application requirements. Quantitatively, we demonstrate that our approach produces more flow-compliant trajectories, compared to two baselines.

    Ladda ner fulltext (pdf)
    Down The CLiFF: Flow-aware Trajectory Planning under Motion Pattern Uncertainty
  • 30.
    Triebel, Rudolph
    et al.
    Department of Computer Science, Technische Universität München, Munich, Germany.
    Arras, Kai
    Social Robotics Lab, University of Freiburg, Freiburg im Breisgau, Germany.
    Alami, Rachid
    Laboratory for Analysis and Architecture of Systems (LAAS-CNRS), Toulouse, France.
    Beyer, Lucas
    Rheinisch-Westfälische Technische Hochschule, Aachen, Germany.
    Breuers, Stefan
    Rheinisch-Westfälische Technische Hochschule, Aachen, Germany.
    Chatila, Raja
    Institute for Intelligent Systems and Robotics (ISIR-CNRS), Paris, France.
    Chetouani, Mohamed
    Institute for Intelligent Systems and Robotics (ISIR-CNRS), Paris, France.
    Cremers, Daniel
    Department of Computer Science, Technische Universität München, Munich, Germany.
    Evers, Vanessa
    University of Twente, Enschede, Netherlands.
    Fiore, Michelangelo
    Laboratory for Analysis and Architecture of Systems (LAAS-CNRS), Toulouse, France.
    Hung, Hayley
    Delft University of Technology, Delft, Netherlands.
    Ramirez, Omar A. Islas
    Institute for Intelligent Systems and Robotics (ISIR-CNRS), Paris, France.
    Joosse, Michiel
    University of Twente, Enschede, Netherlands.
    Khambhaita, Harmish
    Laboratory for Analysis and Architecture of Systems (LAAS-CNRS), Toulouse, France.
    Kucner, Tomasz
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Leibe, Bastian
    Rheinisch-Westfälische Technische Hochschule, Aachen, Germany.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Linder, Timm
    Social Robotics Lab, University of Freiburg, Freiburg im Breisgau, Germany.
    Lohse, Manja
    University of Twente, Enschede, Netherlands.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Okal, Billy
    Social Robotics Lab, University of Freiburg, Freiburg im Breisgau, Germany.
    Palmieri, Luigi
    Social Robotics Lab, University of Freiburg, Freiburg im Breisgau, Germany.
    Rafi, Umer
    Rheinisch-Westfälische Technische Hochschule, Aachen, Germany.
    van Rooij, Marieke
    University of Amsterdam, Amsterdam, Netherlands.
    Zhang, Lu
    University of Twente, Enschede, Netherlands; Delft University of Technology, Delft, Netherlands.
    SPENCER: A Socially Aware Service Robot for Passenger Guidance and Help in Busy Airports2016Ingår i: Field and Service Robotics: Results of the 10th International Conference / [ed] David S. Wettergreen, Timothy D. Barfoot, Springer, 2016, s. 607-622Konferensbidrag (Refereegranskat)
    Abstract [en]

    We present an ample description of a socially compliant mobile robotic platform, which is developed in the EU-funded project SPENCER. The purpose of this robot is to assist, inform and guide passengers in large and busy airports. One particular aim is to bring travellers of connecting flights conveniently and efficiently from their arrival gate to the passport control. The uniqueness of the project stems from the strong demand of service robots for this application with a large potential impact for the aviation industry on one side, and on the other side from the scientific advancements in social robotics, brought forward and achieved in SPENCER. The main contributions of SPENCER are novel methods to perceive, learn, and model human social behavior and to use this knowledge to plan appropriate actions in real-time for mobile platforms. In this paper, we describe how the project advances the fields of detection and tracking of individuals and groups, recognition of human social relations and activities, normative human behavior learning, socially-aware task and motion planning, learning socially annotated maps, and conducting empirical experiments to assess socio-psychological effects of normative robot behaviors.

  • 31.
    Vintr, Tomas
    et al.
    Artificial Intelligence Center, Czech Technical University.
    Molina, Sergi
    Lincoln Centre for Autonomous Systems (L-CAS), University of Lincoln.
    Senanayake, Ransalu
    Stanford University.
    Broughton, George
    Artificial Intelligence Center, Czech Technical University.
    Yan, Zhi
    Distributed Artificial Intelligence and Knowledge Laboratory (CIAD), University of Technology of Belfort-Montbeliard (UTBM), France.
    Ulrich, Jiri
    Artificial Intelligence Center, Czech Technical University.
    Kucner, Tomasz P.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Swaminathan, Chittaranjan Srinivas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Majer, Filip
    Artificial Intelligence Center, Czech Technical University.
    Stachova, Maria
    University of Matej Bel, Banska Bystrica, Slovakia.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Krajnik, Tomas
    Artificial Intelligence Center, Czech Technical University.
    Time-varying Pedestrian Flow Models for Service Robots2019Ingår i: 2019 European Conference on Mobile Robots (ECMR), IEEE, 2019, artikel-id 8870909Konferensbidrag (Refereegranskat)
    Abstract [en]

