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  • 101.
    Mojtahedzadeh, Rasoul
    et al.
    Örebro University, School of Science and Technology. Univ Örebro, Ctr Appl Autonomous Sensor Syst AASS, Örebro, Sweden.
    Stoyanov, Todor
    Örebro University, School of Science and Technology. Univ Örebro, Ctr Appl Autonomous Sensor Syst AASS, Örebro, Sweden.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology. Univ Örebro, Ctr Appl Autonomous Sensor Syst AASS, Örebro, Sweden.
    Application Based 3D Sensor Evaluation: A Case Study in 3D Object Pose Estimation for Automated Unloading of Containers2013In: Proceedings of the European Conference on Mobile Robots (ECMR), IEEE conference proceedings, 2013, 313-318 p.Conference paper (Other academic)
    Abstract [en]

    A fundamental task in the design process of a complex system that requires 3D visual perception is the choice of suitable 3D range sensors. Identifying the utility of 3D range sensors in an industrial application solely based on an evaluation of their distance accuracy and the noise level may lead to an inappropriate selection. To assess the actual effect on the performance of the system as a whole requires a more involved analysis. In this paper, we examine the problem of selecting a set of 3D range sensors when designing autonomous systems for specific industrial applications in a holistic manner. As an instance of this problem we present a case study with an experimental evaluation of the utility of four 3D range sensors for object pose estimation in the process of automation of unloading containers.

  • 102.
    Monroy, Javier
    et al.
    Instituto de Investigación Biomedica de Malaga (IBIMA), Universidad de Malaga, Malaga, Spain.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Fan, Han
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Gonzales-Jimenez, Javier
    Instituto de Investigación Biomedica de Malaga (IBIMA), Universidad de Malaga, Malaga, Spain.
    GADEN: A 3D Gas Dispersion Simulator for Mobile Robot Olfaction in Realistic Environments2017In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 7, 1479Article in journal (Refereed)
    Abstract [en]

    This work presents a simulation framework developed under the widely used Robot Operating System (ROS) to enable the validation of robotics systems and gas sensing algorithms under realistic environments. The framework is rooted in the principles of computational fluid dynamics and filament dispersion theory, modeling wind flow and gas dispersion in 3D real-world scenarios (i.e., accounting for walls, furniture, etc.). Moreover, it integrates the simulation of different environmental sensors, such as metal oxide gas sensors, photo ionization detectors, or anemometers. We illustrate the potential and applicability of the proposed tool by presenting a simulation case in a complex and realistic office-like environment where gas leaks of different chemicals occur simultaneously. Furthermore, we accomplish quantitative and qualitative validation by comparing our simulated results against real-world data recorded inside a wind tunnel where methane was released under different wind flow profiles. Based on these results, we conclude that our simulation framework can provide a good approximation to real world measurements when advective airflows are present in the environment.

  • 103.
    Mosberger, Rafael
    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.
    A customized vision system for tracking humans wearing reflective safety clothing from industrial vehicles and machinery2014In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 14, no 10, 17952-17980 p.Article in journal (Refereed)
    Abstract [en]

    This article presents a novel approach for vision-based detection and tracking of humans wearing high-visibility clothing with retro-reflective markers. Addressing industrial applications where heavy vehicles operate in the vicinity of humans, we deploy a customized stereo camera setup with active illumination that allows for efficient detection of the reflective patterns created by the worker's safety garments. After segmenting reflective objects from the image background, the interest regions are described with local image feature descriptors and classified in order to discriminate safety garments from other reflective objects in the scene. In a final step, the trajectories of the detected humans are estimated in 3D space relative to the camera. We evaluate our tracking system in two industrial real-world work environments on several challenging video sequences. The experimental results indicate accurate tracking performance and good robustness towards partial occlusions, body pose variation, and a wide range of different illumination conditions.

  • 104.
    Mosberger, Rafael
    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.
    Multi-human Tracking using High-visibility Clothing for Industrial Safety2013In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2013, 638-644 p.Conference paper (Refereed)
    Abstract [en]

    We propose and evaluate a system for detecting and tracking multiple humans wearing high-visibility clothing from vehicles operating in industrial work environments. We use a customized stereo camera setup equipped with IR flash and IR filter to detect the reflective material on the worker's garments and estimate their trajectories in 3D space. An evaluation in two distinct industrial environments with different degrees of complexity demonstrates the approach to be robust and accurate for tracking workers in arbitrary body poses, under occlusion, and under a wide range of different illumination settings.

  • 105.
    Mosberger, Rafael
    et al.
    Örebro University, School of Science and Technology.
    Leibe, Bastian
    Aachen University, Aachen, Germany.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Multi-band Hough Forests for detecting humans with Reflective Safety Clothing from mobile machinery2015In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), IEEE Computer Society, 2015, 697-703 p.Conference paper (Refereed)
    Abstract [en]

    We address the problem of human detection from heavy mobile machinery and robotic equipment operating at industrial working sites. Exploiting the fact that workers are typically obliged to wear high-visibility clothing with reflective markers, we propose a new recognition algorithm that specifically incorporates the highly discriminative features of the safety garments in the detection process. Termed Multi-band Hough Forest, our detector fuses the input from active near-infrared (NIR) and RGB color vision to learn a human appearance model that not only allows us to detect and localize industrial workers, but also to estimate their body orientation. We further propose an efficient pipeline for automated generation of training data with high-quality body part annotations that are used in training to increase detector performance. We report a thorough experimental evaluation on challenging image sequences from a real-world production environment, where persons appear in a variety of upright and non-upright body positions.

  • 106.
    Mosberger, Rafael
    et al.
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Inferring human body posture information from reflective patterns of protective work garments2016In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2016, 4131-4136 p.Conference paper (Refereed)
    Abstract [en]

    We address the problem of extracting human body posture labels, upper body orientation and the spatial location of individual body parts from near-infrared (NIR) images depicting patterns of retro-reflective markers. The analyzed patterns originate from the observation of humans equipped with protective high-visibility garments that represent common safety equipment in the industrial sector. Exploiting the shape of the observed reflectors we adopt shape matching based on the chamfer distance and infer one of seven discrete body posture labels as well as the approximate upper body orientation with respect to the camera. We then proceed to analyze the NIR images on a pixel scale and estimate a figure-ground segmentation together with human body part labels using classification of densely extracted local image patches. Our results indicate a body posture classification accuracy of 80% and figure-ground segmentations with 87% accuracy.

