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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 clouds2017In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967Article 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.
    Almqvist, Håkan
    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.
    Improving Point Cloud Accuracy Obtained from a Moving Platform for Consistent Pile Attack Pose Estimation2014In: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 75, no 1, p. 101-128Article in journal (Refereed)
    Abstract [en]

    We present a perception system for enabling automated loading with waist-articulated wheel loaders. To enable autonomous loading of piled materials, using either above-ground wheel loaders or underground load-haul-dump vehicles, 3D data of the pile shape is needed. However, using common 3D scanners, the scan data is distorted while the wheel loader is moving towards the pile. Existing methods that make use of 3D scan data (for autonomous loading as well as tasks such as mapping, localisation, and object detection) typically assume that each 3D scan is accurate. For autonomous robots moving over rough terrain, it is often the case that the vehicle moves a substantial amount during the acquisition of one 3D scan, in which case the scan data will be distorted. We present a study of auto-loading methods, and how to locate piles in real-world scenarios with nontrivial ground geometry. We have compared how consistently each method performs for live scans acquired in motion, and also how the methods perform with different view points and scan configurations. The system described in this paper uses a novel method for improving the quality of distorted 3D scans made from a vehicle moving over uneven terrain. The proposed method for improving scan quality is capable of increasing the accuracy of point clouds without assuming any specific features of the environment (such as planar walls), without resorting to a “stop-scan-go” approach, and without relying on specialised and expensive hardware. Each new 3D scan is registered to the preceding using the normal-distributions transform (NDT). After each registration, a mini-loop closure is performed with a local, per-scan, graph-based SLAM method. To verify the impact of the quality improvement, we present data that shows how auto-loading methods benefit from the corrected scans. The presented methods are validated on data from an autonomous wheel loader, as well as with simulated data. The proposed scan-correction method increases the accuracy of both the vehicle trajectory and the point cloud. We also show that it increases the reliability of pile-shape measures used to plan an efficient attack pose when performing autonomous loading.

  • 3.
    Almqvist, Håkan
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Improving Point-Cloud Accuracy from a Moving Platform in Field Operations2013In: 2013 IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings, 2013, p. 733-738Conference paper (Refereed)
    Abstract [en]

    This paper presents a method for improving the quality of distorted 3D point clouds made from a vehicle equipped with a laser scanner moving over uneven terrain. Existing methods that use 3D point-cloud data (for tasks such as mapping, localisation, and object detection) typically assume that each point cloud is accurate. For autonomous robots moving in rough terrain, it is often the case that the vehicle moves a substantial amount during the acquisition of one point cloud, in which case the data will be distorted. The method proposed in this paper is capable of increasing the accuracy of 3D point clouds, without assuming any specific features of the environment (such as planar walls), without resorting to a "stop-scan-go" approach, and without relying on specialised and expensive hardware. Each new point cloud is matched to the previous using normal-distribution-transform (NDT) registration, after which a mini-loop closure is performed with a local, per-scan, graph-based SLAM method. The proposed method increases the accuracy of both the measured platform trajectory and the point cloud. The method is validated on both real-world and simulated data.

  • 4.
    Andreasson, Henrik
    et al.
    Örebro University, School of Science and Technology.
    Adolfsson, Daniel
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    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.
    Incorporating Ego-motion Uncertainty Estimates in Range Data Registration2017In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1389-1395Conference paper (Refereed)
    Abstract [en]

    Local scan registration approaches commonlyonly utilize ego-motion estimates (e.g. odometry) as aninitial pose guess in an iterative alignment procedure. Thispaper describes a new method to incorporate ego-motionestimates, including uncertainty, into the objective function of aregistration algorithm. The proposed approach is particularlysuited for feature-poor and self-similar environments,which typically present challenges to current state of theart registration algorithms. Experimental evaluation showssignificant improvements in accuracy when using data acquiredby Automatic Guided Vehicles (AGVs) in industrial productionand warehouse environments.

  • 5.
    Andreasson, Henrik
    et al.
    Örebro University, School of Science and Technology.
    Bouguerra, Abdelbaki
    Örebro University, School of Science and Technology.
    Cirillo, Marcello
    Örebro University, School of Science and Technology.
    Dimitrov, Dimitar Nikolaev
    INRIA - Grenoble, Meylan, France .
    Driankov, Dimiter
    Örebro University, School of Science and Technology.
    Karlsson, Lars
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Saarinen, Jari Pekka
    Örebro University, School of Science and Technology. Aalto University, Aalto, Finland .
    Sherikov, Aleksander
    Centre de recherche Grenoble, Rhône-Alpes, Grenoble, France .
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Autonomous transport vehicles: where we are and what is missing2015In: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223X, Vol. 22, no 1, p. 64-75Article in journal (Refereed)
    Abstract [en]

    In this article, we address the problem of realizing a complete efficient system for automated management of fleets of autonomous ground vehicles in industrial sites. We elicit from current industrial practice and the scientific state of the art the key challenges related to autonomous transport vehicles in industrial environments and relate them to enabling techniques in perception, task allocation, motion planning, coordination, collision prediction, and control. We propose a modular approach based on least commitment, which integrates all modules through a uniform constraint-based paradigm. We describe an instantiation of this system and present a summary of the results, showing evidence of increased flexibility at the control level to adapt to contingencies.

  • 6.
    Andreasson, Henrik
    et al.
    Örebro University, Department of Technology.
    Duckett, Tom
    University of Lincoln.
    Lilienthal, Achim J.
    A Minimalistic Approach to Appearance-Based Visual SLAM2008In: IEEE Transactions on Robotics, ISSN 1552-3098, Vol. 24, no 5, p. 991-1001Article in journal (Refereed)
    Abstract [en]

    This paper presents a vision-based approach to SLAM in indoor / outdoor environments with minimalistic sensing and computational requirements. The approach is based on a graph representation of robot poses, using a relaxation algorithm to obtain a globally consistent map. Each link corresponds to a relative measurement of the spatial relation between the two nodes it connects. The links describe the likelihood distribution of the relative pose as a Gaussian distribution. To estimate the covariance matrix for links obtained from an omni-directional vision sensor, a novel method is introduced based on the relative similarity of neighbouring images. This new method does not require determining distances to image features using multiple view geometry, for example. Combined indoor and outdoor experiments demonstrate that the approach can handle qualitatively different environments (without modification of the parameters), that it can cope with violations of the “flat floor assumption” to some degree, and that it scales well with increasing size of the environment, producing topologically correct and geometrically accurate maps at low computational cost. Further experiments demonstrate that the approach is also suitable for combining multiple overlapping maps, e.g. for solving the multi-robot SLAM problem with unknown initial poses.

  • 7.
    Andreasson, Henrik
    et al.
    Örebro University, Department of Technology.
    Duckett, Tom
    University of Lincoln, United Kingdom.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Mini-SLAM: minimalistic visual SLAM in large-scale environments based on a new interpretation of image similarity2007In: 2007 IEEE international conference on robotics and automation (ICRA), 2007, p. 4096-4101Conference paper (Refereed)
    Abstract [en]

    This paper presents a vision-based approach to SLAM in large-scale environments with minimal sensing and computational requirements. The approach is based on a graphical representation of robot poses and links between the poses. Links between the robot poses are established based on odometry and image similarity, then a relaxation algorithm is used to generate a globally consistent map. To estimate the covariance matrix for links obtained from the vision sensor, a novel method is introduced based on the relative similarity of neighbouring images, without requiring distances to image features or multiple view geometry. Indoor and outdoor experiments demonstrate that the approach scales well to large-scale environments, producing topologically correct and geometrically accurate maps at minimal computational cost. Mini-SLAM was found to produce consistent maps in an unstructured, large-scale environment (the total path length was 1.4 km) containing indoor and outdoor passages.

