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  • 51.
    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)
  • 52.
    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.

  • 53.
    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.

  • 54.
    Ishida, Hiroshi
    et al.
    Tokyo University of Agriculture and Technology, Tokyo, Japan.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Matsukura, Haruka
    Tokyo University of Agriculture and Technology, Tokyo, Japan.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Using Chemical Sensors as 'Noses' for Mobile Robots2016In: Essentials of Machine Olfaction and Taste / [ed] Takamichi Nakamoto, Singapore: John Wiley & Sons, 2016, 219-246 p.Chapter in book (Refereed)
    Abstract [en]

    Gas sensors detect the presence of gaseous chemical compounds in air. They are often used in the form of gas alarms for detecting dangerous or hazardous gases. However, a limited number of stationary gas alarms may not be always sufficient to cover a large industrial facility. Human workers having a portable gas detector in their hand needs to be sent to thoroughly check gas leaks in the areas not covered by stationary gas alarms. However, making repetitive measurements with a gas detector at a number of different locations is laborious. Moreover, the places where the gas concentration level needs to be checked are often potentially dangerous for human workers. If a portable gas detector is mounted on a mobile robot, the task of patrolling in an industrial facility for checking a gas leak can be automated. Robots are good at doing repetitive tasks, and can be sent into harsh environments.

  • 55.
    Jun, Li
    et al.
    Örebro University, Department of Technology.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Martìnez-Marìn, Tomas
    Duckett, Tom
    Örebro University, Department of Technology.
    Q-RAN: a constructive reinforcement learning approach for robot behavior learning2006In: 2006 IEEE/RSJ international conference on intelligent robots and systems, 2006, 2656-2662 p.Conference paper (Refereed)
    Abstract [en]

    This paper presents a learning system that uses Q-learning with a resource allocating network (RAN) for behavior learning in mobile robotics. The RAN is used as a function approximator, and Q-learning is used to learn the control policy in `off-policy' fashion that enables learning to be bootstrapped by a prior knowledge controller, thus speeding up the reinforcement learning. Our approach is verified on a PeopleBot robot executing a visual servoing based docking behavior in which the robot is required to reach a goal pose. Further experiments show that the RAN network can also be used for supervised learning prior to reinforcement learning in a layered architecture, thus further improving the performance of the docking behavior.

  • 56.
    Khaliq, Ali
    et al.
    Örebro University, School of Science and Technology.
    Pashami, Sepideh
    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.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Bringing Artificial Olfaction and Mobile Robotics Closer Together: An Integrated 3D Gas Dispersion Simulator in ROS2015In: Proceedings of the 16th International Symposium on Olfaction and Electronic Noses, 2015Conference paper (Refereed)
    Abstract [en]

    Despite recent achievements, the potential of gas-sensitive mobile robots cannot be realized due to the lack of research on fundamental questions. A key limitation is the difficulty to carry out evaluations against ground truth. 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 algorithms using ROS. Gas dispersion is modeled as a set of particles affected by diffusion, turbulence, advection and gravity. Wind information is integrated as time snapshots computed with any fluid dynamics computation tool. In addition, response models for devices such as Metal Oxide (MOX) sensors can be integrated in the framework.

  • 57.
    Krug, Robert
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Kragic, Danica
    Centre for Autonomous Systems, Computer Vision and Active Perception Lab, CSC, KTH Stockholm, Stockholm, Sweden.
    Bekiroglu, Yasemin
    School of Mechanical Engineering, University of Birmingham, Birmingham, United Kingdom.
    Analytic Grasp Success Prediction with Tactile Feedback2016In: 2016 IEEE International Conference on Robotics and Automation, ICRA 2016, New York, USA: IEEE , 2016, 165-171 p.Conference paper (Refereed)
    Abstract [en]

    Predicting grasp success is useful for avoiding failures in many robotic applications. Based on reasoning in wrench space, we address the question of how well analytic grasp success prediction works if tactile feedback is incorporated. Tactile information can alleviate contact placement uncertainties and facilitates contact modeling. We introduce a wrench-based classifier and evaluate it on a large set of real grasps. The key finding of this work is that exploiting tactile information allows wrench-based reasoning to perform on a level with existing methods based on learning or simulation. Different from these methods, the suggested approach has no need for training data, requires little modeling effort and is computationally efficient. Furthermore, our method affords task generalization by considering the capabilities of the grasping device and expected disturbance forces/moments in a physically meaningful way.

  • 58.
    Krug, Robert
    et al.
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Bonilla, Manuel
    Interdepart. Research Center “E. Piaggio”; University of Pisa, Pisa, Italy.
    Tincani, Vinicio
    Interdepart. Research Center “E. Piaggio”; University of Pisa, Pisa, Italy.
    Vaskevicius, Narunas
    Interdepart. Research Center “E. Piaggio”; University of Pisa, Pisa, Italy.
    Fantoni, Gualtiero
    Interdepart. Research Center “E. Piaggio”; University of Pisa, Pisa, Italy.
    Birk, Andreas
    Interdepart. Research Center “E. Piaggio”; University of Pisa, Pisa, Italy.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Bicchi, Antonio
    Interdepart. Research Center “E. Piaggio”; University of Pisa, Pisa, Italy.
    Improving Grasp Robustness via In-Hand Manipulation with Active Surfaces2014In: Workshop on Autonomous Grasping and Manipulation: An Open Challenge, 2014Conference paper (Refereed)
  • 59.
    Krug, Robert
    et al.
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Bonilla, Manuel
    Interdepart. Research Center “E. Piaggio”, University of Pisa, Pisa, Italy.
    Tincani, Vinicio
    Interdepart. Research Center “E. Piaggio”, University of Pisa, Pisa, Italy.
    Vaskevicius, Narunas
    Robotics Group, School of Engineering and Science, Jacobs University Bremen, Bremen, Germany.
    Fantoni, Gualtiero
    Interdepart. Research Center “E. Piaggio”, University of Pisa, Pisa, Italy.
    Birk, Andreas
    Robotics Group, School of Engineering and Science, Jacobs University Bremen, Bremen, Germany.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Bicchi, Antonio
    Interdepart. Research Center “E. Piaggio”, University of Pisa, Pisa, Italy.
    Velvet fingers: grasp planning and execution for an underactuated gripper with active surfaces2014In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings, 2014, 3669-3675 p.Conference paper (Refereed)
    Abstract [en]

    In this work we tackle the problem of planning grasps for an underactuated gripper which enable it to retrieve target objects from a cluttered environment. Furthermore,we investigate how additional manipulation capabilities of the gripping device, provided by active surfaces on the inside of the fingers, can lead to performance improvement in the grasp execution process. To this end, we employ a simple strategy, in which the target object is ‘pulled-in’ towards the palm during grasping which results in firm enveloping grasps. We show the effectiveness of the suggested methods by means of experiments conducted in a real-world scenario.

