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  • 1.
    Almqvist, Håkan
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Kucner, Tomasz Piotr
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Learning to detect misaligned point clouds2018In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 35, no 5, p. 662-677Article in journal (Refereed)
    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 University, School of Science and Technology. National Laboratory of Science and Technology on Automatic Target Recognition, National University of Defense Technology, Changsha, China.
    Kucner, Tomasz Piotr
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Li, Tiancheng
    School of Sciences, University of Salamanca, Salamanca, Spain.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    A Dual PHD Filter for Effective Occupancy Filtering in a Highly Dynamic Environment2018In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 19, no 9, p. 2977-2993Article in journal (Refereed)
    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.
    Hernandez Bennetts, Victor
    et al.
    Örebro University, School of Science and Technology.
    Kucner, Tomasz Piotr
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Neumann, Patrick P.
    Bundesanstalt für Materialforschung und -prüfung, Berlin, Germany.
    Fan, Han
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Probabilistic Air Flow Modelling Using Turbulent and Laminar Characteristics for Ground and Aerial Robots2017In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 2, no 2, p. 1117-1123Article in journal (Refereed)
    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.

  • 4.
    Hernandez Bennetts, Victor
    et al.
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Fan, Han
    Örebro University, School of Science and Technology.
    Kucner, Tomasz Piotr
    Örebro University, School of Science and Technology.
    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 nodes2016In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 131-136, article id 7759045Conference paper (Refereed)
    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.

  • 5.
    Kucner, Tomasz
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Tell me about dynamics!: Mapping velocity fields from sparse samples with Semi-Wrapped Gaussian Mixture Models2016In: Robotics: Science and Systems Conference (RSS 2016), 2016Conference paper (Refereed)
    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.

  • 6.
    Kucner, Tomasz Piotr
    Örebro University, School of Science and Technology.
    Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots2018Doctoral thesis, monograph (Other academic)
    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.

  • 7.
    Kucner, Tomasz Piotr
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Where am I?: An NDT-based prior for MCL2015In: 2015 European Conference on Mobile Robots (ECMR), New York: IEEE conference proceedings , 2015Conference paper (Refereed)
    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.

  • 8.
    Kucner, Tomasz Piotr
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor Manuel
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Enabling Flow Awareness for Mobile Robots in Partially Observable Environments2017In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 2, no 2, p. 1093-1100Article in journal (Refereed)
    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.

  • 9.
    Kucner, Tomasz
    et al.
    Örebro University, School of Science and Technology.
    Sarinen, Jari
    Aalto university, Helsinki, Finland.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Conditional transition maps: learning motion patterns in dynamic environments2013In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, 2013, p. 1196-1201Conference paper (Refereed)
    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.

  • 10.
    Magnusson, Martin
    et al.
    Örebro University, School of Science and Technology.
    Kucner, Tomasz
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Quantitative Evaluation of Coarse-To-Fine Loading Strategies for Material Rehandling2015In: Proceedings of the IEEE International Conference on Automation Science and Engineering (CASE), New York: IEEE conference proceedings , 2015, p. 450-455Conference paper (Refereed)
    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.

  • 11.
    Magnusson, Martin
    et al.
    Örebro University, School of Science and Technology.
    Kucner, Tomasz Piotr
    Örebro University, School of Science and Technology.
    Gholami Shahbandi, Saeed
    IS lab, Halmstad University, Halmstad, Sweden.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Semi-Supervised 3D Place Categorisation by Descriptor Clustering2017In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 620-625Conference paper (Refereed)
    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.

  • 12.
    Palmieri, Luigi
    et al.
    Computer Science Department, University of Freiburg, Freiburg im Breisgau, Germany.
    Kucner, Tomasz
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Arras, Kai
    Bosch Corporate Research, Stuttgart, Germany.
    Kinodynamic Motion Planning on Gaussian Mixture Fields2017In: IEEE International Conference on Robotics and Automation (ICRA 2017), 2017Conference paper (Refereed)
    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.

  • 13.
    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 University, School of Science and Technology.
    Leibe, Bastian
    Rheinisch-Westfälische Technische Hochschule, Aachen, Germany.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Linder, Timm
    Social Robotics Lab, University of Freiburg, Freiburg im Breisgau, Germany.
    Lohse, Manja
    University of Twente, Enschede, Netherlands.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    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 Airports2016In: Field and Service Robotics: Results of the 10th International Conference / [ed] David S. Wettergreen, Timothy D. Barfoot, Springer, 2016, p. 607-622Conference paper (Refereed)
    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.

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