  • 8.
    Andreasson, Henrik
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    6D scan registration using depth-interpolated local image features2010In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 58, no 2, p. 157-165Article in journal (Refereed)
    Abstract [en]

    This paper describes a novel registration approach that is based on a combination of visual and 3D range information.To identify correspondences, local visual features obtained from images of a standard color camera are compared and the depth of matching features (and their position covariance) is determined from the range measurements of a 3D laserscanner. The matched depth-interpolated image features allows to apply registration with known correspondences.We compare several ICP variants in this paper and suggest an extension that considers the spatial distance betweenmatching features to eliminate false correspondences. Experimental results are presented in both outdoor and indoor environments. In addition to pair-wise registration, we also propose a global registration method that registers allscan poses simultaneously.

  • 9.
    Andreasson, Henrik
    et al.
    Örebro University, Department of Technology.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Vision based interpolation of 3D laser scans2006Conference paper (Refereed)
    Abstract [en]

    3D range sensors, particularly 3D laser range scanners, enjoy a rising popularity and are used nowadays for many different applications. The resolution 3D range sensors provide in the image plane is typically much lower than the resolution of a modern color camera. In this paper we focus on methods to derive a high-resolution depth image from a low-resolution 3D range sensor and a color image. The main idea is to use color similarity as an indication of depth similarity, based on the observation that depth discontinuities in the scene often correspond to color or brightness changes in the camera image. We present five interpolation methods and compare them with an independently proposed method based on Markov Random Fields. The algorithms proposed in this paper are non-iterative and include a parameter-free vision-based interpolation method. In contrast to previous work, we present ground truth evaluation with real world data and analyse both indoor and outdoor data. Further, we suggest and evaluate four methods to determine a confidence measure for the accuracy of interpolated range values.

  • 10.
    Andreasson, Henrik
    et al.
    Örebro University, School of Science and Technology.
    Saarinen, Jari
    Örebro University, School of Science and Technology.
    Cirillo, Marcello
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Drive the Drive: From Discrete Motion Plans to Smooth Drivable Trajectories2014In: Robotics, E-ISSN 2218-6581, Vol. 3, no 4, p. 400-416Article in journal (Refereed)
    Abstract [en]

    Autonomous navigation in real-world industrial environments is a challenging task in many respects. One of the key open challenges is fast planning and execution of trajectories to reach arbitrary target positions and orientations with high accuracy and precision, while taking into account non-holonomic vehicle constraints. In recent years, lattice-based motion planners have been successfully used to generate kinematically and kinodynamically feasible motions for non-holonomic vehicles. However, the discretized nature of these algorithms induces discontinuities in both state and control space of the obtained trajectories, resulting in a mismatch between the achieved and the target end pose of the vehicle. As endpose accuracy is critical for the successful loading and unloading of cargo in typical industrial applications, automatically planned paths have not been widely adopted in commercial AGV systems. The main contribution of this paper is a path smoothing approach, which builds on the output of a lattice-based motion planner to generate smooth drivable trajectories for non-holonomic industrial vehicles. The proposed approach is evaluated in several industrially relevant scenarios and found to be both fast (less than 2 s per vehicle trajectory) and accurate (end-point pose errors below 0.01 m in translation and 0.005 radians in orientation).

  • 11.
    Andreasson, Henrik
    et al.
    Örebro University, School of Science and Technology.
    Saarinen, Jari
    Örebro University, School of Science and Technology.
    Cirillo, Marcello
    Örebro University, School of Science and Technology. SCANIA AB, Södertälje, Sweden.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Fast, continuous state path smoothing to improve navigation accuracy2015In: IEEE International Conference on Robotics and Automation (ICRA), 2015, IEEE Computer Society, 2015, p. 662-669Conference paper (Refereed)
    Abstract [en]

    Autonomous navigation in real-world industrial environments is a challenging task in many respects. One of the key open challenges is fast planning and execution of trajectories to reach arbitrary target positions and orientations with high accuracy and precision, while taking into account non-holonomic vehicle constraints. In recent years, lattice-based motion planners have been successfully used to generate kinematically and kinodynamically feasible motions for non-holonomic vehicles. However, the discretized nature of these algorithms induces discontinuities in both state and control space of the obtained trajectories, resulting in a mismatch between the achieved and the target end pose of the vehicle. As endpose accuracy is critical for the successful loading and unloading of cargo in typical industrial applications, automatically planned paths have not be widely adopted in commercial AGV systems. The main contribution of this paper addresses this shortcoming by introducing a path smoothing approach, which builds on the output of a lattice-based motion planner to generate smooth drivable trajectories for non-holonomic industrial vehicles. In real world tests presented in this paper we demonstrate that the proposed approach is fast enough for online use (it computes trajectories faster than they can be driven) and highly accurate. In 100 repetitions we achieve mean end-point pose errors below 0.01 meters in translation and 0.002 radians in orientation. Even the maximum errors are very small: only 0.02 meters in translation and 0.008 radians in orientation.

  • 12.
    Andreasson, Henrik
    et al.
    Örebro University, Department of Technology.
    Triebel, Rudolph
    University of Freiburg, Germany.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Non-iterative Vision-based Interpolation of 3D Laser Scans2007In: Autonomous Robots and Agents, Berlin / Heidelberg: Springer , 2007, Vol. 76, p. 83-90Chapter in book (Other academic)
    Abstract [en]

    3D range sensors, particularly 3D laser range scanners, enjoy a rising popularity and are used nowadays for many different applications. The resolution 3D range sensors provide in the image plane is typically much lower than the resolution of a modern colour camera. In this chapter we focus on methods to derive a highresolution depth image from a low-resolution 3D range sensor and a colour image. The main idea is to use colour similarity as an indication of depth similarity, based on the observation that depth discontinuities in the scene often correspond to colour or brightness changes in the camera image. We present five interpolation methods and compare them with an independently proposed method based on Markov random fields. The proposed algorithms are non-iterative and include a parameter-free vision-based interpolation method. In contrast to previous work, we present ground truth evaluation with real world data and analyse both indoor and outdoor data

  • 13.
    Arain, Muhammad Asif
    et al.
    Örebro University, School of Science and Technology.
    Cirillo, Marcello
    Örebro University, School of Science and Technology. Scania AB, Granparksvagen 10, SE-15187 Södertälje, Sweden.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Efficient Measurement Planning for Remote Gas Sensing with Mobile Robots2015In: 2015 IEEE International Conference on Robotics and Automation (ICRA), Washington, USA: IEEE Computer Society, 2015, p. 3428-3434Conference paper (Refereed)
    Abstract [en]

    The problem of gas detection is relevant to manyreal-world applications, such as leak detection in industrialsettings and surveillance. In this paper we address the problemof gas detection in large areas with a mobile robotic platformequipped with a remote gas sensor. We propose a novelmethod based on convex relaxation for quickly finding anexploration plan that guarantees a complete coverage of theenvironment. Our method proves to be highly efficient in termsof computational requirements and to provide nearly-optimalsolutions. We validate our approach both in simulation andin real environments, thus demonstrating its applicability toreal-world problems.