  • 107.
    Neumann, Patrick
    et al.
    BAM, Berlin, Germany.
    Asadi, Sahar
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Bartholmai, Matthias
    BAM, Berlin, Germany.
    Monitoring of CCS areas using micro unmanned aerial vehicles (MUAVs)2013In: Energy Procedia, ISSN 1876-6102, E-ISSN 1876-6102, Vol. 37, 4182-4190 p.Article in journal (Refereed)
    Abstract [en]

    Carbon capture & storage (CCS) is one of the most promis ing technologies for greenhouse gas (GHG) management.However, an unsolved issue of CCS is the development of appropriate long-term monitoring systems for leakdetection of the stored CO2. To complement already existing monitoring infrastructure for CO2 storage areas, and toincrease the granularity of gas concentration measurements, a quickly deployab le, mobile measurement device isneeded. In this paper, we present an autonomous gas-sensitive micro-drone, which can be used to monitor GHGemissions, more specifically, CO2. Two different measurement strategies are proposed to address this task. First, theuse of predefined sensing trajectories is evaluated for the task of gas distribution mapping using the micro-drone.Alternatively, we present an adaptive strategy, which suggests sampling points based on an artific ial potential field(APF). The results of real-world experiments demonstrate the feas ibility of using gas-sensitive micro-drones for GHG monitoring missions. Thus, we suggest a multi-layered surveillance system for CO2 storage areas.

  • 108. Neumann, Patrick
    et al.
    Asadi, Sahar
    Örebro University, School of Science and Technology.
    Schiller, Jochen H.
    Institute of Computer Science, Freie Universität Berlin.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Bartholmai, Matthias
    An artificial potential field based sampling strategy for a gas-sensitive micro-drone2011Conference paper (Refereed)
    Abstract [en]

    This paper presents a sampling strategy for mobile gas sensors. Sampling points are selected using a modified artificial potential field (APF) approach, which balances multiple criteria to direct sensor measurements towards locations of high mean concentration, high concentration variance and areas for which the uncertainty about the gas distribution model is still large. By selecting in each step the most often suggested close-by measurement location, the proposed approach introduces a locality constraint that allows planning suitable paths for mobile gas sensors. Initial results in simulation and in real-world experiments witha gas-sensitive micro-drone demonstrate the suitability of the proposed sampling strategy for gas distribution mapping and its use for gas source localization.

  • 109.
    Neumann, Patrick
    et al.
    BAM Federal Institute for Materials Research and Testing, Berlin, Germany.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Bartholmai, Matthias
    BAM Federal Institute for Materials Research and Testing, Berlin, Germany.
    Schiller, Jochen H.
    Institute of Computer Science, FU University, Berlin, Germany.
    Gas source localization with a micro-drone using bio-inspired and particle filter-based algorithms2013In: Advanced Robotics, ISSN 0169-1864, E-ISSN 1568-5535, ISSN 0169-1864, Vol. 27, no 9, 725-738 p.Article in journal (Refereed)
    Abstract [en]

    Gas source localization (GSL) with mobile robots is a challenging task due to the unpredictable nature of gas dispersion,the limitations of the currents sensing technologies, and the mobility constraints of ground-based robots. This work proposesan integral solution for the GSL task, including source declaration. We present a novel pseudo-gradient-basedplume tracking algorithm and a particle filter-based source declaration approach, and apply it on a gas-sensitivemicro-drone. We compare the performance of the proposed system in simulations and real-world experiments againsttwo commonly used tracking algorithms adapted for aerial exploration missions.

  • 110.
    Neumann, Patrick P.
    et al.
    BAM Federal Institute for Materials Research and Testing, Berlin, Germany.
    Asadi, Sahar
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Bartholmai, Matthias
    Sensors and Measurement Systems Working Group, BAM Federal Institute for Materials Research and Testing, Berlin, Germany.
    Schiller, Jochen H.
    Computer Systems and Telematics Working Group, Institute of Computer Science, Freie Universität, Berlin, Germany.
    Autonomous gas-sensitive microdrone wind vector estimation and gas distribution mapping2012In: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223X, Vol. 19, no 1, 50-61 p.Article in journal (Refereed)
    Abstract [en]

    This article presents the development and validation of an autonomous, gas sensitive microdrone that is capable of estimating the wind vector in real time using only the onboard control unit of the microdrone and performing gas distribution mapping (DM). Two different sampling approaches are suggested to address this problem. On the one hand, a predefined trajectory is used to explore the target area with the microdrone in a real-world gas DM experiment. As an alternative sampling approach, we introduce an adaptive strategy that suggests next sampling points based on an artificial potential field (APF). Initial results in real-world experiments demonstrate the capability of the proposed adaptive sampling strategy for gas DM and its use for gas source localization.

  • 111.
    Neumann, Patrick P.
    et al.
    BAM Federal Institute for Materials Research and Testing, Berlin, Germany.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Bartholmai, Matthias
    BAM Federal Institute for Materials Research and Testing, Berlin, Germany.
    From Insects to Micro Air Vehicles: A Comparison of Reactive Plume Tracking Strategies2016In: Intelligent Autonomous Systems 13, Springer, 2016, 1533-1548 p.Conference paper (Refereed)
    Abstract [en]

    Insect behavior is a common source of inspiration for roboticists and computer scientists when designing gas-sensitive mobile robots. More specifically, tracking airborne odor plumes, and localization of distant gas sources are abilities that suit practical applications such as leak localization and emission monitoring. Gas sensing with mobile robots has been mostly addressed with ground-based platforms and under simplified conditions and thus, there exist a significant gap between the outstanding insect abilities and state-of-the-art robotics systems. As a step toward practical applications, we evaluated the performance of three biologically inspired plume tracking algorithms. The evaluation is carried out not only with computer simulations, but also with real-world experiments in which, a quadrocopter-based micro Unmanned Aerial Vehicle autonomously follows a methane trail toward the emitting source. Compared to ground robots, micro UAVs bring several advantages such as their superior steering capabilities and fewer mobility restrictions in complex terrains. The experimental evaluation shows that, under certain environmental conditions, insect like behavior in gas-sensitive UAVs is feasible in real-world environments.