  • 60.
    Krug, Robert
    et al.
    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.
    Grasp Envelopes for Constraint-based Robot Motion Planning and Control2015In: Robotics: Science and Systems Conference: Workshop on Bridging the Gap between Data-driven and Analytical Physics-based Grasping and Manipulation, 2015Conference paper (Refereed)
    Abstract [en]

    We suggest a grasp represen-tation in form of a set of enveloping spatial constraints. Our representation transforms the grasp synthesisproblem (i. e., the question of where to position the graspingdevice) from finding a suitable discrete manipulator wrist pose to finding a suitable pose manifold. Also the correspondingmotion planning and execution problem is relaxed – insteadof transitioning the wrist to a discrete pose, it is enough tomove it anywhere within the grasp envelope which allows toexploit kinematic redundancy.

  • 61.
    Krug, Robert
    et al.
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Tincani, Vinicio
    Interdepart. Research Center “E. Piaggio”; University of Pisa, Pisa, Italy.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Mosberger, Rafael
    Örebro University, School of Science and Technology.
    Fantoni, Gualtiero
    Interdepart. Research Center “E. Piaggio”; University of Pisa, Pisa, Italy.
    Bicchi, Antonio
    Interdepart. Research Center “E. Piaggio”; University of Pisa, Pisa, Italy.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    On Using Optimization-based Control instead of Path-Planning for Robot Grasp Motion Generation2015In: IEEE International Conference on Robotics and Automation (ICRA) - Workshop on Robotic Hands, Grasping, and Manipulation, 2015Conference paper (Refereed)
  • 62.
    Krug, Robert
    et al.
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Tincani, Vinicio
    University of Pisa, Pisa, Italy.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Mosberger, Rafael
    Örebro University, School of Science and Technology.
    Fantoni, Gualtiero
    University of Pisa, Pisa, Italy.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    The Next Step in Robot Commissioning: Autonomous Picking and Palletizing2016In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 1, no 1, 546-553 p.Article in journal (Refereed)
    Abstract [en]

    So far, autonomous order picking (commissioning) systems have not been able to meet the stringent demands regarding speed, safety, and accuracy of real-world warehouse automation, resulting in reliance on human workers. In this letter, we target the next step in autonomous robot commissioning: automatizing the currently manual order picking procedure. To this end, we investigate the use case of autonomous picking and palletizing with a dedicated research platform and discuss lessons learned during testing in simplified warehouse settings. The main theoretical contribution is a novel grasp representation scheme which allows for redundancy in the gripper pose placement. This redundancy is exploited by a local, prioritized kinematic controller which generates reactive manipulator motions on-the-fly. We validated our grasping approach by means of a large set of experiments, which yielded an average grasp acquisition time of 23.5 s at a success rate of 94.7%. Our system is able to autonomously carry out simple order picking tasks in a humansafe manner, and as such serves as an initial step toward future commercial-scale in-house logistics automation solutions.

  • 63.
    Kucner, Tomasz
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Tell me about dynamics!: Mapping velocity fields from sparse samples with Semi-Wrapped Gaussian Mixture Models2016In: Robotics: Science and Systems Conference (RSS 2016), 2016Conference paper (Refereed)
    Abstract [en]

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

  • 64.
    Kucner, Tomasz Piotr
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Where am I?: An NDT-based prior for MCL2015In: 2015 European Conference on Mobile Robots (ECMR), New York: IEEE conference proceedings , 2015Conference paper (Refereed)
    Abstract [en]

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

  • 65.
    Kucner, Tomasz Piotr
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor Manuel
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Enabling Flow Awareness for Mobile Robots in Partially Observable Environments2017In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 2, no 2, 1093-1100 p.Article in journal (Refereed)
    Abstract [en]

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

  • 66.
    Kucner, Tomasz
    et al.
    Örebro University, School of Science and Technology.
    Sarinen, Jari
    Aalto Iniversity.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Conditional transition maps: learning motion patterns in dynamic environments2013In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, 2013, 1196-1201 p.Conference paper (Refereed)
    Abstract [en]

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

  • 67.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Improved gas source localization with a mobile robot by learning analytical gas dispersal models from statistical gas distribution maps using evolutionary algorithms2011In: Intelligent Systems for Machine Olfaction: Tools and Methodologies / [ed] Evor L. Hines, Mark S. Leeson, IGI Global, 2011, 249-276 p.Chapter in book (Refereed)
    Abstract [en]

    The method presented in this chapter computes an estimate of the location of a single gas sourcefrom a set of localised gas sensor measurements. The estimation process consists of three steps.First, a statistical model of the time-averaged gas distribution is estimated in the form of a two-dimensional grid map. In order to compute the gas distribution grid map the Kernel DM algorithm isapplied, which carries out spatial integration by convolving localised sensor readings and modelling theinformation content of the point measurements with a Gaussian kernel. The statistical gas distributiongrid map averages out the transitory effects of turbulence and converges to a representation of thetime-averaged spatial distribution of a target gas. The second step is to learn the parameters ofan analytical model of average gas distribution. Learning is achieved by nonlinear least squaresfitting of the analytical model to the statistical gas distribution map using Evolution Strategies (ES),which are a special type of Evolutionary Algorithms (EA). This step provides an analysis of thestatistical gas distribution map regarding the airflow conditions and an alternative estimate of thegas source location, i.e. the location predicted by the analytical model in addition to the location ofthe maximum in the statistical gas distribution map. In the third step, an improved estimate of thegas source position can then be derived by considering the maximum in the statistical gas distributionmap, the best fit as well as the corresponding fitness value. Different methods to select the mosttruthful estimate are introduced and a comparison regarding their accuracy is presented, based on atotal of 34 hours of gas distribution mapping experiments with a mobile robot. This chapter is anextended version of a paper by the authors (Lilienthal et al. [2005]).