    We present a human-centric spatio-temporal model for service robots operating in densely populated environments for long time periods. The method integrates observations of pedestrians performed by a mobile robot at different locations and times into a memory efficient model, that represents the spatial layout of natural pedestrian flows and how they change over time. To represent temporal variations of the observed flows, our method does not model the time in a linear fashion, but by several dimensions wrapped into themselves. This representation of time can capture long-term (i.e. days to weeks) periodic patterns of peoples’ routines and habits. Knowledge of these patterns allows making long-term predictions of future human presence and walking directions, which can support mobile robot navigation in human-populated environments. Using datasets gathered by a robot for several weeks, we compare the model to state-of-the-art methods for pedestrian flow modelling.

    Ladda ner fulltext (pdf)
    Time-varying Pedestrian Flow Models for Service Robots
  • 32.
    Vintr, Tomas
    et al.
    Czech Technical University in Prague, Prague, the Czech Republic.
    Yan, Zhi
    University of Technology of Belfort-Montbeliard (UTBM), France.
    Eyisoy, Kerem
    Department of Computer Engineering, Faculty of Engineering, Marmara University, Turkey.
    Kubis, Filip
    Czech Technical University in Prague, Prague, the Czech Republic.
    Blaha, Jan
    Czech Technical University in Prague, Prague, the Czech Republic.
    Ulrich, Jiri
    Czech Technical University in Prague, Prague, the Czech Republic.
    Swaminathan, Chittaranjan Srinivas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Molina, Sergi
    University of Lincoln, UK.
    Kucner, Tomasz Piotr
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Cielniak, Grzegorz
    University of Lincoln, UK.
    Faigl, Jan
    Czech Technical University in Prague, Prague, the Czech Republic.
    Duckett, Tom
    University of Lincoln, UK.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Krajnik, Tomas
    Czech Technical University in Prague, Prague, the Czech Republic.
    Natural Criteria for Comparison of Pedestrian Flow Forecasting Models2020Ingår i: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE Press, 2020, s. 11197-11204Konferensbidrag (Refereegranskat)
    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.

  • 33.
    Yu, Wonpil
    et al.
    Electronics and Telecommunications Research Institute, Daejeon, Korea.
    Amigoni, Francesco
    Politecnico di Milano, Milano, Lombardia, Italy.
    Kucner, Tomasz Piotr
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lee, Yu-Cheol
    Electronics and Telecommunications Research Institute, Daejeon, Korea.
    Exploring the Possibility of Semantic Map Data Representation as an Extension of the IEEE 2D and 3D Map Data Representation Standards2020Ingår i: 2020 20th International Conference on Control, Automation and Systmes (ICCAS 2020), 2020Konferensbidrag (Refereegranskat)
    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.

  • 34.
    Zhu, Yufei
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Rudenko, Andrey
    Bosch Corporate Research, Robert Bosch GmbH, Stuttgart, Germany.
    Kucner, Tomasz
    Finnish Center for Artificial Intelligence, School of Electrical Engineering, Aalto University, Finland.
    Palmieri, Luigi
    Bosch Corporate Research, Robert Bosch GmbH, Stuttgart, Germany.
    Arras, Kai
    Bosch Corporate Research, Robert Bosch GmbH, Stuttgart, Germany.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik. TU Munich, Germany.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    CLiFF-LHMP: Using Spatial Dynamics Patterns for Long-Term Human Motion Prediction2023Ingår i: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 01-05 October 2023, Detroit, MI, USA, IEEE, 2023, s. 3795-3802Konferensbidrag (Refereegranskat)
    Abstract [en]

    Human motion prediction is important for mobile service robots and intelligent vehicles to operate safely and smoothly around people. The more accurate predictions are, particularly over extended periods of time, the better a system can, e.g., assess collision risks and plan ahead. In this paper, we propose to exploit maps of dynamics (MoDs, a class of general representations of place-dependent spatial motion patterns, learned from prior observations) for long-term human motion prediction (LHMP). We present a new MoD-informed human motion prediction approach, named CLiFF-LHMP, which is data efficient, explainable, and insensitive to errors from an upstream tracking system. Our approach uses CLiFF -map, a specific MoD trained with human motion data recorded in the same environment. We bias a constant velocity prediction with samples from the CLiFF-map to generate multi-modal trajectory predictions. In two public datasets we show that this algorithm outperforms the state of the art for predictions over very extended periods of time, achieving 45 % more accurate prediction performance at 50s compared to the baseline.

    Ladda ner fulltext (pdf)
    CLiFF-LHMP: Using Spatial Dynamics Patterns for Long-Term Human Motion Prediction
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