  • 112.
    Neumann, Patrick P.
    et al.
    BAM Federal Institute for Materials Research and Testing, Berlin, Germany.
    Schnürmacher, Michael
    BAM Federal Institute for Materials Research and Testing, Berlin, Germany.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Bartholmai, Matthias
    BAM Federal Institute for Materials Research and Testing, Berlin, Germany.
    Schiller, Jochen
    BAM Federal Institute for Materials Research and Testing, Berlin, Germany.
    A Probabilistic Gas Patch Path Prediction Approach for Airborne Gas Source Localization in Non-Uniform Wind FieldsIn: Sensor Letters, ISSN 1546-198XArticle in journal (Refereed)
    Abstract [en]

    In this paper, we show that a micro unmanned aerial vehicle (UAV) equipped with commercially available gas sensors can address environmental monitoring and gas source localization (GSL) tasks. To account for the challenges of gas sensing under real-world conditions, we present a probabilistic approach to GSL that is based on a particle filter (PF). Simulation and real-world experiments demonstrate the suitability of this algorithm for micro UAV platforms.

  • 113.
    Neumann, Patrick P.
    et al.
    BAM Federal Institute for Materials Research and Testing, Berlin.
    Schnürmacher, Michael
    Institute of Computer Science, FU University, Berlin, Germany.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Bartholmai, Matthias
    BAM Federal Institute for Materials Research and Testing, Berlin.
    Schiller, Jochen H.
    Institute of Computer Science, FU University, Berlin, Germany.
    A Probabilistic Gas Patch Path Prediction Approach for Airborne Gas Source Localization in Non-Uniform Wind Fields2014In: Sensor Letters, ISSN 1546-198X, Vol. 12, no 6-7, 1113-1118 p.Article in journal (Refereed)
    Abstract [en]

    In this paper, we show that a micro unmanned aerial vehicle (UAV) equipped with commercially available gas sensors can addressenvironmental monitoring and gas source localization (GSL) tasks. To account for the challenges of gas sensing under real-world conditions,we present a probabilistic approach to GSL that is based on a particle filter (PF). Simulation and real-world experiments demonstrate thesuitability of this algorithm for micro UAV platforms.

  • 114. Neumann, Patrick
    et al.
    Schnürmacher, Michael
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Bartholmai, Matthias
    Schiller, Jochen
    A Probabilistic Gas Patch Prediction Approach for Airborne Gas Source Localization in Non-Uniform Wind Fields2013In: Proceedings of the 15th ISOEN, 2013Conference paper (Refereed)
  • 115.
    Palm, Rainer
    et al.
    Örebro University, School of Science and Technology.
    Bouguerra, Abdelbaki
    Örebro University, School of Science and Technology.
    Abdullah, Muhammad
    The university of Faisalabad, Pakistan.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Navigation in Human-Robot and Robot-Robot Interaction using Optimization Methods2016In: SMC 2016: 2016 IEEE International Conference on Systems, Man, and Cybernetics, IEEE, 2016, 4489-4494 p.Conference paper (Refereed)
    Abstract [en]

    Human-robot interaction and robot-robot interaction and cooperation in shared spatial areas is a challenging field of research regarding safety, stability and performance. In this paper the collision avoidance between human and robot by extrapolation of human intentions and a suitable optimization of tracking velocities is discussed. Furthermore for robot-robot interactions in a shared area traffic rules and artificial force potential fields and their optimization by market-based approach are applied for obstacle avoidance. For testing and verification, the navigation strategy is implemented and tested in simulation of more realistic vehicles. Extensive simulation experiments are performed to examine the improvement of the traditional potential field (PF) method by the MBO strategy.

  • 116.
    Palm, Rainer
    et al.
    Örebro University, School of Science and Technology.
    Chadalavada, Ravi
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Fuzzy Modeling and Control for Intention Recognition in Human-Robot Systems2016In: Proceedings of the 8th International Joint Conference on Computational Intelligence (IJCCI 2016), Setúbal, Portugal: SciTePress, 2016, Vol. 2, 67-74 p.Conference paper (Refereed)
    Abstract [en]

    The recognition of human intentions from trajectories in the framework of human-robot interaction is a challenging field of research. In this paper some control problems of the human-robot interaction and their intentions to compete or cooperate in shared work spaces are addressed and the time schedule of the information flow is discussed. The expected human movements relative to the robot are summarized in a so-called "compass dial" from which fuzzy control rules for the robot's reactions are derived. To avoid collisions between robot and human very early the computation of collision times at predicted human-robot intersections is discussed and a switching controller for collision avoidance is proposed. In the context of the recognition of human intentions to move to certain goals, pedestrian tracks are modeled by fuzzy clustering, lanes preferred by human agents are identified, and the identification of degrees of membership of a pedestrian track to specific lanes are discussed. Computations based on simulated and experimental data show the applicability of the methods presented.

  • 117.
    Palm, Rainer
    et al.
    Örebro University, School of Science and Technology.
    Chadalavada, Ravi
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Recognition of Human-Robot Motion Intentions by Trajectory Observation2016In: 2016 9th International Conference on Human System Interactions, HSI 2016: Proceedings, New York: Institute of Electrical and Electronics Engineers (IEEE), 2016, 229-235 p.Conference paper (Refereed)
    Abstract [en]

    The intention of humans and autonomous robots to interact in shared spatial areas is a challenging field of research regarding human safety, system stability and performance of the system's behavior. In this paper the intention recognition between human and robot from the control point of view are addressed and the time schedule of the exchanged signals is discussed. After a description of the kinematic and geometric relations between human and robot a so-called 'compass dial' with the relative velocities is presented from which suitable fuzzy control rules are derived. The computation of the collision times at intersections and possible avoidance strategies are further discussed. Computations based on simulated and experimental data show the applicability of the methods presented.