  • 68.
    Lilienthal, Achim J.
    et al.
    Örebro University, School of Science and Technology.
    Asadi, Sahar
    Örebro University, School of Science and Technology.
    Reggente, Matteo
    Örebro University, School of Science and Technology.
    Estimating predictive variance for statistical gas distribution modelling2009In: Olfaction and electronic nose: proceedings / [ed] Matteo Pardo, Giorgio Sberveglieri, Melville, USA: American Institute of Physics (AIP), 2009, 65-68 p.Conference paper (Refereed)
    Abstract [en]

    Recent publications in statistical gas distribution modelling have proposed algorithms that model mean and variance of a distribution. This paper argues that estimating the predictive concentration variance entails not only a gradual improvement but is rather a significant step to advance the field. This is, first, since the models much better fit the particular structure of gas distributions, which exhibit strong fluctuations with considerable spatial variations as a result of the intermittent character of gas dispersal. Second, because estimating the predictive variance allows to evaluate the model quality in terms of the data likelihood. This offers a solution to the problem of ground truth evaluation, which has always been a critical issue for gas distribution modelling. It also enables solid comparisons of different modelling approaches, and provides the means to learn meta parameters of the model, to determine when the model should be updated or re-initialised, or to suggest new measurement locations based on the current model. We also point out directions of related ongoing or potential future research work.

  • 69.
    Lilienthal, Achim J.
    et al.
    Örebro University, Department of Technology.
    Duckett, Tom
    Örebro University, Department of Technology.
    A stereo electronic nose for a mobile inspection robot2003Conference paper (Refereed)
    Abstract [en]

    This paper describes the design of a gas-sensitive system that is suitable for use on a mobile robot ("mobile nose"). The stereo architecture comprises two equivalent sets of gas sensors mounted inside separated ventilated tubes (or "nostrils"). To characterise the dynamic response, the whole system is modelled as a first-order sensor. The corresponding parameters, including the response and recovery time, can be obtained by fitting this model to the values recorded during a simple experiment described in this paper. Our experiments confirmed the suitability of the applied model and permitted a quantitative comparison of different set-ups. It is shown that using suction fans lowers the recovery time of the metal oxide gas sensors by a factor of two, while a solid separation between the tubes (a "septum") is necessary to maintain the sensitivity of the mobile nose to concentration gradients.

  • 70.
    Lilienthal, Achim J.
    et al.
    Örebro University, Department of Technology.
    Duckett, Tom
    Örebro University, Department of Technology.
    An absolute positioning system for 100 euros2003Conference paper (Refereed)
    Abstract [en]

    This paper describes an absolute positioning system, which provides accurate and reliable measurements using low-cost equipment that is easy to set up. The system uses a number of fixed web-cameras to track a distinctly coloured object. In order to calculate the (x,y) position of this object, estimates calculated by triangulation from each combination of two cameras are combined, resulting in centimeter-level accuracy. Example applications, including tracking of mobile robots and persons, are described. An extended set-up is also introduced, which allows determination of the heading of a two coloured object from single images

  • 71.
    Lilienthal, Achim J.
    et al.
    Örebro University, Department of Technology.
    Duckett, Tom
    Örebro University, Department of Technology.
    Approaches to gas source tracing and declaration by pure chemo-tropotaxis2003In: Proceedings of the autonome mobile systeme: AMS, 2003, 161-171 p.Conference paper (Refereed)
    Abstract [en]

    This paper addresses the problem of localising a static gas source in an uncontrolled indoor environment by a mobile robot. In contrast to previous works, especially the condition of an environment that is not artificially ventilated to produce a strong unidirectional airflow is considered. Here, the propagation of the analyte molecules is dominated by turbulence and convection flow rather than diffusion, thus creating a patchy distribution of spatially distributed eddies. Positive and negative tropotaxis, based on the spatial concentration gradient measured by a pair of electrochemical gas sensor arrays, were investigated. Both strategies were implemented utilising a direct sensor-motor coupling (a Braitenberg vehicle) and were shown to be useful to accomplish the gas source localisation task. As a possible solution to the problem of gas source declaration (the task of determining with certainty that the gas source has been found), an indirect localisation strategy based on exploration and concentration peak avoidance is suggested. Here, a gas source is located by exploiting the fact that local concentration maxima occur more frequently near the gas source compared to distant regions

  • 72.
    Lilienthal, Achim J.
    et al.
    Örebro University, Department of Technology.
    Duckett, Tom
    Örebro University, Department of Technology.
    Building gas concentration gridmaps with a mobile robot2004In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 48, no 1, 3-16 p.Article in journal (Refereed)
    Abstract [en]

    This paper addresses the problem of mapping the structure of a gas distribution by creating concentration gridmaps from the data collected by a mobile robot equipped with gas sensors. By contrast to metric gridmaps extracted from sonar or laser range scans, a single measurement from a gas sensor provides information about a comparatively small area. To overcome this problem, a mapping technique is introduced that uses a Gaussian weighting function to model the decreasing likelihood that a particular reading represents the true concentration with respect to the distance from the point of measurement. This method is evaluated in terms of its suitability regarding the slow response and recovery of the gas sensors, and experimental comparisons of different exploration strategies are presented. The stability of the mapped structures and the capability to use concentration gridmaps to locate a gas source are also discussed

  • 73.
    Lilienthal, Achim J.
    et al.
    Örebro University, Department of Technology.
    Duckett, Tom
    Örebro University, Department of Technology.
    Creating gas concentration gridmaps with a mobile robot2003In: Proceedings of 2003 IEEE/RSJ international conference on intelligent robots and systems: IROS 2003, 2003, 118-123 p.Conference paper (Refereed)
    Abstract [en]

    This paper addresses the problem of mapping the features of a gas distribution by creating concentration gridmaps from the data collected by a mobile robot equipped with an electronic nose. By contrast to metric gridmaps extracted from sonar or laser range scans, a single measurement of the electronic nose provides information about a comparatively small area. To overcome this problem, a mapping technique is introduced that uses a Gaussian density function to model the decreasing likelihood that a particular reading represents the true concentration with respect to the distance from the point of measurement. This method is evaluated in terms of its suitability regarding the slow response and recovery of the gas sensors. The stability of the mapped features and the capability to use concentration gridmaps to locate a gas source are also discussed.