  • 14.
    Arain, Muhammad Asif
    et al.
    Örebro University, School of Science and Technology.
    Fan, Han
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor
    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.
    Improving Gas Tomography With Mobile Robots: An Evaluation of Sensing Geometries in Complex Environments2017In: 2017 ISOCS/IEEE International Symposium on Olfaction andElectronic Nose (ISOEN 2017) Proceedings, 2017, article id 7968895Conference paper (Refereed)
    Abstract [en]

    An accurate model of gas emissions is of high importance in several real-world applications related to monitoring and surveillance. Gas tomography is a non-intrusive optical method to estimate the spatial distribution of gas concentrations using remote sensors. The choice of sensing geometry, which is the arrangement of sensing positions to perform gas tomography, directly affects the reconstruction quality of the obtained gas distribution maps. In this paper, we present an investigation of criteria that allow to determine suitable sensing geometries for gas tomography. We consider an actuated remote gas sensor installed on a mobile robot, and evaluated a large number of sensing configurations. Experiments in complex settings were conducted using a state-of-the-art CFD-based filament gas dispersal simulator. Our quantitative comparison yields preferred sensing geometries for sensor planning, which allows to better reconstruct gas distributions.

  • 15.
    Arain, Muhammad Asif
    et al.
    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 J.
    Örebro University, School of Science and Technology.
    The Right Direction to Smell: Efficient Sensor Planning Strategies for Robot Assisted Gas Tomography2016In: 2016 IEEE International Conference on Robotics and Automation (ICRA), New York, USA: IEEE Robotics and Automation Society, 2016, p. 4275-4281Conference paper (Refereed)
    Abstract [en]

    Creating an accurate model of gas emissions is an important task in monitoring and surveillance applications. A promising solution for a range of real-world applications are gas-sensitive mobile robots with spectroscopy-based remote sensors that are used to create a tomographic reconstruction of the gas distribution. The quality of these reconstructions depends crucially on the chosen sensing geometry. In this paper we address the problem of sensor planning by investigating sensing geometries that minimize reconstruction errors, and then formulate an optimization algorithm that chooses sensing configurations accordingly. The algorithm decouples sensor planning for single high concentration regions (hotspots) and subsequently fuses the individual solutions to a global solution consisting of sensing poses and the shortest path between them. The proposed algorithm compares favorably to a template matching technique in a simple simulation and in a real-world experiment. In the latter, we also compare the proposed sensor planning strategy to the sensing strategy of a human expert and find indications that the quality of the reconstructed map is higher with the proposed algorithm.

  • 16.
    Arain, Muhammad Asif
    et al.
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Cirillo, Marcello
    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.
    Global coverage measurement planning strategies for mobile robots equipped with a remote gas sensor2015In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 15, no 3, p. 6845-6871Article in journal (Refereed)
    Abstract [en]

    The problem of gas detection is relevant to many real-world applications, such as leak detection in industrial settings and landfill monitoring. In this paper, we address the problem of gas detection in large areas with a mobile robotic platform equipped with a remote gas sensor. We propose an algorithm that leverages a novel method based on convex relaxation for quickly solving sensor placement problems, and for generating an efficient exploration plan for the robot. To demonstrate the applicability of our method to real-world environments, we performed a large number of experimental trials, both on randomly generated maps and on the map of a real environment. Our approach proves to be highly efficient in terms of computational requirements and to provide nearly-optimal solutions.

  • 17.
    Asadi, Sahar
    et al.
    Örebro University, School of Science and Technology.
    Badica, Costin
    University of Craiova, Craiova, Romania.
    Comes, Tina
    Karslruhe Institute of Technology, Karslruhe, Germany.
    Conrado, Claudine
    Thales Research and Technology, Delft, The Nederlands.
    Evers, Vanessa
    University of Amsterdam, Amsterdam, The Netherlands.
    Groen, Frans
    University of Amsterdam, Amsterdam, The Netherlands.
    Illie, Sorin
    University of Craiova, Craiova, Romania.
    Steen Jensen, Jan
    Danish Emergency Management Agency (DEMA), Birkerød, Denmark.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Milan, Bianca
    DCMR, Delft, The Netherlands.
    Neidhart, Thomas
    Space Applications Services, Zaventem, Belgium.
    Nieuwenhuis, Kees
    Thales Research and Technology, Delft, The Netherlands.
    Pashami, Sepideh
    Örebro University, School of Science and Technology.
    Pavlin, Gregor
    Thales Research and Technology, Delft, The Netherlands.
    Pehrsson, Jan
    Prolog Development Center, Brøndby Copenhagen, Denmark.
    Pinchuk, Rani
    Space Applications and Services, Zaventem, Belgium.
    Scafes, Mihnea
    University of Craiova, Craiova, Romania.
    Schou-Jensen, Leo
    DCMR, Brøndby Copenhagen, Denmark.
    Schultmann, Frank
    Karslruhe Institute of Technology, Karlsruhe, Germany.
    Wijngaards, Niek
    Thales Research and Technology, Delft, the Netherlands.
    ICT solutions supporting collaborative information acquisition, situation assessment and decision making in contemporary environmental management problems: the DIADEM approach2011In: Proceedings of the 25th EnviroInfo Conference "Environmental Informatics", Herzogenrath: Shaker Verlag, 2011, p. 920-931Conference paper (Refereed)
    Abstract [en]

    This paper presents a framework of ICT solutions developed in the EU research project DIADEM that supports environmental management with an enhanced capacity to assess population exposure and health risks, to alert relevant groups and to organize efficient response. The emphasis is on advanced solutions which are economically feasible and maximally exploit the existing communication, computing and sensing resources. This approach enables efficient situation assessment in complex environmental management problems by exploiting relevant information obtained from citizens via the standard communication infrastructure as well as heterogeneous data acquired through dedicated sensing systems. This is achieved through a combination of (i) advanced approaches to gas detection and gas distribution modelling, (ii) a novel service-oriented approach supporting seamless integration of human-based and automated reasoning processes in large-scale collaborative sense making processes and (iii) solutions combining Multi-Criteria Decision Analysis, Scenario-Based Reasoning and advanced human-machine interfaces. This paper presents the basic principles of the DIADEM solutions, explains how different techniques are combined to a coherent decision support system and briefly discusses evaluation principles and activities in the DIADEM project.

