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  • 1.
    Almqvist, Håkan
    et al.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Magnusson, Martin
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    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, 101-128 p.Article 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.

  • 2.
    Almqvist, Håkan
    et al.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Magnusson, Martin
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Stoyanov, Todor
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Improving Point-Cloud Accuracy from a Moving Platform in Field Operations2013In: 2013 IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings, 2013, 733-738 p.Conference 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.

  • 3.
    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, Vol. 22, no 1, 64-75 p.Article 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.

  • 4.
    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, 991-1001 p.Article 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.

  • 5.
    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, 4096-4101 p.Conference 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.

  • 6.
    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, 157-165 p.Article 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.

  • 7.
    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.

  • 8.
    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, 400-416 p.Article 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).

  • 9.
    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, 662-669 p.Conference 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.

  • 10.
    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, 83-90 p.Chapter 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

  • 11.
    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, 3428-3434 p.Conference 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.

  • 12.
    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, 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.

  • 13.
    Arain, Muhammad Asif
    et al.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    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, 4275-4281 p.Conference 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.

  • 14.
    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, Vol. 15, no 3, 6845-6871 p.Article 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.

  • 15.
    Asadi, Sahar
    et al.
    Örebro University, School of Science and Technology.
    Badica, Costin
    University of Craiova.
    Comes, Tina
    Karslruhe Institute of Technology.
    Conrado, Claudine
    Thales Research and Technology, Delft.
    Evers, Vanessa
    University of Amsterdam.
    Groen, Frans
    University of Amsterdam.
    Illie, Sorin
    University of Craiova.
    Steen Jensen, Jan
    DEMA, Denmark.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Milan, Bianca
    DCMR, Delft.
    Neidhart, Thomas
    Space Applications Services, Zaventem, Belgium,.
    Nieuwenhuis, Kees
    Thales Research and Technology, Delft.
    Pashami, Sepideh
    Örebro University, School of Science and Technology.
    Pavlin, Gregor
    Thales Research and Technology, Delft.
    Pehrsson, Jan
    Prolog Development Center, Denmark.
    Pinchuk, Rani
    Space Applications and Services.
    Scafes, Mihnea
    University of Craiova.
    Schou-Jensen, Leo
    DCMR, Denmark.
    Schultmann, Frank
    Karslruhe Institute of Technology.
    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", 2011, 920-931 p.Conference 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.

  • 16.
    Asadi, Sahar
    et al.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Lilienthal, Achim
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Approaches to Time-Dependent Gas Distribution Modelling2015In: 2015 European Conference on Mobile Robots (ECMR), New York: IEEE conference proceedings , 2015, 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.

  • 17.
    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, 281-282 p.Conference 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.

  • 18.
    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.
    Plagemann, Christian
    Stanford University.
    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, 153-179 p.Chapter 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.

  • 19.
    Bennetts, Victor Hernandez
    et al.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Stoyanov, Todor
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Trincavelli, Marco
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Robot Assisted Gas Tomography - Localizing Methane Leaks in Outdoor Environments2014In: 2014 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), IEEE conference proceedings, 2014, 6362-6367 p.Conference 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.

  • 20.
    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, Örebro University, Sweden.
    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.

  • 21.
    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, 1563-1566 p.Conference 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.

  • 22.
    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, 93-98 p.Conference 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.

  • 23.
    Bunz, Elsa
    et al.
    Örebro University, Örebro, Sweden.
    Chadalavada, Ravi Teja
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Andreasson, Henrik
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Krug, Robert
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Schindler, Maike
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Lilienthal, Achim
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    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.

  • 24.
    Canelhas, Daniel R.
    et al.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Stoyanov, Todor
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    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, 1148-1155 p.Article 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.

  • 25.
    Canelhas, Daniel R.
    et al.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Stoyanov, Todor
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Improved local shape feature stability through dense model tracking2013In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2013, 3203-3209 p.Conference 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

  • 26.
    Canelhas, Daniel R.
    et al.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Stoyanov, Todor
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    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, 3671-3676 p.Conference 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

  • 27.
    Chadalavada, Ravi Teja
    et al.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Andreasson, Henrik
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Krug, Robert
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Lilienthal, Achim
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    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.

  • 28.
    Chadalavada, Ravi Teja
    et al.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Andreasson, Henrik
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Krug, Robert
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Lilienthal, Achim
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    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.

  • 29.
    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, 1-6 p.Conference 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.

  • 30.
    Cielniak, Grzegorz
    et al.
    Lincoln Univ, Sch Comp Sci, Lincoln, Lincs LN6 7TS, England.
    Duckett, Tom
    Lincoln Univ, Sch Comp Sci, Lincoln, Lincs LN6 7TS, 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, 435-443 p.Article 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.

  • 31.
    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, 3436-3441 p.Conference 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.