  • 118.
    Palmieri, Luigi
    et al.
    University of Freiburg, Computer Science Department, Germany.
    Kucner, Tomasz
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Arras, Kai
    Bosch Corporate Research, Stuttgart, Germany.
    Kinodynamic Motion Planning on Gaussian Mixture Fields2017In: IEEE International Conference on Robotics and Automation (ICRA 2017), 2017Conference paper (Refereed)
    Abstract [en]

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

  • 119.
    Pashami, Sepideh
    et al.
    Örebro University, School of Science and Technology.
    Asadi, Sahar
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Integration of OpenFOAM Flow Simulation and Filament-Based Gas Propagation Models for Gas Dispersion Simulation2010Conference paper (Refereed)
    Abstract [en]

    In this paper, we present a gas dispersal simulation package which integrates OpenFOAM flow simulation and a filament-based gas propagation model to simulate gas dispersion for compressible flows with a realistic turbulence model. Gas dispersal simulation can be useful for many applications. In this paper, we focus on the evaluation of statistical gas distribution models. Simulated data offer several advantages for this purpose, including the availability of ground truth information, repetition of experiments with the exact same constraints and that intricate issue which come with using real gas sensors can be avoided.Apart from simulation results obtained in a simulated wind tunnel (designed to be equivalent to its real-world counterpart), we present initial results with time-independent and time-dependent statistical modelling approaches applied to simulated and real-world data.

  • 120.
    Pashami, Sepideh
    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.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    rTREFEX: Reweighting norms for detecting changes in the response of MOX gas sensors2014In: Sensor Letters, ISSN 1546-198X, E-ISSN 1546-1971, Vol. 12, no 6/7, 1123-1127 p.Article in journal (Refereed)
    Abstract [en]

     The detection of changes in the response of metal oxide (MOX) gas sensors deployed in an open sampling system is a hard problem. It is relevant for applications such as gas leak detection in mines or large-scale pollution monitoring where it is impractical to continuously store or transfer sensor readings and reliable calibration is hard to achieve. Under these circumstances, it is desirable to detect points in the signal where a change indicates a significant event, e.g. the presence of gas or a sudden change of concentration. The key idea behind the proposed change detection approach is that a change in the emission modality of a gas source appears locally as an exponential function in the response of MOX sensors due to their long response and recovery times. The algorithm proposed in this paper, rTREFEX, is an extension of the previously proposed TREFEX algorithm. rTREFEX interprets the sensor response by fitting piecewise exponential functions with different time constants for the response and recovery phase. The number of exponentials, which has to be kept as low as possible, is determined automatically using an iterative approach that solves a sequence of convex optimization problems based on l1-norm. The algorithm is evaluated with an experimental setup where a gas source changes in intensity, compound, and mixture ratio, and the gas source is delivered to the sensors exploiting natural advection and turbulence mechanisms. rTREFEX is compared against the previously proposed TREFEX, which already proved superior to other algorithms.

  • 121.
    Pashami, Sepideh
    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.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    TREFEX: trend estimation and change detection in the response of mox gas sensors2013In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 13, no 6, 7323-7344 p.Article in journal (Refereed)
    Abstract [en]

    Many applications of metal oxide gas sensors can benefit from reliable algorithmsto detect significant changes in the sensor response. Significant changes indicate a changein the emission modality of a distant gas source and occur due to a sudden change ofconcentration or exposure to a different compound. As a consequence of turbulent gastransport and the relatively slow response and recovery times of metal oxide sensors,their response in open sampling configuration exhibits strong fluctuations that interferewith the changes of interest. In this paper we introduce TREFEX, a novel change pointdetection algorithm, especially designed for metal oxide gas sensors in an open samplingsystem. TREFEX models the response of MOX sensors as a piecewise exponentialsignal and considers the junctions between consecutive exponentials as change points. Weformulate non-linear trend filtering and change point detection as a parameter-free convexoptimization problem for single sensors and sensor arrays. We evaluate the performanceof the TREFEX algorithm experimentally for different metal oxide sensors and severalgas emission profiles. A comparison with the previously proposed GLR method shows aclearly superior performance of the TREFEX algorithm both in detection performance andin estimating the change time.

  • 122.
    Pashami, Sepideh
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    A trend filtering approach for change point detection in MOX gas sensors2013Conference paper (Refereed)
    Abstract [en]

    Detecting changes in the response of metal oxide (MOX) gas sensors deployed in an open sampling system is a hard problem. It is relevant for applicationssuch as gas leak detection in coal mines[1],[2] or large scale pollution monitoring [3],[4] where it is unpractical to continuously store or transfer sensor readings and reliable calibration is hard to achieve. Under these circumstances it is desirable to detect points in the signal where a change indicates a significant event, e.g. the presence of gas or a sudden change of concentration. The key idea behind the proposed change detection approach isthat a change in the emission modality of a gas source appears locally as an exponential function in the response of MOX sensors due to their long response and recovery times. The proposed method interprets the sensor responseby fitting piecewise exponential functions with different time constants for the response and recovery phase. The number of exponentials is determined automatically using an approximate method based on the L1-norm. This asymmetric exponential trend filtering problem is formulated as a convex optimization problem, which is particularly advantageous from the computational point of view. The algorithm is evaluated with an experimental setup where a gas source changes in intensity, compound, and mixture ratio, and it is compared against the previously proposed Generalized Likelihood Ratio (GLR) based algorithm [6].

  • 123.
    Pashami, Sepideh
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Change detection in an array of MOX sensors2012Conference paper (Refereed)
    Abstract [en]

    In this article we present an algorithm for online detection of change points in the response of an array of metal oxide (MOX) gas sensors deployed in an open sampling system.True change points occur due to changes in the emission modality of the gas source. The main challenge for change point detection in an open sampling system is the chaotic nature of gas dispersion, which causes fluctuations in the sensor response that are not related to changes in the gas source. These fluctuations should not be considered change points in the sensor response. The presented algorithm is derived from the well known Generalized Likelihood Ratio algorithm and it is used both on the output of a single sensor as well on the output of two or more sensors on the array. The algorithm is evaluated with an experimental setup where a gas source changes in intensity, compound, or mixture ratio. The performance measures considered are the detection rate, the number of false alarms and the delay of detection.

  • 124.
    Pashami, Sepideh
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Detecting changes of a distant gas source with an array of MOX gas sensors2012In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 12, no 12, 16404-16419 p.Article in journal (Refereed)
    Abstract [en]

    We address the problem of detecting changes in the activity of a distant gas source from the response of an array of metal oxide (MOX) gas sensors deployed in an open sampling system. The main challenge is the turbulent nature of gas dispersion and the response dynamics of the sensors. We propose a change point detection approach and evaluate it on individual gas sensors in an experimental setup where a gas source changes in intensity, compound, or mixture ratio. We also introduce an efficient sensor selection algorithm and evaluate the change point detection approach with the selected sensor array subsets.