  • 18.
    Asadi, Sahar
    et al.
    Örebro University, School of Science and Technology.
    Fan, Han
    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.
    Time-dependent gas distribution modelling2017In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 96, p. 157-170Article in journal (Refereed)
    Abstract [en]

    Artificial olfaction can help to address pressing environmental problems due to unwanted gas emissions. Sensor networks and mobile robots equipped with gas sensors can be used for e.g. air pollution monitoring. Key in this context is the ability to derive truthful models of gas distribution from a set of sparse measurements. Most statistical gas distribution modelling methods assume that gas dispersion is a time constant random process. While this assumption approximately holds in some situations, it is necessary to model variations over time in order to enable applications of gas distribution modelling in a wider range of realistic scenarios. Time-invariant approaches cannot model well evolving gas plumes, for example, or major changes in gas dispersion due to a sudden change of the environmental conditions. This paper presents two approaches to gas distribution modelling, which introduce a time-dependency and a relation to a time-scale in generating the gas distribution model either by sub-sampling or by introducing a recency weight that relates measurement and prediction time. We evaluated these approaches in experiments performed in two real environments as well as on several simulated experiments. As expected, the comparison of different sub-sampling strategies revealed that more recent measurements are more informative to derive an estimate of the current gas distribution as long as a sufficient spatial coverage is given. Next, we compared a time-dependent gas distribution modelling approach (TD Kernel DM+V), which includes a recency weight, to the state-of-the-art gas distribution modelling approach (Kernel DM+V), which does not consider sampling times. The results indicate a consistent improvement in the prediction of unseen measurements, particularly in dynamic scenarios. Furthermore, this paper discusses the impact of meta-parameters in model selection and compares the performance of time-dependent GDM in different plume conditions. Finally, we investigated how to set the target time for which the model is created. The results indicate that TD Kernel DM+V performs best when the target time is set to the maximum sampling time in the test set.

  • 19.
    Asadi, Sahar
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Approaches to Time-Dependent Gas Distribution Modelling2015In: 2015 European Conference on Mobile Robots (ECMR), New York: IEEE conference proceedings , 2015, article id 7324215Conference paper (Refereed)
    Abstract [en]

    Mobile robot olfaction solutions for gas distribution modelling offer a number of advantages, among them autonomous monitoring in different environments, mobility to select sampling locations, and ability to cooperate with other systems. However, most data-driven, statistical gas distribution modelling approaches assume that the gas distribution is generated by a time-invariant random process. Such time-invariant approaches cannot model well developing plumes or fundamental changes in the gas distribution. In this paper, we discuss approaches that explicitly consider the measurement time, either by sub-sampling according to a given time-scale or by introducing a recency weight that relates measurement and prediction time. We evaluate the performance of these time-dependent approaches in simulation and in real-world experiments using mobile robots. The results demonstrate that in dynamic scenarios improved gas distribution models can be obtained with time-dependent approaches.

  • 20.
    Asadi, Sahar
    et al.
    Örebro University, School of Science and Technology.
    Pashami, Sepideh
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    TD Kernel DM+V: time-dependent statistical gas distribution modelling on simulated measurements2011In: Olfaction and Electronic Nose: proceedings of the 14th International Symposium on Olfaction and Electronic Nose (ISOEN) / [ed] Perena Gouma, Springer Science+Business Media B.V., 2011, p. 281-282Conference paper (Refereed)
    Abstract [en]

    To study gas dispersion, several statistical gas distribution modelling approaches have been proposed recently. A crucial assumption in these approaches is that gas distribution models are learned from measurements that are generated by a time-invariant random process. While a time-independent random process can capture certain fluctuations in the gas distribution, more accurate models can be obtained by modelling changes in the random process over time. In this work we propose a time-scale parameter that relates the age of measurements to their validity for building the gas distribution model in a recency function. The parameters of the recency function define a time-scale and can be learned. The time-scale represents a compromise between two conflicting requirements for obtaining accurate gas distribution models: using as many measurements as possible and using only very recent measurements. We have studied several recency functions in a time-dependent extension of the Kernel DM+V algorithm (TD Kernel DM+V). Based on real-world experiments and simulations of gas dispersal (presented in this paper) we demonstrate that TD Kernel DM+V improves the obtained gas distribution models in dynamic situations. This represents an important step towards statistical modelling of evolving gas distributions.

  • 21.
    Asadi, Sahar
    et al.
    Örebro University, School of Science and Technology.
    Reggente, Matteo
    Örebro University, School of Science and Technology.
    Stachniss, Cyrill
    University of Freiburg, Freiburg, Germany.
    Plagemann, Christian
    Stanford University, Stanford CA, USA.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Statistical gas distribution modeling using kernel methods2011In: Intelligent systems for machine olfaction: tools and methodologies / [ed] E. L. Hines and M. S. Leeson, IGI Global, 2011, 1, p. 153-179Chapter in book (Refereed)
    Abstract [en]

    Gas distribution models can provide comprehensive information about a large number of gas concentration measurements, highlighting, for example, areas of unusual gas accumulation. They can also help to locate gas sources and to plan where future measurements should be carried out. Current physical modeling methods, however, are computationally expensive and not applicable for real world scenarios with real-time and high resolution demands. This chapter reviews kernel methodsthat statistically model gas distribution. Gas measurements are treated as randomvariables, and the gas distribution is predicted at unseen locations either using akernel density estimation or a kernel regression approach. The resulting statistical 

    apmodelsdo not make strong assumptions about the functional form of the gas distribution,such as the number or locations of gas sources, for example. The majorfocus of this chapter is on two-dimensional models that provide estimates for themeans and predictive variances of the distribution. Furthermore, three extensionsto the presented kernel density estimation algorithm are described, which allow toinclude wind information, to extend the model to three dimensions, and to reflecttime-dependent changes of the random process that generates the gas distributionmeasurements. All methods are discussed based on experimental validation usingreal sensor data.

  • 22.
    Bennetts, Victor Hernandez
    et al.
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Robot Assisted Gas Tomography - Localizing Methane Leaks in Outdoor Environments2014In: 2014 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), IEEE conference proceedings, 2014, p. 6362-6367Conference paper (Refereed)
    Abstract [en]

    In this paper we present an inspection robot to produce gas distribution maps and localize gas sources in large outdoor environments. The robot is equipped with a 3D laser range finder and a remote gas sensor that returns integral concentration measurements. We apply principles of tomography to create a spatial gas distribution model from integral gas concentration measurements. The gas distribution algorithm is framed as a convex optimization problem and it models the mean distribution and the fluctuations of gases. This is important since gas dispersion is not an static phenomenon and furthermore, areas of high fluctuation can be correlated with the location of an emitting source. We use a compact surface representation created from the measurements of the 3D laser range finder with a state of the art mapping algorithm to get a very accurate localization and estimation of the path of the laser beams. In addition, a conic model for the beam of the remote gas sensor is introduced. We observe a substantial improvement in the gas source localization capabilities over previous state-of-the-art in our evaluation carried out in an open field environment.

  • 23.
    Blanco, Jose Luis
    et al.
    University of Màlaga, Màlaga, Spain.
    Monroy, Javier G.
    University of Màlaga, Màlaga, Spain.
    Gonzalez-Jimenez, Javier
    University of Màlaga, Màlaga, Spain.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    A Kalman Filter Based Approach To Probabilistic Gas Distribution Mapping2013Conference paper (Refereed)
    Abstract [en]

    Building a model of gas concentrations has important indus-trial and environmental applications, and mobile robots ontheir own or in cooperation with stationary sensors play animportant role in this task. Since an exact analytical de-scription of turbulent flow remains an intractable problem,we propose an approximate approach which not only esti-mates the concentrations but also their variances for eachlocation. Our point of view is that of sequential Bayesianestimation given a lattice of 2D cells treated as hidden vari-ables. We first discuss how a simple Kalman filter pro-vides a solution to the estimation problem. To overcomethe quadratic computational complexity with the mappedarea exhibited by a straighforward application of Kalmanfiltering, we introduce a sparse implementation which runsin constant time. Experimental results for a real robot vali-date the proposed method.