  • 74.
    Lilienthal, Achim J.
    et al.
    Örebro University, Department of Technology.
    Duckett, Tom
    Örebro University, Department of Technology.
    Experimental analysis of gas-sensitive Braitenberg vehicles2004In: Advanced Robotics, ISSN 0169-1864, E-ISSN 1568-5535, Vol. 18, no 8, 817-834 p.Article in journal (Refereed)
    Abstract [en]

    This article addresses the problem of localising a static gas source in an indoor environment by a mobile robot. In contrast to previous works, the environment is not artificially ventilated to produce a strong unidirectional airflow. Here, the dominant transport mechanisms of gas molecules are turbulence and convection flow rather than diffusion, which results in a patchy, chaotically fluctuating gas distribution. Two Braitenberg-type strategies (positive and negative tropotaxis) based on the instantaneously measured spatial concentration gradient were investigated. Both strategies were shown to be of potential use for gas source localisation. As a possible solution to the problem of gas source declaration (the task of determining with certainty that the gas source has been found), an indirect localisation strategy based on exploration and concentration peak avoidance is suggested. Here, a gas source is located by exploiting the fact that local concentration maxima occur more frequently near the gas source compared to distant regions

  • 75.
    Lilienthal, Achim J.
    et al.
    Örebro University, Department of Technology.
    Duckett, Tom
    Örebro University, Department of Technology.
    Experimental analysis of smelling Braitenberg vehicles2003Conference paper (Refereed)
    Abstract [en]

    This paper addresses the problem of localisation of a static odour source in an unstructured indoor environment by a mobile robot using electrochemical gas sensors. In particular, reactive localisation strategies based on the instantaneously measured spatial concentration gradient are considered. In contrast to previous works, the environment is not artificially ventilated to produce a strong constant airflow, and thus the distribution of the odour molecules is dominated by turbulence. An experimental set-up is presented that enables different strategies for odour source localisation to be compared directly in a precisely measured experiment. Two alternative strategies that utilise a direct sensor-motor coupling are then investigated and a detailed numerical analysis of the results is presented, including tests of statistical significance. Both tested strategies proved to be useful to accomplish the localisation task. As a possible solution to the problem of detecting that the odour source - which is usually not corresponding to the global concentration maximum - was found, one of the tested strategies exploits the fact that local concentration maxima occur more frequently near the odour source compared to distant regions

  • 76.
    Lilienthal, Achim J.
    et al.
    Örebro University, Department of Technology.
    Duckett, Tom
    Örebro University, Department of Technology.
    Gas source localisation by constructing concentration gridmaps with a mobile robot2003In: Proceedings of the European conference on mobile robots: ECMR 2003, 2003, 159-164 p.Conference paper (Refereed)
    Abstract [en]

    This paper addresses the problem of mapping the features of a gas distribution by creating concentration gridmaps with a mobile robot equipped with a gas-sensitive system ("mobile nose"). By contrast to metric gridmaps extracted from sonar or laser range scans, a gas sensor measurement provides information about a comparatively small area. To overcome this problem, a mapping technique is introduced that uses a Gaussian density function to model the decreasing likelihood that a particular reading represents the true concentration with respect to the distance from the point of measurement. The structure of the mapped features is discussed with respect to the parameters of the applied density function, the evolution of the gas distribution over time, and the capability to locate a gas source.

  • 77.
    Lilienthal, Achim J.
    et al.
    Örebro University, School of Science and Technology.
    Duckett, TomLincoln School of Computer Science, University of Lincoln.
    Proceedings of the 5th European Conference on Mobile Robots ECMR 2011: September 7-9, 2011, Örebro, Sweden2011Conference proceedings (editor) (Other academic)
  • 78.
    Lilienthal, Achim J.
    et al.
    Örebro University, Department of Technology.
    Duckett, Tom
    Örebro University, Department of Technology.
    Ishida, Hiroshi
    Tokyo Univ. of Agriculture & Technology.
    Werner, Felix
    University of Tubingen.
    Indicators of gas source proximity using metal oxide sensors in a turbulent environment2006In: The First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006, BioRob 2006, 2006, 733-738 p.Conference paper (Refereed)
    Abstract [en]

    This paper addresses the problem of estimating proximity to a gas source using concentration measurements. In particular, we consider the problem of gas source declaration by a mobile robot equipped with metal oxide sensors in a turbulent indoor environment. While previous work has shown that machine learning classifiers can be trained to detect close proximity to a gas source, it is difficult to interpret the learned models. This paper investigates possible underlying indicators of gas source proximity, comparing three different statistics derived from the sensor measurements of the robot. A correlation analysis of 1056 trials showed that response variance (measured as standard deviation) was a better indicator than average response. An improved result was obtained when the standard deviation was normalized to the average response for each trial, a strategy that also reduces calibration problems.