  • 125.
    Persson, Martin
    et al.
    Örebro University, Department of Technology.
    Duckett, Tom
    University of Lincoln, Uk.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping2007In: Proceedings of the IROS Workshop "From Sensors to Human Spatial Concepts", 2007, 17-24 p.Conference paper (Refereed)
    Abstract [en]

    This paper investigates the use of semantic information to link ground-level occupancy maps and aerial images. A ground-level semantic map is obtained by a mobile robot equipped with an omnidirectional camera, differential GPS and a laser range finder. The mobile robot uses a virtual sensor for building detection (based on omnidirectional images) to compute the ground-level semantic map, which indicates the probability of the cells being occupied by the wall of a building. These wall estimates from a ground perspective are then matched with edges detected in an aerial image. The result is used to direct a region- and boundary-based segmentation algorithm for building detection in the aerial image. This approach addresses two difficulties simultaneously: 1) the range limitation of mobile robot sensors and 2) the difficulty of detecting buildings in monocular aerial images. With the suggested method building outlines can be detected faster than the mobile robot can explore the area by itself, giving the robot an ability to "see" around corners. At the same time, the approach can compensate for the absence of elevation data in segmentation of aerial images. Our experiments demonstrate that ground-level semantic information (wall estimates) allows to focus the segmentation of the aerial image to find buildings and produce a ground-level semantic map that covers a larger area than can be built using the onboard sensors.

  • 126.
    Persson, Martin
    et al.
    Örebro University, School of Science and Technology.
    Duckett, Tom
    Department of Computing and Informatics, University of Lincoln.
    Lilienthal, Achim J.
    Örebro University, Department of Natural Sciences.
    Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping2008In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 0921-8830, Vol. 56, no 6, 483-492 p.Article in journal (Refereed)
    Abstract [en]

    This work investigates the use of semantic information to link ground level occupancy maps and aerial images. A ground level semantic map, which shows open ground and indicates the probability of cells being occupied by walls of buildings, is obtained by a mobile robot equipped with an omni-directional camera, GPS and a laser range finder. This semantic information is used for local and global segmentation of an aerial image. The result is a map where the semantic information has been extended beyond the range of the robot sensors and predicts where the mobile robot can find buildings and potentially driveable ground.

  • 127.
    Persson, Martin
    et al.
    Örebro University, Department of Technology.
    Duckett, Tom
    University of Lincoln.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Improved mapping and image segmentation by using semantic information to link aerial images and ground-level information2007In: Proceedings of the IEEE international conference on advanced robotics: ICAR 2007, 2007, 924-929 p.Conference paper (Refereed)
    Abstract [en]

    This paper investigates the use of semantic information to link ground-level occupancy maps and aerial images. In the suggested approach a ground-level semantic map is obtained by a mobile robot equipped with an omnidirectional camera, differential GPS and a laser range finder. The mobile robot uses a virtual sensor for building detection (based on omnidirectional images) to compute the ground-level semantic map, which indicates the probability of the cells being occupied by the wall of a building. These wall estimates from a ground perspective are then matched with edges detected in an aerial image. The result is used to direct a region- and boundary-based segmentation algorithm for building detection in the aerial image. This approach addresses two difficulties simultaneously: 1) the range limitation of mobile robot sensors and 2) the difficulty of detecting buildings in monocular aerial images. With the suggested method building outlines can be detected faster than the mobile robot can explore the area by itself, giving the robot an ability to "see" around corners. At the same time, the approach can compensate for the absence of elevation data in segmentation of aerial images. Our experiments demonstrate that ground-level semantic information (wall estimates) allows to focus the segmentation of the aerial image to find buildings and produce a groundlevel semantic map that covers a larger area than can be built using the onboard sensors along the robot trajectory

  • 128.
    Persson, Martin
    et al.
    Örebro University, School of Science and Technology.
    Duckett, Tom
    Department of Computing and Informatics, University of Lincoln.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Improved mapping and image segmentation by using semantic information to link aerial images and ground-level information2008In: Recent Progress in Robotics: Viable Robotic Service to Human, Berlin: Springer , 2008, 157-169 p.Chapter in book (Other academic)
    Abstract [en]

    This paper investigates the use of semantic information to link ground-level occupancy maps and aerial images. A ground-level semantic map is obtained by a mobile robot equipped with an omnidirectional camera, differential GPS and a laser range finder. The mobile robot uses a virtual sensor for building detection (based on omnidirectional images) to compute the ground-level semantic map, which indicates the probability of the cells being occupied by the wall of a building. These wall estimates from a ground perspective are then matched with edges detected in an aerial image. The result is used to direct a region- and boundary-based segmentation algorithm for building detection in the aerial image. This approach addresses two difficulties simultaneously: 1) the range limitation of mobile robot sensors and 2) the difficulty of detecting buildings in monocular aerial images. With the suggested method building outlines can be detected faster than the mobile robot can explore the area by itself, giving the robot an ability to “see” around corners. At the same time, the approach can compensate for the absence of elevation data in segmentation of aerial images. Our experiments demonstrate that ground-level semantic information (wall estimates) allows to focus the segmentation of the aerial image to find buildings and produce a ground-level semantic map that covers a larger area than can be built using the onboard sensors.

  • 129.
    Persson, Martin
    et al.
    Örebro University, Department of Technology.
    Duckett, Tom
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Virtual sensors for human concepts: building detection by an outdoor mobile robot2007In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 0921-8830, Vol. 55, no 5, 383-390 p.Article in journal (Refereed)
    Abstract [en]

    In human-robot communication it is often important to relate robot sensor readings to concepts used by humans. We suggest the use of a virtual sensor (one or several physical sensors with a dedicated signal processing unit for the recognition of real world concepts) and a method with which the virtual sensor can learn from a set of generic features. The virtual sensor robustly establishes the link between sensor data and a particular human concept. In this work, we present a virtual sensor for building detection that uses vision and machine learning to classify the image content in a particular direction as representing buildings or non-buildings. The virtual sensor is trained on a diverse set of image data, using features extracted from grey level images. The features are based on edge orientation, the configurations of these edges, and on grey level clustering. To combine these features, the AdaBoost algorithm is applied. Our experiments with an outdoor mobile robot show that the method is able to separate buildings from nature with a high classification rate, and to extrapolate well to images collected under different conditions. Finally, the virtual sensor is applied on the mobile robot, combining its classifications of sub-images from a panoramic view with spatial information (in the form of location and orientation of the robot) in order to communicate the likely locations of buildings to a remote human operator. (c) 2006 Elsevier B.V. All rights reserved.