  • 32.
    Cielniak, Grzegorz
    et al.
    Örebro University, Department of Technology.
    Miladinovic, Mihajlo
    Hammarin, Daniel
    Göransson, Linus
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Duckett, Tom
    Örebro University, Department of Technology.
    Appearance-based tracking of persons with an omnidirectional vision sensor2003Conference 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

  • 33.
    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 0921-8830, Vol. 61, no 10, 1049-1050 p.Article in journal (Other academic)
  • 34.
    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, 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.

  • 35.
    Fan, Han
    et al.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    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. 

  • 36.
    Ferri, Gabriele
    et al.
    Scuola Superiore Sant'Anna.
    Mondini, Alessio
    Scuola Superiore Sant'Anna.
    Manzi, Alessandro
    Scuola Superiore Sant'Anna.
    Mazzolai, Barbara
    Scuola Superiore Sant'Anna.
    Laschi, Cecilia
    Scuola Superiore Sant'Anna.
    Mattoli, Virgilio
    Scuola Superiore Sant'Anna.
    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.
    Dario, Paolo.
    Scuola Superiore Sant'Anna.
    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.

  • 37.
    Gonzàlez Monroy, Javier
    et al.
    University of Málaga.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Blanco, Jose Luis
    University of Almería.
    Gonzàlez Jimenez, Javier
    University of Málaga.
    Trincavelli, Marco
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    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, 298-312 p.Article 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.

  • 38.
    Hernandez Bennetts, Victor
    et al.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Kucner, Tomasz Piotr
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Neumann, Patrick P.
    Fan, Han
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Probabilistic Air Flow Modelling Using Turbulent and Laminar Characteristics for Ground and Aerial Robots2017In: IEEE Robotics and Automation Letters, ISSN 2377-3766, Vol. 2, no 2, 1117-1123 p.Article 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.

  • 39.
    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, 1-12 p.Article 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.

  • 40.
    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.

  • 41.
    Hernandez Bennetts, Victor
    et al.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Trincavelli, Marco
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Creating true gas concentration maps in presence of multiple heterogeneous gas sources2012In: Sensors, 2012 IEEE, Institute of Electrical and Electronics Engineers (IEEE), 2012, 554-557 p.Conference 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.

  • 42.
    Hernandez Bennetts, Victor Manuel
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology. Univ Örebro, AASS Res Ctr, Örebro, Sweden.
    Khaliq, Ali Abdul
    Örebro University, School of Science and Technology. Univ Örebro, AASS Res Ctr, Örebro, Sweden.
    Pomareda Sese, Victor
    Institute of Bioengineering of Catalonia, Spain.
    Trincavelli, Marco
    Örebro University, School of Science and Technology. Univ Örebro, AASS Res Ctr, Örebro, Sweden.
    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, 2335-2340 p.Conference 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.

  • 43.
    Hernandez Bennetts, Victor
    et al.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Fan, Han
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Kucner, Tomasz Piotr
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    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, 131-136 p., 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.

  • 44.
    Hernandez Bennetts, Victor
    et al.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Pomadera Sese, Victor
    Institute of Bioengineering of Catalonia, Barcelona, Spain.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Marco, Santiago
    Signal and Information Processing for Sensing Systema, Institute for Bioengineering of Catalonia, Barcelona, Spain; Departament d’Electrònica, Universitat de Barcelona, Barcelona, Spain.
    Trincavelli, Marco
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Combining Non Selective Gas Sensors on a Mobile Robot for Identification and Mapping of Multiple Chemical Compounds2014In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 14, no 9, 17331-17352 p.Article in journal (Refereed)
    Abstract [en]

    In this paper, we address the task of gas distribution modeling in scenarios where multiple heterogeneous compounds are present. Gas distribution modeling is particularly useful in emission monitoring applications where spatial representations of the gaseous patches can be used to identify emission hot spots. In realistic environments, the presence of multiple chemicals is expected and therefore, gas discrimination has to be incorporated in the modeling process. The approach presented in this work addresses the task of gas distribution modeling by combining different non selective gas sensors. Gas discrimination is addressed with an open sampling system, composed by an array of metal oxide sensors and a probabilistic algorithm tailored to uncontrolled environments. For each of the identified compounds, the mapping algorithm generates a calibrated gas distribution model using the classification uncertainty and the concentration readings acquired with a photo ionization detector. The meta parameters of the proposed modeling algorithm are automatically learned from the data. The approach was validated with a gas sensitive robot patrolling outdoor and indoor scenarios, where two different chemicals were released simultaneously. The experimental results show that the generated multi compound maps can be used to accurately predict the location of emitting gas sources.