  • 79.
    Lilienthal, Achim J.
    et al.
    Örebro University, Department of Technology.
    Loutfi, Amy
    Örebro University, Department of Technology.
    Blanco, Jose Luis
    University of Malaga.
    Galindo, Cipriano
    University of Malaga.
    Gonzalez, Javier
    University of Malaga.
    A Rao-Blackwellisation approach to GDM-SLAM: integrating SLAM and gas distribution mapping2007In: Proceedings of the European Conference on Mobile Robots, ECMR (2007), 2007, 126-131 p.Conference paper (Refereed)
    Abstract [en]

    In this paper we consider the problem of creating a two dimensional spatial representation of gas distribution with a mobile robot. In contrast to previous approaches to the problem of gas distribution mapping (GDM) we do not assume that the robot has perfect knowledge about its position. Instead we develop a probabilistic framework for simultaneous localisation and occupancy and gas distribution mapping (GDM/SLAM) that allows to account for the uncertainty about the robot’s position when computing the gas distribution map. Considering the peculiarities of gas sensing in real-world environments, we show which dependencies in the posterior over occupancy and gas distribution maps can be neglected under certain practical assumptions. We develop a Rao-Blackwellised particle filter formulation of the GDM/SLAM problem that allows to plug in any algorithm to compute a gas distribution map from a sequence of gas sensor measurements and a known trajectory. In this paper we use the Kernel Based Gas Distribution Mapping (Kernel- GDM) method. As a first step towards outdoor gas distribution mapping we present results obtained in a large, uncontrolled, partly open indoor environment

  • 80.
    Lilienthal, Achim J.
    et al.
    Örebro University, Department of Technology.
    Loutfi, Amy
    Örebro University, Department of Technology.
    Blanco, Jose Luis
    Galindo, Cipriano
    Gonzalez, Javier
    Integrating SLAM into gas distribution mapping2007In: Proceedings of ICRA Workshop on Robotic Olfaction: Towards Real Applications. ICRA 2007 - Rome Italy, 2007, 21-28 p.Conference paper (Refereed)
    Abstract [en]

    In this paper we consider the problem of creating a spatial representation of a gas distribution in an environment using a mobile robot equipped with gas sensors. The gas distribution mapping method used models the information content of a given measurement about the average concentration distribution with respect to the point of measurement. In this paper, we present an extension which can consider the uncertainty about the robot’s position in the gas distribution mapping. We present a preliminary result where a mobile robot equipped with gas sensors creates a map of a large indoor environment, using both spatial and olfactory information.

  • 81.
    Lilienthal, Achim J.
    et al.
    Örebro University, Department of Technology.
    Loutfi, Amy
    Örebro University, Department of Technology.
    Duckett, Tom
    University of Lincoln.
    Airborne chemical sensing with mobile robots2006In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 6, no 11, 1616-1678 p.Article in journal (Refereed)
    Abstract [en]

    Airborne chemical sensing with mobile robots has been an active research area since the beginning of the 1990s. This article presents a review of research work in this field, including gas distribution mapping, trail guidance, and the different subtasks of gas source localisation. Due to the difficulty of modelling gas distribution in a real world environment with currently available simulation techniques, we focus largely on experimental work and do not consider publications that are purely based on simulations.

  • 82.
    Lilienthal, Achim J.
    et al.
    Örebro University, School of Science and Technology.
    Reggente, Matteo
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Blanco, Jose Luis
    Dept. of System Engineering and Automation, University of Malaga.
    Gonzalez, Javier
    Örebro University, School of Science and Technology.
    A statistical approach to gas distribution modelling with mobile robots: the Kernel DM+V algorithm2009In: IEEE/RSJ international conference on intelligent robots and systems: IROS 2009, IEEE conference proceedings, 2009, 570-576 p.Conference paper (Refereed)
    Abstract [en]

    Gas distribution modelling constitutes an ideal application area for mobile robots, which – as intelligent mobile gas sensors – offer several advantages compared to stationary sensor networks. In this paper we propose the Kernel DM+V algorithm to learn a statistical 2-d gas distribution model from a sequence of localized gas sensor measurements. The algorithm does not make strong assumptions about the sensing locations and can thus be applied on a mobile robot that is not primarily used for gas distribution monitoring, and also in the case of stationary measurements. Kernel DM+V treats distribution modelling as a density estimation problem. In contrast to most previous approaches, it models the variance in addition to the distribution mean. Estimating the predictive variance entails a significant improvement for gas distribution modelling since it allows to evaluate the model quality in terms of the data likelihood. This offers a solution to the problem of ground truth evaluation, which has always been a critical issue for gas distribution modelling. Estimating the predictive variance also provides the means to learn meta parameters and to suggest new measurement locations based on the current model. We derive the Kernel DM+V algorithm and present a method for learning the hyper-parameters. Based on real world data collected with a mobile robot we demonstrate the consistency of the obtained maps and present a quantitative comparison, in terms of the data likelihood of unseen samples, with an alternative approach that estimates the predictive variance.

  • 83.
    Lilienthal, Achim J.
    et al.
    Örebro University, Department of Technology.
    Reiman, Denis
    University of Tübingen.
    Zell, Andreas
    University of Tubingen.
    Gas source tracing with a mobile robot using an adapted moth strategy2003In: Proceedings of autonome mobile systeme: AMS, 2003, 150-160 p.Conference paper (Refereed)
    Abstract [en]

    As a sub-task of the general gas source localisation problem, gas source tracing is supposed to guide a gas-sensitive mobile system towards a source by using the cues determined from the gas distribution sensed along a driven path. This paper reports on an investigation of a biologically inspired gas source tracing strategy. Similar to the behaviour of the silkworm moth Bombyx mori, the implemented behaviour consists of a fixed motion pattern that realises a local search, and a mechanism that (re-)starts this motion pattern if an increased gas concentration is sensed. While the moth uses the local airflow direction to orient the motion pattern, this is not possible for a mobile robot due to the detection limits of currently available anemometers. Thus, an alternative method was implemented that uses an asymmetric motion pattern, which is biased towards the side where higher gas sensor readings were obtained. The adaptated strategy was implemented and tested on an experimental platform. This paper describes the strategy and evaluates its performance in terms of the ability to drive the robot towards a gas source and to keep it within close proximity of the source

  • 84.
    Lilienthal, Achim J.
    et al.
    Örebro University, Department of Technology.
    Streichert, Felix
    University of Tubingen.
    Zell, Andreas
    University of Tubingen.
    Model-based shape analysis of gas concentration gridmaps for improved gas source localisation2005In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, ICRA 2005, 2005, 3564-3569 p.Conference paper (Refereed)
    Abstract [en]

    This work addresses the capability to use concentration gridmaps to locate a static gas source. In previous works it was found that depending on the shape of the mapped gas distribution (corresponding to different airflow conditions) the gas source location can be sometimes approximated with high accuracy by the maximum in the concentration map while this is not possible in other cases. This paper introduces a method to distinguish both cases by analysing the shape of the obtained concentration map in terms of a model of the time-averaged gas distribution known from physics. The parameters of the model that approximates the concentration map most closely are determined by nonlinear least squares fitting using evolution strategies (ES). The best fit also provides a better estimate of the gas source position in situations where the concentration maximum estimate fails. Different methods to select the most truthful estimate are introduced in this work and a comparison regarding their accuracy is presented, based on a total of 34h of concentration mapping experiments.