  • 130.
    Persson, Martin
    et al.
    Örebro University, Department of Technology.
    Duckett, Tom
    University of Lincoln, UK.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Virtual sensors for human concepts: building detection by an outdoor mobile robot2006Conference paper (Refereed)
    Abstract [en]

    In human–robot communication it is often important to relate robot sensor readings to concepts used by humans. We suggest the use of a virtual sensor (one or several physical sensors with a dedicated signal processing unit for the recognition of real world concepts) and a method with which the virtual sensor can learn from a set of generic features. The virtual sensor robustly establishes the link between sensor data and a particular human concept. In this work, we present a virtual sensor for building detection that uses vision and machine learning to classify the image content in a particular direction as representing buildings or non-buildings. The virtual sensor is trained on a diverse set of image data, using features extracted from grey level images. The features are based on edge orientation, the configurations of these edges, and on grey level clustering. To combine these features, the AdaBoost algorithm is applied. Our experiments with an outdoor mobile robot show that the method is able to separate buildings from nature with a high classification rate, and to extrapolate well to images collected under different conditions. Finally, the virtual sensor is applied on the mobile robot, combining its classifications of sub-images from a panoramic view with spatial information (in the form of location and orientation of the robot) in order to communicate the likely locations of buildings to a remote human operator.

  • 131.
    Persson, Martin
    et al.
    Örebro University, Department of Technology.
    Duckett, Tom
    University of Lincoln.
    Valgren, Christoffer
    Örebro University, Department of Technology.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Probabilistic semantic mapping with a virtual sensor for building/nature detection2007In: International symposium on computational intelligence in robotics and automation, 2007. CIRA 2007, 2007, 236-242 p.Conference paper (Refereed)
    Abstract [en]

    In human-robot communication it is often important to relate robot sensor readings to concepts used by humans. We believe that access to semantic maps will make it possible for robots to better communicate information to a human operator and vice versa. The main contribution of this paper is a method that fuses data from different sensor modalities, range sensors and vision sensors are considered, to create a probabilistic semantic map of an outdoor environment. The method combines a learned virtual sensor (understood as one or several physical sensors with a dedicated signal processing unit for recognition of real world concepts) for building detection with a standard occupancy map. The virtual sensor is applied on a mobile robot, combining classifications of sub-images from a panoramic view with spatial information (location and orientation of the robot) giving the likely locations of buildings. This information is combined with an occupancy map to calculate a probabilistic semantic map. Our experiments with an outdoor mobile robot show that the method produces semantic maps with correct labeling and an evident distinction between "building" objects from "nature" objects

  • 132. Petrovic, Ivan
    et al.
    Lilienthal, Achim J.Örebro University, School of Science and Technology.
    Proceedings of the 4th European Conferenceon Mobile Robots: ECMR’092009Conference proceedings (editor) (Other academic)
  • 133. Petrovitc, Ivan
    et al.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Special issue ECMR 20092011In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 59, no 5, 263-264 p.Article in journal (Refereed)
  • 134.
    Pomareda, Victor
    et al.
    University of Barcelona, Spain.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Abdul Khaliq, Ali
    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.
    Marco, Santiago
    University of Barcelona, Spain.
    Chemical source localization in real environments integrating chemical concentrations in a probabilistic plume mapping approach2013In: Proceedings of the 15th International Symposium on Olfaction and Electronic Nose (ISOEN 2013), 2013Conference paper (Refereed)
    Abstract [en]

    Chemical plume source localization algorithms can be classified either as reactive plume tracking or gas distribution mapping approaches. Here, we focus on gas distribution mapping methods where the robot does not need to track the plume to find the source and can be used for other tasks. Probabilistic mapping approaches have been previously applied to real-world data successfully; e.g., in the approach proposed by Pang and Farrell. Instead of the quasi-continuous gas measurement values, this algorithm considers events (detections and non-detections) based on whether the sensor response is above or below a threshold to update recursively a source probability grid map; thus, discarding important information. We developed an extension of this event-based approach, integrating chemical concentrations directly instead of binary information. In this work, both algorithms are compared using real-world data obtained from a photo-ionization detector (PID), a non-selective gas sensor, and an anemometer in real environments. We validate simulation results and demonstrate that the concentration-based approach is more accurate in terms of a higher probability at the ground truth source location, a smaller distance between the probability maximum and the source location, and a more peaked probability distribution, measured in terms of the overall entropy.

  • 135.
    Reggente, Matteo
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Statistical evaluation of the kernel DM+V/W algorithm for building gas distribution maps in uncontrolled environments2009In: Proceedings of Eurosensors XXIII conference / [ed] Juergen Brugger, Danick Briand, Elsevier, 2009, Vol. 1, 481-484 p.Conference paper (Refereed)
    Abstract [en]

    In this paper we present a statistical evaluation of the Kernel DM+V/W algorithm to build two-dimensional gas distribution maps with a mobile robot. In addition to gas sensor measurements from an "e-nose" the Kernel DM+V/W algorithm also takes into account wind information received from an ultrasonic anemometer. We evaluate the method based on real measurements in three uncontrolled environments with very different properties. As a measure for the model quality we compute how well unseen measurements are predicted in terms of the data likelihood. A paired Wilcoxon signed rank test shows a significant improvement (at a confidence level of 95%) of the model quality when using wind information.