  • 24.
    Bouguerra, Abdelbaki
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Åstrand, Björn
    Halmstad University, Halmstad, Sweden.
    Rögnvaldsson, Thorsteinn
    Halmstad University, Halmstad, Sweden.
    An autonomous robotic system for load transportation2009In: 2009 IEEE Conference on Emerging Technologies & Factory Automation (EFTA 2009), New York: IEEE conference proceedings, 2009, p. 1563-1566Conference paper (Refereed)
    Abstract [en]

    This paper presents an overview of an autonomous robotic material handling system. The goal of the system is to extend the functionalities of traditional AGVs to operate in highly dynamic environments. Traditionally, the reliable functioning of AGVs relies on the availability of adequate infrastructure to support navigation. In the target environments of our system, such infrastructure is difficult to setup in an efficient way. Additionally, the location of objects to handle are unknown, which requires that the system be able to detect and track object positions at runtime. Another requirement of the system is to be able to generate trajectories dynamically, which is uncommon in industrial AGV systems.

  • 25.
    Bouguerra, Abdelbaki
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Åstrand, Björn
    Halmstad University.
    Rögnvaldsson, Thorsteinn
    Halmstad University, Sweden.
    MALTA: a system of multiple autonomous trucks for load transportation2009In: Proceedings of the 4th European conference on mobile robots (ECMR) / [ed] Ivan Petrovic, Achim J. Lilienthal, 2009, p. 93-98Conference paper (Refereed)
    Abstract [en]

    This paper presents an overview of an autonomousrobotic material handling system. The goal of the system is toextend the functionalities of traditional AGVs to operate in highlydynamic environments. Traditionally, the reliable functioning ofAGVs relies on the availability of adequate infrastructure tosupport navigation. In the target environments of our system,such infrastructure is difficult to setup in an efficient way.Additionally, the location of objects to handle are unknown,which requires that the system be able to detect and track objectpositions at runtime. Another requirement of the system is to beable to generate trajectories dynamically, which is uncommon inindustrial AGV systems.

  • 26.
    Bunz, Elsa
    et al.
    Örebro University, Örebro, Sweden.
    Chadalavada, Ravi Teja
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Krug, Robert
    Örebro University, School of Science and Technology.
    Schindler, Maike
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Spatial Augmented Reality and Eye Tracking for Evaluating Human Robot Interaction2016In: Proceedings of RO-MAN 2016 Workshop: Workshop on Communicating Intentions in Human-Robot Interaction, 2016Conference paper (Refereed)
    Abstract [en]

    Freely moving autonomous mobile robots may leadto anxiety when operating in workspaces shared with humans.Previous works have given evidence that communicating in-tentions using Spatial Augmented Reality (SAR) in the sharedworkspace will make humans more comfortable in the vicinity ofrobots. In this work, we conducted experiments with the robotprojecting various patterns in order to convey its movementintentions during encounters with humans. In these experiments,the trajectories of both humans and robot were recorded witha laser scanner. Human test subjects were also equipped withan eye tracker. We analyzed the eye gaze patterns and thelaser scan tracking data in order to understand how the robot’sintention communication affects the human movement behavior.Furthermore, we used retrospective recall interviews to aid inidentifying the reasons that lead to behavior changes.

  • 27.
    Canelhas, Daniel R.
    et al.
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Davison, Andrew J.
    Department of Computing, Imperial College London, London, United Kingdom.
    Compressed Voxel-Based Mapping Using Unsupervised Learning2017In: Robotics, E-ISSN 2218-6581, Vol. 6, no 3, article id 15Article in journal (Refereed)
    Abstract [en]

    In order to deal with the scaling problem of volumetric map representations, we propose spatially local methods for high-ratio compression of 3D maps, represented as truncated signed distance fields. We show that these compressed maps can be used as meaningful descriptors for selective decompression in scenarios relevant to robotic applications. As compression methods, we compare using PCA-derived low-dimensional bases to nonlinear auto-encoder networks. Selecting two application-oriented performance metrics, we evaluate the impact of different compression rates on reconstruction fidelity as well as to the task of map-aided ego-motion estimation. It is demonstrated that lossily reconstructed distance fields used as cost functions for ego-motion estimation can outperform the original maps in challenging scenarios from standard RGB-D (color plus depth) data sets due to the rejection of high-frequency noise content.

  • 28.
    Canelhas, Daniel R.
    et al.
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    From Feature Detection in Truncated Signed Distance Fields to Sparse Stable Scene Graphs2016In: IEEE Robotics and Automation Letters, ISSN 2377-3766, Vol. 1, no 2, p. 1148-1155Article in journal (Refereed)
    Abstract [en]

    With the increased availability of GPUs and multicore CPUs, volumetric map representations are an increasingly viable option for robotic applications. A particularly important representation is the truncated signed distance field (TSDF) that is at the core of recent advances in dense 3D mapping. However, there is relatively little literature exploring the characteristics of 3D feature detection in volumetric representations. In this paper we evaluate the performance of features extracted directly from a 3D TSDF representation. We compare the repeatability of Integral invariant features, specifically designed for volumetric images, to the 3D extensions of Harris and Shi & Tomasi corners. We also study the impact of different methods for obtaining gradients for their computation. We motivate our study with an example application for building sparse stable scene graphs, and present an efficient GPU-parallel algorithm to obtain the graphs, made possible by the combination of TSDF and 3D feature points. Our findings show that while the 3D extensions of 2D corner-detection perform as expected, integral invariants have shortcomings when applied to discrete TSDFs. We conclude with a discussion of the cause for these points of failure that sheds light on possible mitigation strategies.

  • 29.
    Canelhas, Daniel R.
    et al.
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Improved local shape feature stability through dense model tracking2013In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2013, p. 3203-3209Conference paper (Refereed)
    Abstract [en]

    In this work we propose a method to effectively remove noise from depth images obtained with a commodity structured light sensor. The proposed approach fuses data into a consistent frame of reference over time, thus utilizing prior depth measurements and viewpoint information in the noise removal process. The effectiveness of the approach is compared to two state of the art, single-frame denoising methods in the context of feature descriptor matching and keypoint detection stability. To make more general statements about the effect of noise removal in these applications, we extend a method for evaluating local image gradient feature descriptors to the domain of 3D shape descriptors. We perform a comparative study of three classes of such descriptors: Normal Aligned Radial Features, Fast Point Feature Histograms and Depth Kernel Descriptors; and evaluate their performance on a real-world industrial application data set. We demonstrate that noise removal enabled by the dense map representation results in major improvements in matching across all classes of descriptors as well as having a substantial positive impact on keypoint detection reliability

  • 30.
    Canelhas, Daniel R.
    et al.
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    SDF tracker: a parallel algorithm for on-line pose estimation and scene reconstruction from depth images2013In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2013, p. 3671-3676Conference paper (Refereed)
    Abstract [en]

    Ego-motion estimation and environment mapping are two recurring problems in the field of robotics. In this work we propose a simple on-line method for tracking the pose of a depth camera in six degrees of freedom and simultaneously maintaining an updated 3D map, represented as a truncated signed distance function. The distance function representation implicitly encodes surfaces in 3D-space and is used directly to define a cost function for accurate registration of new data. The proposed algorithm is highly parallel and achieves good accuracy compared to state of the art methods. It is suitable for reconstructing single household items, workspace environments and small rooms at near real-time rates, making it practical for use on modern CPU hardware

  • 31.
    Chadalavada, Ravi Teja
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Krug, Robert
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Empirical evaluation of human trust in an expressive mobile robot2016In: Proceedings of RSS Workshop "Social Trust in Autonomous Robots 2016", 2016Conference paper (Refereed)
    Abstract [en]

    A mobile robot communicating its intentions using Spatial Augmented Reality (SAR) on the shared floor space makes humans feel safer and more comfortable around the robot. Our previous work [1] and several other works established this fact. We built upon that work by adding an adaptable information and control to the SAR module. An empirical study about how a mobile robot builds trust in humans by communicating its intentions was conducted. A novel way of evaluating that trust is presented and experimentally shown that adaption in SAR module lead to natural interaction and the new evaluation system helped us discover that the comfort levels between human-robot interactions approached those of human-human interactions.