    Keywords: Gas concentration mapping, gas source localisation

  • 85.
    Lilienthal, Achim J.
    et al.
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    It's always smelly around here! Modeling the Spatial Distribution of Gas Detection Events with BASED Grid Maps2013In: Proceedings of the 15th International Symposium on Olfaction and Electronic Nose (ISOEN 2013), 2013Conference paper (Refereed)
    Abstract [en]

    In this paper we introduce a novel gas distribution mapping algorithm, Bayesian Spatial Event Distribution (BASED), that, instead of modeling the spatial distribution of the gas concentration, models the spatial distribution of events of detection and non-detection of a target gas. The proposed algorithm is based on the Bayesian inference framework and models the likelihood of events at a certain location with a Bernoulli distribution. In order to avoid overfitting a Bayesian approach is used with a beta distribution prior for the parameter u that governs the Bernoulli distribution. In this way, the posterior distribution maintains the same form of the prior, i.e. will be a beta distribution, enabling a simple approach for sequential learning. To learn a field of beta distributions, we discretize the inspection area into a grid map and extrapolate from local measurements using Gaussian kernels. We demonstrate the proposed algorithm for different sensors mounted on a mobile robot and show how qualitatively similar maps are obtained from very different gas sensors.

  • 86.
    Lilienthal, Achim J.
    et al.
    Örebro University, Department of Technology.
    Ulmer, Holger
    University of Tübingen.
    Fröhlich, Holger
    University of Tubingen.
    Stützle, Andreas
    University of Tubingen.
    Werner, Felix
    University of Tubingen.
    Zell, Andreas
    University of Tubingen.
    Gas source declaration with a mobile robot2004In: 2004 IEEE international conference on robotics and automation, ICRA '04: Proceedings, 2004, 1430-1435 p.Conference paper (Refereed)
    Abstract [en]

    As a sub-task of the general gas source localisation problem, gas source declaration is the process of determining the certainty that a source is in the immediate vicinity. Due to the turbulent character of gas transport in a natural indoor environment, it is not sufficient to search for instantaneous concentration maxima, in order to solve this task. Therefore, this paper introduces a method to classify whether an object is a gas source or not from a series of concentration measurements, recorded while the robot performs a rotation manoeuvre in front of a possible source. For three different gas source positions, a total of 288 declaration experiments were carried out at different robot-to-source distances. Based on these readings, two machine learning techniques (ANN, SVM) were evaluated in terms of their classification performance. With learning parameters that were optimised by grid search, a maximal hit rate of approximately 87.5% could be obtained using a support vector machine

  • 87.
    Lilienthal, Achim J.
    et al.
    Örebro University, Department of Technology.
    Ulmer, Holger
    University of Tübingen.
    Fröhlich, Holger
    University of Tubingen.
    Werner, Felix
    University of Tubingen.
    Zell, Andreas
    University of Tubingen.
    Learning to detect proximity to a gas source with a mobile robot2004In: Proceedings of the 2004 IEEE/RSJ international conference on intelligent robots and systems, 2004 (IROS 2004): IROS - Sendai, Japan, 2004, 2004, 1444-1449 p.Conference paper (Refereed)
    Abstract [en]

    As a sub-task of the general gas source localisation problem, gas source declaration is the process of determining the certainty that a source is in the immediate vicinity. Due to the turbulent character of gas transport in a natural indoor environment, it is not sufficient to search for instantaneous concentration maxima, in order to solve this task. Therefore, this paper introduces a method to classify whether an object is a gas source from a series of concentration measurements, recorded while the robot performs a rotation manoeuvre in front of a possible source. For three different gas source positions, a total of 1056 declaration experiments were carried out at different robot-to-source distances. Based on these readings, support vector machines (SVM) with optimised learning parameters were trained and the cross-validation classification performance was evaluated. The results demonstrate the feasibility of the approach to detect proximity to a gas source using only gas sensors. The paper presents also an analysis of the classification rate depending on the desired declaration accuracy, and a comparison with the classification rate that can be achieved by selecting an optimal threshold value regarding the mean sensor signal

  • 88.
    Lilienthal, Achim J.
    et al.
    Örebro University, Department of Technology.
    Wandel, Michael R.
    University of Tubingen.
    Weimar, Udo
    University of Tubingen.
    Zell, Andreas
    University of Tubingen.
    Detection and localization of an odour source2002In: Robotik 2002: Leistungsstand - Anwendungen - Visionen - Trends, 2002, 689-694 p.Conference paper (Refereed)
    Abstract [en]

    This paper presents studies concerning the use of an electronic nose on an autonomous mobile robot. In particular experiments were introduced in which a mobile robot generates two dimensional concentration maps of a known target gas in an unventilated room. It was shown that these concentration maps are clearly related to the position of the odour source. Moreover our results show that if accurate localization of the odour source itself is desired one has to consider weak air currents which usually occur even in closed unventilated rooms (often caused by convection).