  • 136.
    Reggente, Matteo
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    The 3D-kernel DM+V/W algorithm: using wind information in three dimensional gas distribution modelling with a mobile robot2010In: 2010 IEEE SENSORS, 2010, 999-1004 p.Conference paper (Other academic)
    Abstract [en]

    In this paper we present a statistical method to build three-dimensional gas distribution maps from gas sensor and wind measurements obtained with a mobile robot in uncontrolled environments. The particular contribution of this paper is to introduce and evaluate an algorithm for 3D statistical gas distribution mapping, that takes into account airflow information. 3D-Kernel DM+V/W algorithm uses a multivariate Gaussian weighting function to model the information provided by the gas sensors and an ultrasonic anemometer. The proposed algorithm is evaluated with respect to the ability of the obtained models to predict unseen measurements. The results based on 15 trials with a mobile robot in an indoor environment show improvements in the model performance when using the 3D kernel DM+V/W algorithm. Moreover the model is able to adapt to the dynamical changes of the environment learning the hyper-parameter from the sensors readings.

  • 137.
    Reggente, Matteo
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Three-dimensional statistical gas distribution mapping in an uncontrolled indoor environment2009In: Olfaction and electronic nose / [ed] Matteo Pardo, Giorgio Sberveglieri, 2009, 109-112 p.Conference paper (Refereed)
    Abstract [en]

    In this paper we present a statistical method to build three-dimensional gas distribution maps (3D-DM). The proposed mapping technique uses kernel extrapolation with a tri-variate Gaussian kernel that models the likelihood that a reading represents the concentration distribution at a distant location in the three dimensions. The method is evaluated using a mobile robot equipped with three "e-noses" mounted at different heights. Initial experiments in an uncontrolled indoor environment are presented and evaluated with respect to the ability of the 3D map, computed from the lower and upper nose, to predict the map from the middle nose.

  • 138.
    Reggente, Matteo
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Using local wind information for gas distribution mapping in outdoor environments with a mobile robot2009In: IEEE sensors, vols 1-3, New York: IEEE conference proceedings, 2009, 1637-1642 p.Conference paper (Refereed)
    Abstract [en]

    In this paper we introduce a statistical method tobuild two-dimensional gas distribution maps (Kernel DM+V/Walgorithm). In addition to gas sensor measurements, the proposedmethod also takes into account wind information by modelingthe information content of the gas sensor measurements as abivariate Gaussian kernel whose shape depends on the measuredwind vector. We evaluate the method based on real measurementsin an outdoor environment obtained with a mobile robot thatwas equipped with gas sensors and an ultrasonic anemometerfor wind measurements. As a measure of the model quality wecompute how well unseen measurements are predicted in termsof the data likelihood. The initial results are encouraging andshow a clear improvement of the proposed method compared tothe case where wind is not considered.

  • 139.
    Reggente, Matteo
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Using local wind information for gas distribution mapping in outdoor environments with a mobile robot2009In: 2009 IEEE SENSORS, VOLS 1-3, NEW YORK: IEEE conference proceedings, 2009, 1715-1720 p.Chapter in book (Other academic)
    Abstract [en]

    In this paper we introduce a statistical method to build two-dimensional gas distribution maps (Kernel DM+V/W algorithm). In addition to gas sensor measurements, the proposed method also takes into account wind information by modeling the information content of the gas sensor measurements as a bivariate Gaussian kernel whose shape depends on the measured wind vector. We evaluate the method based on real measurements in an outdoor environment obtained with a mobile robot that was equipped with gas sensors and an ultrasonic anemometer for wind measurements. As a measure of the model quality we compute how well unseen measurements are predicted in terms of the data likelihood. The initial results are encouraging and show a clear improvement of the proposed method compared to the case where wind is not considered.

  • 140.
    Reggente, Matteo
    et al.
    Örebro University, School of Science and Technology.
    Mondini, Alessio
    CRIM Laboratory, Scuola Superiore Sant'Anna, Pisa, Italy .
    Ferri, Gabriele
    CRIM Laboratory, Scuola Superiore Sant'Anna, Pisa, Italy .
    Mazzolai, Barbara
    3 Centre in MicroBioRobotics IIT at SSSA, Italian Institute of Technology, Pisa, Italy .
    Manzi, Alessandro
    Arts Laboratory, Scuola Superiore Sant'Anna, Pisa, Italy .
    Gabelletti, Matteo
    Arts Laboratory, Scuola Superiore Sant'Anna, Pisa, Italy .
    Dario, Paolo
    CRIM Laboratory, Scuola Superiore Sant'Anna, Pisa, Italy .
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    The DustBot system: using mobile robots to monitor pollution in pedestrian area2010In: NOSE 2010: international conference on environmental odour monitoring and control / [ed] R. DelRosso, 2010, Vol. 23, 273-278 p.Conference paper (Other academic)
    Abstract [en]

    The EU project DustBot addresses urban hydeience. Two types of robots were designed, the DustClean robot to autonomously clean pedestrian areas, and the DustCart robot for door-to-door garbage collection. Three prototype robots were built and equipped with electronic noses so as to enable them to collect environmental data while performing their urban hygiene tasks. Essentially, the robots act as a mobile, wirless node in a sensor network. In this paper we give an overview of the DusBot platform focusig on the Air Monitoring Module (AMM). We descibe the data flow between the robots throught the ubiquitous network to a gas distribution modelling server, where a gas deisribution model is computed. We descibe the Kernel DM+V algorithn, an approach to create statistical gas disdtribution models in the form of predictive mean and variance discrtized onto a grid map. Finally we present and discuss results obtained with the DustBot AMM during experimental trails performex in outdoor public places; a courtyard in Pontedera, Italy and a pedestrian square in Orebor, Sweden.