  • 32.
    Chadalavada, Ravi Teja
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Krug, Robert
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    That’s on my Mind!: Robot to Human Intention Communication through on-board Projection on Shared Floor Space2015In: 2015 European Conference on Mobile Robots (ECMR), New York: IEEE conference proceedings , 2015Conference paper (Refereed)
    Abstract [en]

    The upcoming new generation of autonomous vehicles for transporting materials in industrial environments will be more versatile, flexible and efficient than traditional AGVs, which simply follow pre-defined paths. However, freely navigating vehicles can appear unpredictable to human workers and thus cause stress and render joint use of the available space inefficient. Here we address this issue and propose on-board intention projection on the shared floor space for communication from robot to human. We present a research prototype of a robotic fork-lift equipped with a LED projector to visualize internal state information and intents. We describe the projector system and discuss calibration issues. The robot’s ability to communicate its intentions is evaluated in realistic situations where test subjects meet the robotic forklift. The results show that already adding simple information, such as the trajectory and the space to be occupied by the robot in the near future, is able to effectively improve human response to the robot.

  • 33.
    Charusta, Krzysztof
    et al.
    Örebro University, School of Science and Technology.
    Dimitrov, Dimitar
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Iliev, Boyko
    Örebro University, School of Science and Technology.
    Extraction of grasp-related features by human dual-hand object exploration2009In: 2009 International Conference on Advanced Robotics, Piscataway, NJ: IEEE conference proceedings, 2009, p. 1-6Conference paper (Refereed)
    Abstract [en]

    We consider the problem of objects exploration for grasping purposes, specifically in cases where vision based methods are not applicable. A novel dual-hand object exploration method is proposed that takes benefits from a human demonstration to enrich knowledge about an object. The user handles an object freely using both hands, without restricting the object pose. A set of grasp-related features obtained during exploration is demonstrated and utilized to generate grasp oriented bounding boxes that are basis for pre-grasp hypothesis. We believe that such exploration done in a natural and user friendly way creates important link between an operator intention and a robot action.

  • 34.
    Cielniak, Grzegorz
    et al.
    Sch Comp Sci, Lincoln Univ, Lincoln Lincolnshire, England.
    Duckett, Tom
    Sch Comp Sci, Lincoln Univ, Lincoln Lincolnshire, England.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Data association and occlusion handling for vision-based people tracking by mobile robots2010In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 58, no 5, p. 435-443Article in journal (Refereed)
    Abstract [en]

    This paper presents an approach for tracking multiple persons on a mobile robot with a combination of colour and thermal vision sensors, using several new techniques. First, an adaptive colour model is incorporated into the measurement model of the tracker. Second, a new approach for detecting occlusions is introduced, using a machine learning classifier for pairwise comparison of persons (classifying which one is in front of the other). Third, explicit occlusion handling is incorporated into the tracker. The paper presents a comprehensive, quantitative evaluation of the whole system and its different components using several real world data sets. (C) 2010 Elsevier B.V. All rights reserved.

  • 35.
    Cielniak, Grzegorz
    et al.
    Örebro University, Department of Technology.
    Duckett, Tom
    Örebro University, Department of Technology. Department of Computing and Informatics, University of Lincoln.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Improved data association and occlusion handling for vision-based people tracking by mobile robots2007In: IEEE/RSJ international conference on intelligent robots and systems, 2007. IROS 2007, 2007, p. 3436-3441Conference paper (Refereed)
    Abstract [en]

    This paper presents an approach for tracking multiple persons using a combination of colour and thermal vision sensors on a mobile robot. First, an adaptive colour model is incorporated into the measurement model of the tracker. Second, a new approach for detecting occlusions is introduced, using a machine learning classifier for pairwise comparison of persons (classifying which one is in front of the other). Third, explicit occlusion handling is then incorporated into the tracker.

  • 36.
    Cielniak, Grzegorz
    et al.
    Örebro University, Department of Technology.
    Miladinovic, Mihajlo
    Dept. of Technology, AASS, Örebro University, Örebro, Sweden.
    Hammarin, Daniel
    Dept. of Technology, AASS, Örebro University, Örebro, Sweden.
    Göransson, Linus
    Dept. of Technology, AASS, Örebro University, Örebro, Sweden.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Duckett, Tom
    Örebro University, Department of Technology.
    Appearance-based tracking of persons with an omnidirectional vision sensor2003In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, IEEE, 2003, Vol. 7, article id 4624346Conference paper (Refereed)
    Abstract [en]

    This paper addresses the problem of tracking a moving person with a single, omnidirectional camera. An appearance-based tracking system is described which uses a self-acquired appearance model and a Kalman filter to estimate the position of the person. Features corresponding to ``depth cues'' are first extracted from the panoramic images, then an artificial neural network is trained to estimate the distance of the person from the camera. The estimates are combined using a discrete Kalman filter to track the position of the person over time. The ground truth information required for training the neural network and the experimental analysis was obtained from another vision system, which uses multiple webcams and triangulation to calculate the true position of the person. Experimental results show that the tracking system is accurate and reliable, and that its performance can be further improved by learning multiple, person-specific appearance models

  • 37.
    Duckett, Tom
    et al.
    Lincoln School of Computer Science, University of Lincoln, Brayford Pool, Lincoln, United Kingdom.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Special Issue: Selected Papers from the 5th European Conference on Mobile Robots (ECMR 2011)2013In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 61, no 10, p. 1049-1050Article in journal (Other academic)
  • 38.
    Echelmeyer, Wolfgang
    et al.
    University of Reutlingen, Reutlingen, Germany.
    Kirchheim, Alice
    School of Science and Technology, Örebro University, Örebro, Sweden.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Akbiyik, Hülya
    University of Reutlingen, Reutlingen, Germany.
    Bonini, Marco
    University of Reutlingen, Reutlingen, Germany.
    Performance Indicators for Robotics Systems in Logistics Applications2011Conference paper (Refereed)
    Abstract [en]

    The transfer of research results to market-ready products is often a costly and time-consuming process. In order to generate successful products, researchers must cooperate with industrial companies; both the industrial and academic partners need to have a detailed understanding of the requirements of all parties concerned. Academic researchers need to identify the performance indicators for technical systems within a business environment and be able to apply them.

    Inservice logistics today, nearly all standardized mass goods are unloaded manually with one reason for this being the undefined position and orientation of the goods in the carrier. A study regarding the qualitative and quantitative properties of goods that are transported in containers shows that there is a huge economic relevance for autonomous systems. In 2008, more than 8,4 billion Twenty-foot equivalent units (TEU) were imported and unloaded manually at European ports, corresponding to more than 331,000 billion single goods items.