  • 45.
    Hernandez Bennetts, Victor
    et al.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Pomadera Sese, Victor
    Institute of Bioengineering of Catalonia, Spain.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Trincavelli, Marco
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    A Novel Approach for Gas Discrimination in Natural Environments with Open Sampling Systems2014In: Proceedings of the IEEE Sensors Conference 2014, IEEE conference proceedings, 2014, -2049 p.Conference paper (Refereed)
    Abstract [en]

    This work presents a gas discrimination approachfor Open Sampling Systems (OSS), composed of non-specificmetal oxide sensors only. In an OSS, as used on robots or insensor networks, the sensors are exposed to the dynamics of theenvironment and thus, most of the data corresponds to highlydiluted samples while high concentrations are sparse. In addition,a positive correlation between class separability and concentra-tion level can be observed. The proposed approach computes theclass posteriors by coupling the pairwise probabilities betweenthe compounds to a confidence model based on an estimation ofthe concentration. In this way a rejection posterior, analogous tothe detection limit of the human nose, is learned. Evaluation wasconducted in indoor and outdoor sites, with an OSS equippedrobot, in the presence of two gases. The results show that theproposed approach achieves a high classification performancewith a low sensitivity to the selection of meta parameters.

  • 46.
    Hernandez Bennetts, Victor
    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: an alternative approach for the detection of fugitive methane emissions2014In: Workshop on Robot Monitoring, 2014Conference paper (Refereed)
    Abstract [en]

    Methane (CH4) based combustibles, such as Natural Gas (NG) and BioGas (BG), are considered bridge fuels towards a decarbonized global energy system. NG emits less CO2 during combustion than other fossil fuels and BG can be produced from organic waste. However, at BG production sites, leaks are common and CH4 can escape through fissures in pipes and insulation layers. While by regulation BG producers shall issue monthly CH4 emission reports, measurements are sparsely collected, only at a few predefined locations. Due to the high global warming potential of CH4, efficient leakage detection systems are critical. We present a robotics approach to localize CH4 leaks. In Robot assisted Gas Tomography (RGT), a mobile robot is equipped with remote gas sensors to create gas distribution maps, which can be used to infer the location of emitting sources. Spectroscopy based remote gas sensors report integral concentrations, which means that the measurements are spatially unresolved, with neither information regarding the gas distribution over the optical path nor the length of the s beam. Thus, RGT fuses different sensing modalities, such as range sensors for robot localization and ray tracing, in order to infer plausible gas distribution models that explain the acquired integral concentration measurements.

  • 47.
    Hernandez Bennetts, Victor
    et al.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Stoyanov, Todor
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Trincavelli, Marco
    Örebro University, School of Science and Technology, Örebro University, Sweden.
    Robot assisted gas tomography: localizing methane leaks in outdoor environments2014In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings, 2014Conference 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.

  • 48.
    Hernandez Bennetts, Victor
    et al.
    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.
    Pomadera Sese, Victor
    Institute of Bioengineering of Catalonia, Spain.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Online parameter selection for gas distribution mapping2013In: Proceedings of the ISOEN conference 2013, 2013Conference paper (Refereed)
  • 49.
    Hernandez Bennetts, Victor
    et al.
    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.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Online parameter selection for gas distribution mapping2014In: Sensor Letters, ISSN 1546-198X, E-ISSN 1546-1971, Vol. 12, no 6-7, 1147-1151 p.Article in journal (Refereed)
    Abstract [en]

    The ability to produce truthful maps of the distribution of one or more gases is beneficial for applications ranging from environmental monitoring to mines and industrial plants surveillance. Realistic environments are often too complicated for applying analytical gas plume models or performing reliable CFD simulations, making data-driven statistical gas distribution models the most attractive alternative. However, statistical models for gas distribution modelling, often rely on a set of meta-parameters that need to be learned from the data through Cross Validation (CV) techniques. CV techniques are computationally expensive and therefore need to be computed offline. As a faster alternative, we propose a parameter selection method based on Virtual Leave-One-Out Cross Validation (VLOOCV) that enables online learning of meta-parameters. In particular, we consider the Kernel DM+V, one of the most well studied algorithms for statistical gas distribution mapping, which relies on a meta-parameter, the kernel bandwidth. We validate the proposed VLOOCV method on a set of indoor and outdoor experiments where a mobile robot with a Photo Ionization Detector (PID) was collecting gas measurements. The approximation provided by the proposed VLOOCV method achieves very similar results to plain Cross Validation at a fraction of the computational cost. This is an important step in the development of on-line statistical gas distribution modelling algorithms.

  • 50.
    Huhle, Benjamin
    et al.
    Universität Tübingen.
    Magnusson, Martin
    Örebro University, Department of Technology.
    Straßer, Wolfgang
    Universität Tübingen.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Registration of colored 3D point clouds with a Kernel-based extension to the normal distributions transform2008In: IEEE international conference on robotics and automation, ICRA 2008: ICRA 2008, 2008, 4025-4030 p.Conference paper (Refereed)
    Abstract [en]

    We present a new algorithm for scan registration of colored 3D point data which is an extension to the Normal Distributions Transform (NDT). The probabilistic approach of NDT is extended to a color-aware registration algorithm by modeling the point distributions as Gaussian mixture-models in color space. We discuss different point cloud registration techniques, as well as alternative variants of the proposed algorithm. Results showing improved robustness of the proposed method using real-world data acquired with a mobile robot and a time-of-flight camera are presented.

1234 1 - 50 of 182
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