  • 89.
    Lilienthal, Achim J.
    et al.
    Örebro University, Department of Technology.
    Wandel, Michael
    Weimar, Udo
    Zell, Andreas
    Ein autonomer mobiler Roboter mit elektronischer Nase2000In: Autonome Mobile Systeme: AMS - 16, 2000, 201-210 p.Conference paper (Refereed)
  • 90.
    Lilienthal, Achim J.
    et al.
    Örebro University, Department of Technology.
    Wandel, Michael
    University of Tubingen.
    Weimar, Udo
    University of Tubingen.
    Zell, Andreas
    University of Tubingen.
    Sensing odour sources in indoor environments without a constant airflow by a mobile robot2001In: IEEE international conference on robotics and automation: ICRA 2001, 2001, 4005-4010 p.Conference paper (Refereed)
    Abstract [en]

    This paper describes the assembly of a mobile odour sensing system and investigates its practical operation in an indoor environment without a constant airflow. Lacking a constant airflow leads to a problem which cannot be neglected in real world applications. The response of the metal oxide gas sensors used is dominated by air turbulence rather than concentration differences. We show that this problem can be overcome by driving the robot with a constant speed, thus adding an extra constant airflow relative to the gas sensors location. If the robot's speed is not too low the system described proved to be well suited to detect even weak odour sources. Since driving with constant speed is an indispensable condition to perform the basic tasks of a mobile odour sensing system, a new localization strategy is proposed, which takes this into account.

  • 91.
    Lilienthal, Achim J.
    et al.
    Örebro University, Department of Technology.
    Zell, Andreas
    University of Tubingen.
    Wandel, Michael R.
    University of Tubingen.
    Weimar, Udo
    University of Tubingen.
    Experiences using gas sensors on an autonomous mobile robot2001In: Proceedings of EUROBOT 2001, 4th European workshop on advanced mobile robots, 2001, 4005-4010 p.Conference paper (Refereed)
    Abstract [en]

    This paper reports on experiences concerning the deployment of gas sensors on an autonomous mobile robot. It particularly addresses the suitability of the developed system to localize a distant odour source. First experiments were undertaken in which the robot was ordered to move along different weakly ventilated corridors, while keeping track of its center (framing a '1D' scenario). The measured sensor values show evident peaks that roughly indicate the location of the odour source, if the robot moves with a speed not too low. In this case the system proved to be well suited to detect even weak odour sources. Otherwise the observed course of the received values show many peaks hardly correlated with the location of the odour source. Several investigations were performed to clear up this behaviour but it is still not possible to make concluding statements about the reasons. Finally the setup to perform experiments in a '2D' scenario is described and concerning results of first investigations are presented. It was shown that the utilized system is also capable of detecting a distant odour source in a 2D environment and that the somewhat harder localization task has to account for some weak airflow even in closed, unventilated rooms.

  • 92.
    Loutfi, Amy
    et al.
    Örebro University, School of Science and Technology.
    Coradeschi, Silvia
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Gonzalez, Javier
    Dept. of System Engineering and Automation, University of Malaga.
    Gas Distribution Mapping of Multiple Odour Sources using a Mobile Robot2009In: Robotica (Cambridge. Print), ISSN 0263-5747, E-ISSN 1469-8668, Vol. 27, no 2, 311-319 p.Article in journal (Refereed)
    Abstract [en]

    Mobile olfactory robots can be used in a number of relevant application areas where a better understanding of agas distribution is needed, such as environmental monitoring and safety and security related fields. In this paper wepresent a method to integrate the classification of odours together with gas distribution mapping. The resulting odourmap is then correlated with the spatial information collected from a laser range scanner to form a combined map.Experiments are performed using a mobile robot in large and unmodified indoor and outdoor environments. Multipleodour sources are used and are identified using only transient information from the gas sensor response. The resultingmulti level map can be used as a intuitive representation of the collected odour data for a human user.

  • 93.
    Magnusson, Martin
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Nüchter, A.
    Jacobs University Bremen, Bremen, Germany.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Appearance-based loop detection from 3D laser data using the normal distributions transform2009In: IEEE International Conference on Robotics and Automation 2009 (ICRA '09), IEEE conference proceedings, 2009, 23-28 p.Conference paper (Other academic)
    Abstract [en]

    We propose a new approach to appearance based loop detection from metric 3D maps, exploiting the NDT surface representation. Locations are described with feature histograms based on surface orientation and smoothness, and loop closure can be detected by matching feature histograms. We also present a quantitative performance evaluation using two realworld data sets, showing that the proposed method works well in different environments.© 2009 IEEE.

  • 94.
    Magnusson, Martin
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Nüchter, Andreas
    Jacobs University Bremen.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Automatic appearance-based loop detection from three-dimensional laser data using the normal distributions transform2009In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 26, no 11-12, 892-914 p.Article in journal (Refereed)
    Abstract [en]

    We propose a new approach to appearance-based loop detection for mobile robots, usingthree-dimensional (3D) laser scans. Loop detection is an important problem in the simultaneouslocalization and mapping (SLAM) domain, and, because it can be seen as theproblem of recognizing previously visited places, it is an example of the data associationproblem. Without a flat-floor assumption, two-dimensional laser-based approaches arebound to fail in many cases. Two of the problems with 3D approaches that we address inthis paper are how to handle the greatly increased amount of data and how to efficientlyobtain invariance to 3D rotations.We present a compact representation of 3D point cloudsthat is still discriminative enough to detect loop closures without false positives (i.e.,detecting loop closure where there is none). A low false-positive rate is very important becausewrong data association could have disastrous consequences in a SLAM algorithm.Our approach uses only the appearance of 3D point clouds to detect loops and requires nopose information. We exploit the normal distributions transform surface representationto create feature histograms based on surface orientation and smoothness. The surfaceshape histograms compress the input data by two to three orders of magnitude. Becauseof the high compression rate, the histograms can be matched efficiently to compare theappearance of two scans. Rotation invariance is achieved by aligning scans with respectto dominant surface orientations. We also propose to use expectation maximization to fit a gamma mixture model to the output similarity measures in order to automatically determinethe threshold that separates scans at loop closures from nonoverlapping ones.Wediscuss the problem of determining ground truth in the context of loop detection and thedifficulties in comparing the results of the few available methods based on range information.Furthermore, we present quantitative performance evaluations using three realworlddata sets, one of which is highly self-similar, showing that the proposed methodachieves high recall rates (percentage of correctly identified loop closures) at low falsepositiverates in environments with different characteristics.