    Besides the economic relevance, the opinion of market participants is an important factor for the success of new systems on the market. The main outcomes of a study regarding the challenges, opportunities and barriers in robotic-logistics, allow for the estimation of the economic efficiency of performance indicators, performance flexibility and soft factors. The economic efficiency of the performance parameters is applied to the parcel robot – a cognitive system to unload parcels autonomously from containers. In the following article, the results of the study are presented and the resultant conclusions discussed.

  • 39.
    Fan, Han
    et al.
    Örebro University, School of Science and Technology.
    Arain, Muhammad Asif
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor
    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.
    Improving Gas Dispersal Simulation For Mobile Robot Olfaction: Using Robotcreatedoccupancy Maps And Remote Gas Sensors In The Simulation Loop2017In: 2017 ISOCS/IEEE International Symposium on Olfaction andElectronic Nose (ISOEN 2017) Proceedings, IEEE conference proceedings, 2017, article id 17013581Conference paper (Refereed)
    Abstract [en]

    Mobile robot platforms equipped with olfaction systems have been used in many gas sensing applications. However, in-field validation of mobile robot olfaction systems is time consuming, expensive, cumbersome and lacks repeatability. In order to address these issues, simulation tools are used. However, the available mobile robot olfaction simulations lack models for remote gas sensors, and the possibility to import geometrical representations of actual real-world environments in a convenient way. In this paper, we describe extensions to an open-source CFD-based filament gas dispersal simulator. These improvements arrow to use robot-created occupancy maps and offer remote sensing capabilities in the simulation loop. We demonstrate the novel features in an example application: we created a 3D map a complex indoor environment, and performed a gas emission monitoring task with a Tunable Diode Laser Absorption Spectroscopy based remote gas sensor in a simulated version of the environment.

  • 40.
    Fan, Han
    et al.
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    A cluster analysis approach based on exploiting density peaks for gas discrimination with electronic noses in open environments2018In: Sensors and actuators. B, Chemical, ISSN 0925-4005, E-ISSN 1873-3077, Vol. 259, p. 183-203Article in journal (Refereed)
    Abstract [en]

    Gas discrimination in open and uncontrolled environments based on smart low-cost electro-chemical sensor arrays (e-noses) is of great interest in several applications, such as exploration of hazardous areas, environmental monitoring, and industrial surveillance. Gas discrimination for e-noses is usually based on supervised pattern recognition techniques. However, the difficulty and high cost of obtaining extensive and representative labeled training data limits the applicability of supervised learning. Thus, to deal with the lack of information regarding target substances and unknown interferents, unsupervised gas discrimination is an advantageous solution. In this work, we present a cluster-based approach that can infer the number of different chemical compounds, and provide a probabilistic representation of the class labels for the acquired measurements in a given environment. Our approach is validated with the samples collected in indoor and outdoor environments using a mobile robot equipped with an array of commercial metal oxide sensors. Additional validation is carried out using a multi-compound data set collected with stationary sensor arrays inside a wind tunnel under various airflow conditions. The results show that accurate class separation can be achieved with a low sensitivity to the selection of the only free parameter, namely the neighborhood size, which is used for density estimation in the clustering process.

  • 41.
    Fan, Han
    et al.
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor
    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.
    Unsupervised gas discrimination in uncontrolled environments by exploiting density peaks2016In: 2016 IEEE SENSORS, Institute of Electrical and Electronics Engineers (IEEE), 2016Conference paper (Refereed)
    Abstract [en]

    Gas discrimination with Open Sampling Systems based on low-cost electro-chemical sensor arrays is of great interest in several applications, such as exploration of hazardous areas and environmental monitoring. Due to the lack of labeled training data or the high costs of obtaining them, as well as the presence of unknown interferents in the target environments, supervised learning is often not applicable and thus, unsupervised learning is an interesting alternative. In this work, we present a cluster analysis approach that can infer the number of different chemical compounds and label the measurements in a given uncontrolled environment without relying on previously acquired training data. Our approach is validated with data collected in indoor and outdoor environments by a mobile robot equipped with an array of metal oxide sensors. The results show that high classification accuracy can be achieved with a rather low sensitivity to the selection of the only functional parameter of our proposed algorithm. 

  • 42.
    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 Environment2017In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. PP, no 99, p. 1-17Article 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.

  • 43.
    Ferri, Gabriele
    et al.
    Scuola Superiore Sant'Anna, Pisa, Italy.
    Mondini, Alessio
    Scuola Superiore Sant'Anna, Pisa, Italy.
    Manzi, Alessandro
    Scuola Superiore Sant'Anna, Pisa, Italy.
    Mazzolai, Barbara
    Scuola Superiore Sant'Anna, Pisa, Italy.
    Laschi, Cecilia
    Scuola Superiore Sant'Anna, Pisa, Italy.
    Mattoli, Virgilio
    Scuola Superiore Sant'Anna, Pisa, Italy.
    Reggente, Matteo
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Lettere, Marco
    Scuola Superiore Sant'Anna, Pisa, Italy.
    Dario, Paolo.
    Scuola Superiore Sant'Anna, Pisa, Italy.
    DustCart, a Mobile Robot for Urban Environments: Experiments of Pollution Monitoring and Mapping during Autonomous Navigation in Urban Scenarios2010In: Proceedings of ICRA Workshop on Networked and Mobile Robot Olfaction in Natural, Dynamic Environments, 2010Conference paper (Refereed)
    Abstract [en]

    In the framework of DustBot European project, aimed at developing a new multi-robot system for urban hygiene management, we have developed a twowheeled robot: DustCart. DustCart aims at providing a solution to door-to-door garbage collection: the robot, called by a user, navigates autonomously to his/her house; collects the garbage from the user and discharges it in an apposite area. An additional feature of DustCart is the capability to monitor the air pollution by means of an on board Air Monitoring Module (AMM). The AMM integrates sensors to monitor several atmospheric pollutants, such as carbon monoxide (CO), particular matter (PM10), nitrogen dioxide (NO2), ozone (O3) plus temperature (T) and relative humidity (rHu). An Ambient Intelligence platform (AmI) manages the robots’ operations through a wireless connection. AmI is able to collect measurements taken by different robots and to process them to create a pollution distribution map. In this paper we describe the DustCart robot system, focusing on the AMM and on the process of creating the pollutant distribution maps. We report results of experiments of one DustCart robot moving in urban scenarios and producing gas distribution maps using the Kernel DM+V algorithm. These experiments can be considered as one of the first attempts to use robots as mobile monitoring devices that can complement the traditional fixed stations.

  • 44.
    Gonzàlez Monroy, Javier
    et al.
    University of Málaga, Málaga, Spain.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Blanco, Jose Luis
    University of Almería, Almería, spain.
    Gonzàlez Jimenez, Javier
    University of Málaga, Málaga, Spain.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Probabilistic gas quantification with MOX sensors in open sampling systems: a gaussian process approach2013In: Sensors and actuators. B, Chemical, ISSN 0925-4005, E-ISSN 1873-3077, Vol. 188, p. 298-312Article in journal (Refereed)
    Abstract [en]

    Gas quantification based on the response of an array of metal oxide (MOX) gas sensors in an Open Sampling System is a complex problem due to the highly dynamic characteristic of turbulent airflow and the slow dynamics of the MOX sensors. However, many gas related applications require to determine the gas concentration the sensors are being exposed to. Due to the chaotic nature that dominates gas dispersal, in most cases it is desirable to provide, together with an estimate of the mean concentration, an estimate of the uncertainty of the prediction. This work presents a probabilistic approach for gas quantification with an array of MOX gas sensors based on Gaussian Processes, estimating for every measurement of the sensors a posterior distribution of the concentration, from which confidence intervals can be obtained. The proposed approach has been tested with an experimental setup where an array of MOX sensors and a Photo Ionization Detector (PID), used to obtain ground truth concentration, are placed downwind with respect to the gas source. Our approach has been implemented and compared with standard gas quantification methods, demonstrating the advantages when estimating gas concentrations.