  • 95.
    Magnusson, Martin
    et al.
    Örebro University, School of Science and Technology.
    Kucner, Tomasz
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Quantitative Evaluation of Coarse-To-Fine Loading Strategies for Material Rehandling2015In: Proceedings of the IEEE International Conference on Automation Science and Engineering (CASE), New York: IEEE conference proceedings , 2015, 450-455 p.Conference paper (Refereed)
    Abstract [en]

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

  • 96.
    Magnusson, Martin
    et al.
    Örebro University, School of Science and Technology.
    Kucner, Tomasz Piotr
    Örebro University, School of Science and Technology.
    Gholami Shahbandi, Saeed
    IS lab, Halmstad University, Halmstad, Sweden.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Semi-Supervised 3D Place Categorisation by Descriptor Clustering2017In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017, 620-625 p.Conference paper (Refereed)
    Abstract [en]

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

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

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

  • 97.
    Magnusson, Martin
    et al.
    Örebro University, Department of Technology.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Duckett, Tom
    University of Lincoln.
    Scan registration for autonomous mining vehicles using 3D-NDT2007In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 24, no 10, 803-827 p.Article in journal (Refereed)
    Abstract [en]

    Scan registration is an essential sub-task when building maps based on range finder data from mobile robots. The problem is to deduce how the robot has moved between consecutive scans, based on the shape of overlapping portions of the scans. This paper presents a new algorithm for registration of 3D data. The algorithm is a generalisation and improvement of the normal distributions transform (NDT) for 2D data developed by Biber and Straßer, which allows for accurate registration using a memory-efficient representation of the scan surface. A detailed quantitative and qualitative comparison of the new algorithm with the 3D version of the popular ICP (iterative closest point) algorithm is presented. Results with actual mine data, some of which were collected with a new prototype 3D laser scanner, show that the presented algorithm is faster and slightly more reliable than the standard ICP algorithm for 3D registration, while using a more memory-efficient scan surface representation.

  • 98.
    Magnusson, Martin
    et al.
    Örebro University, Department of Technology.
    Nüchter, Andreas
    Institute of Computer Science, University of Osnabrück.
    Lörken, Christopher
    Institute of Computer Science, University of Osnabrück.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Hertzberg, Joachim
    Institute of Computer Science, University of Osnabrück.
    3D mapping the Kvarntorp mine: a rield experiment for evaluation of 3D scan matching algorithms2008Conference paper (Other academic)
    Abstract [en]

    This paper presents the results of a field experiment in the Kvarntorp mine outside of Örebro in Sweden. 3D mapping of the underground mine has been used to compare two scan matching methods, namely the iterative closest point algorithm (ICP) and the normal distributions transform (NDT). The experimental results of the algorithm are compared in terms of robustness and speed. For robustness we measure how reliably 3D scans are registered with respect to different starting pose estimates. Speed is evaluated running the authors’ best implementations on the same hardware. This leads to an unbiased comparison. In these experiments, NDT was shown to converge form a larger range of initial pose estimates than ICP, and to perform faster.

  • 99.
    Magnusson, Martin
    et al.
    Örebro University, School of Science and Technology.
    Nüchter, Andreas
    Jacobs University Bremen, Bremen, Germany; Knowledge Systems Research Group of the Institute of Computer Science, University of Osnabrück, Germany.
    Lörken, Christopher
    Institute of Computer Science, University of Osnabrück, Germany.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Hertzberg, Joachim
    Institute of Computer Science, University of Osnabrück, Germany.
    Evaluation of 3D registration reliability and speed: a comparison of ICP and NDT2009In: Proceedings of the 2009 IEEE international conference on Robotics and Automation, ICRA'09, IEEE conference proceedings, 2009, 2263-2268 p.Conference paper (Refereed)
    Abstract [en]

    To advance robotic science it is important to perform experiments that can be replicated by other researchers to compare different methods. However, these comparisons tend to be biased, since re-implementations of reference methods often lack thoroughness and do not include the hands-on experience obtained during the original development process. This paper presents a thorough comparison of 3D scan registration algorithms based on a 3D mapping field experiment, carried out by two research groups that are leading in the field of 3D robotic mapping. The iterative closest points algorithm (ICP) is compared to the normal distributions transform (NDT). We also present an improved version of NDT with a substantially larger valley of convergence than previously published versions.

  • 100.
    Mielle, Malcolm
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Using emergency maps to add not yet explored places into SLAM2017Conference paper (Other academic)
    Abstract [en]

    While using robots in search and rescue missions would help ensure the safety of first responders, a key issue is the time needed by the robot to operate. Even though SLAM is faster and faster, it might still be too slow to enable the use of robots in critical situations. One way to speed up operation time is to use prior information.

    We aim at integrating emergency-maps into SLAM to complete the SLAM map with information about not yet explored part of the environment. By integrating prior information, we can speed up exploration time or provide valuable prior information for navigation, for example, in case of sensor blackout/failure. However, while extensively used by firemen in their operations, emergency maps are not easy to integrate in SLAM since they are often not up to date or with non consistent scales.

    The main challenge we are tackling is in dealing with the imperfect scale of the rough emergency maps and integrate it with the online SLAM map in addition to challenges due to incorrect matches between these two types of map. We developed a formulation of graph-based SLAM incorporating information from an emergency map into SLAM, and propose a novel optimization process adapted to this formulation.

    We extract corners from the emergency map and the SLAM map, in between which we find correspondences using a distance measure. We then build a graph representation associating information from the emergency map and the SLAM map. Corners in the emergency map, corners in the robot map, and robot poses are added as nodes in the graph, while odometry, corner observations, walls in the emergency map, and corner associations are added as edges. To conserve the topology of the emergency map, but correct its possible errors in scale, edges representing the emergency map's walls are given a covariance so that they are easy to extend or shrink but hard to rotate. Correspondences between corners represent a zero transformation for the optimization to match them as close as possible. The graph optimization is done by using a combination robust kernels. We first use the Huber kernel, to converge toward a good solution, followed by Dynamic Covariance Scaling, to handle the remaining errors.

    We demonstrate our system in an office environment. We run the SLAM online during the exploration. Using the map enhanced by information from the emergency map, the robot was able to plan the shortest path toward a place it has not yet explored. This capability can be a real asset in complex buildings where exploration can take up a long time. It can also reduce exploration time by avoiding exploration of dead-ends, or search of specific places since the robot knows where it is in the emergency map.

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