  • 45.
    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.

  • 46.
    Hernandez Bennetts, Victor
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Neumann, Patrick P.
    BAM Federal Institute for Materials Research and Testing, Berlin, Germany.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Mobile robots for localizing gas emission sources on landfill sites: is bio-inspiration the way to go?2012In: Frontiers in Neuroengineering, ISSN 1662-6443, Vol. 4, no 20, p. 1-12Article in journal (Refereed)
    Abstract [en]

    Roboticists often take inspiration from animals for designing sensors, actuators, or algorithms that control the behavior of robots. Bio-inspiration is motivated with the uncanny ability of animals to solve complex tasks like recognizing and manipulating objects, walking on uneven terrains, or navigating to the source of an odor plume. In particular the task of tracking an odor plume up to its source has nearly exclusively been addressed using biologically inspired algorithms and robots have been developed, for example, to mimic the behavior of moths, dung beetles, or lobsters. In this paper we argue that biomimetic approaches to gas source localization are of limited use, primarily because animals differ fundamentally in their sensing and actuation capabilities from state-of-the-art gas-sensitive mobile robots. To support our claim, we compare actuation and chemical sensing available to mobile robots to the corresponding capabilities of moths. We further characterize airflow and chemosensor measurements obtained with three different robot platforms (two wheeled robots and one flying micro-drone) in four prototypical environments and show that the assumption of a constant and unidirectional airflow, which is the basis of many gas source localization approaches, is usually far from being valid. This analysis should help to identify how underlying principles, which govern the gas source tracking behavior of animals, can be usefully translated into gas source localization approaches that fully take into account the capabilities of mobile robots. We also describe the requirements for a reference application, monitoring of gas emissions at landfill sites with mobile robots, and discuss an engineered gas source localization approach based on statistics as an alternative to biologically inspired algorithms.

  • 47.
    Hernandez Bennetts, Victor
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Ferrari, Silvia
    Sibley School of Mechanical and Aerospace Engineering, Cornell University, NY, USA.
    Albertson, John
    School of Civil and Environmental Engineering, Cornell University, NY, USA.
    Integrated Simulation of Gas Dispersion and Mobile Sensing Systems2015In: Workshop on Realistic, Rapid and Repeatable Robot Simulation, 2015Conference paper (Refereed)
    Abstract [en]

    Accidental or intentional releases of contaminants into the atmosphere pose risks to human health, the environment, the economy, and national security. In some cases there may be a single release from an unknown source, while in other cases there are fugitive emissions from multiple sources. The need to locate and characterize the sources efficiently - whether it be the urgent need to evacuate or the systematic need to cover broad geographical regions with limited resources - is shared among all cases. Efforts have begun to identify leaks with gas analyzers mounted on Mobile Robot Olfaction (MRO) systems, road vehicles, and networks of fixed sensors, such as may be based in urban environments. To test and compare approaches for gas-sensitive robots a truthful gas dispersion simulator is needed. In this paper, we present a unified framework to simulate gas dispersion and to evaluate mobile robotics and gas sensing technologies using ROS. This framework is also key to developing and testing optimization and planning algorithms for determining sensor placement and sensor motion, as well as for fusing and connecting the sensor measurements to the leak locations.

  • 48.
    Hernandez Bennetts, Victor
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Creating true gas concentration maps in presence of multiple heterogeneous gas sources2012In: Sensors, 2012 IEEE, Institute of Electrical and Electronics Engineers (IEEE), 2012, p. 554-557Conference paper (Refereed)
    Abstract [en]

    Gas distribution mapping is a crucial task in emission monitoring and search and rescue applications. A common assumption made by state-of-the art mapping algorithms is that only one type of gaseous substance is present in the environment. For real world applications, this assumption can become very restrictive. In this paper we present an algorithm that creates gas concentration maps in a scenario where multiple heterogeneous gas sources are present. First, using an array of metal oxide (MOX) sensors and a pattern recognition algorithm, the chemical compound is identified. Then, for each chemical compound a gas concentration map using the readings of a Photo Ionization Detector (PID) is created. The proposed approach has been validated in experiments with the sensors mounted on a mobile robot which performed a predefined trajectory in a room where two gas sources emitting respectively ethanol and 2-propanol have been placed.

  • 49.
    Hernandez Bennetts, Victor
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Khaliq, Ali Abdul
    Örebro University, School of Science and Technology.
    Pomareda Sese, Victor
    Institute for Bioengineering of Catalonia (IBEC), Barcelona, Spain.
    Trincavelli, Marco
    Örebro University, Örebro, Sweden..
    Gasbot: A Mobile Robotic Platform for Methane Leak Detection and Emission Monitoring2012In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Workshop on Robotics for Environmental Monitoring (WREM), Vilamoura, Portugal, October 7-12, 2012, 2012Conference paper (Refereed)
    Abstract [en]

    Due to its environmental, economical and safety implications, methane leak detection is a crucial task to address in the biogas production industry. In this paper, we introduce Gasbot, a robotic platform that aims to automatize methane emission monitoring in landfills and biogas production sites. The distinctive characteristic of the Gasbot platform is the use of a Tunable Laser Absorption Spectroscopy (TDLAS) sensor, along with a novel gas distribution algorithm to generate methane concentration maps of indoor and outdoor exploration areas. The Gasbot platform has been tested in two different scenarios: an underground corridor, where a pipeline leak was simulated and in a decommissioned landfill site, where an artificial methane emission source was introduced.

  • 50.
    Hernandez Bennetts, Victor Manuel
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Khaliq, Ali Abdul
    Örebro University, School of Science and Technology.
    Pomareda Sese, Victor
    Institute of Bioengineering of Catalonia, Barcelona, Spain.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Towards Real-World Gas Distribution Mapping and Leak Localization Using a Mobile Robot with 3D and Remote Gas Sensing Capabilities2013In: 2013 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), IEEE conference proceedings, 2013, p. 2335-2340Conference paper (Refereed)
    Abstract [en]

    Due to its environmental, economical and safety implications, methane leak detection is a crucial task to address in the biogas production industry. In this paper, we introduce Gasbot, a robotic platform that aims to automatize methane emission monitoring in landfills and biogas production sites. The distinctive characteristic of the Gasbot platform is the use of a Tunable Laser Absorption Spectroscopy (TDLAS) sensor. This sensor provides integral concentration measurements over the path of the laser beam. Existing gas distribution mapping algorithms can only handle local measurements obtained from traditional in-situ chemical sensors. In this paper we also describe an algorithm to generate 3D methane concentration maps from integral concentration and depth measurements. The Gasbot platform has been tested in two different scenarios: an underground corridor, where a pipeline leak was simulated and in a decommissioned landfill site, where an artificial methane emission source was introduced.

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