  • 141.
    Rituerto, Alejandro
    et al.
    Instituto de Investigación en Ingeniería de Aragón, Deptartmento de Informática e Ingeniería de Sistemas, University of Zaragoza, Zaragoza, Spain.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Murillo, Ana C.
    Instituto de Investigación en Ingeniería de Aragón, Deptartmento de Informática e Ingeniería de Sistemas, University of Zaragoza, Zaragoza, Spain.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Jesus Guerrero, Jose
    Instituto de Investigación en Ingeniería de Aragón, Deptartmento de Informática e Ingeniería de Sistemas, University of Zaragoza, Zaragoza, Spain.
    Building an Enhanced Vocabulary of the Robot Environment with a Ceiling Pointing Camera2016In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 16, no 4, 493Article in journal (Refereed)
    Abstract [en]

    Mobile robots are of great help for automatic monitoring tasks in different environments. One of the first tasks that needs to be addressed when creating these kinds of robotic systems is modeling the robot environment. This work proposes a pipeline to build an enhanced visual model of a robot environment indoors. Vision based recognition approaches frequently use quantized feature spaces, commonly known as Bag of Words (BoW) or vocabulary representations. A drawback using standard BoW approaches is that semantic information is not considered as a criteria to create the visual words. To solve this challenging task, this paper studies how to leverage the standard vocabulary construction process to obtain a more meaningful visual vocabulary of the robot work environment using image sequences. We take advantage of spatio-temporal constraints and prior knowledge about the position of the camera. The key contribution of our work is the definition of a new pipeline to create a model of the environment. This pipeline incorporates (1) tracking information to the process of vocabulary construction and (2) geometric cues to the appearance descriptors. Motivated by long term robotic applications, such as the aforementioned monitoring tasks, we focus on a configuration where the robot camera points to the ceiling, which captures more stable regions of the environment. The experimental validation shows how our vocabulary models the environment in more detail than standard vocabulary approaches, without loss of recognition performance. We show different robotic tasks that could benefit of the use of our visual vocabulary approach, such as place recognition or object discovery. For this validation, we use our publicly available data-set.

  • 142.
    Saarinen, Jari
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Ala-Luhtala, Juha
    Aalto University of Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Normal distributions transform occupancy maps: application to large-scale online 3D mapping2013In: IEEE International Conference on Robotics and Automation, New York: IEEE conference proceedings, 2013, 2233-2238 p.Conference paper (Refereed)
    Abstract [en]

    Autonomous vehicles operating in real-world industrial environments have to overcome numerous challenges, chief among which is the creation and maintenance of consistent 3D world models. This paper proposes to address the challenges of online real-world mapping by building upon previous work on compact spatial representation and formulating a novel 3D mapping approach — the Normal Distributions Transform Occupancy Map (NDT-OM). The presented algorithm enables accurate real-time 3D mapping in large-scale dynamic nvironments employing a recursive update strategy. In addition, the proposed approach can seamlessly provide maps at multiple resolutions allowing for fast utilization in high-level functions such as localization or path planning. Compared to previous approaches that use the NDT representation, the proposed NDT-OM formulates an exact and efficient recursive update formulation and models the full occupancy of the map.

  • 143.
    Saarinen, Jari
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    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.
    3D normal distributions transform occupancy maps: an efficient representation for mapping in dynamic environments2013In: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176, Vol. 32, no 14, 1627-1644 p.Article in journal (Refereed)
    Abstract [en]

    In order to enable long-term operation of autonomous vehicles in industrial environments numerous challenges need to be addressed. A basic requirement for many applications is the creation and maintenance of consistent 3D world models. This article proposes a novel 3D spatial representation for online real-world mapping, building upon two known representations: normal distributions transform (NDT) maps and occupancy grid maps. The proposed normal distributions transform occupancy map (NDT-OM) combines the advantages of both representations; compactness of NDT maps and robustness of occupancy maps. One key contribution in this article is that we formulate an exact recursive updates for NDT-OMs. We show that the recursive update equations provide natural support for multi-resolution maps. Next, we describe a modification of the recursive update equations that allows adaptation in dynamic environments. As a second key contribution we introduce NDT-OMs and formulate the occupancy update equations that allow to build consistent maps in dynamic environments. The update of the occupancy values are based on an efficient probabilistic sensor model that is specially formulated for NDT-OMs. In several experiments with a total of 17 hours of data from a milk factory we demonstrate that NDT-OMs enable real-time performance in large-scale, long-term industrial setups.

  • 144.
    Saarinen, Jari
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    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.
    Normal distributions transform monte-carlo localization (NDT-MCL)2013In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2013, 382-389 p.Conference paper (Refereed)
  • 145.
    Saarinen, Jari
    et al.
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    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.
    Fast 3D mapping in highly dynamic environments using normal distributions transform occupancy maps2013In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2013, 4694-4701 p.Conference paper (Refereed)
  • 146.
    Schaffernicht, Erik
    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.
    Bayesian Spatial Event Distribution Grid Maps for Modeling the Spatial Distribution of Gas Detection Events2014In: Sensor Letters, ISSN 1546-198X, E-ISSN 1546-1971, Vol. 12, no 6-7, 1142-1146 p.Article in journal (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 a quasi-continuous gas concentration, models the spatial distribution of gas events, for example 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 μ 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 as well, enabling a simple approach for sequential learning. To learn a map composed of beta distributions, we discretize the inspection area into a grid and extrapolate from local measurements using Gaussian kernels. We demonstrate the proposed algorithm for MOX sensors and a photo ionization detector mounted on a mobile robot and show how qualitatively similar maps are obtained from very different gas sensors.

  • 147.
    Schindler, Maike
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Chadalavada, Ravi
    Örebro University, School of Science and Technology.
    Ögren, Magnus
    Örebro University, School of Science and Technology.
    Creativity in the eye of the student: Refining investigations of mathematical creativity using eye-tracking goggles2016In: Proceedings of the 40th Conference of the International Group for the Psychology of Mathematics Education (PME) / [ed] C. Csíkos, A. Rausch, & J. Szitányi, 2016Conference paper (Refereed)
    Abstract [en]

    Mathematical creativity is increasingly important for improved innovation and problem-solving. In this paper, we address the question of how to best investigate mathematical creativity and critically discuss dichotomous creativity scoring schemes. In order to gain deeper insights into creative problem-solving processes, we suggest the use of mobile, unobtrusive eye-trackers for evaluating students’ creativity in the context of Multiple Solution Tasks (MSTs). We present first results with inexpensive eye-tracking goggles that reveal the added value of evaluating students’ eye movements when investigating mathematical creativity—compared to an analysis of written/drawn solutions as well as compared to an analysis of simple videos.

  • 148.
    Siddiqui, J. Rafid
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Driankov, Dimiter
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Towards visual mapping in industrial environments: a heterogeneous task-specific and saliency driven approach2016In: 2016 IEEE International Conference on Robotics and Automation (ICRA), Institute of Electrical and Electronics Engineers (IEEE), 2016, 5766-5773 p., 7487800Conference paper (Refereed)
    Abstract [en]

    The highly percipient nature of human mind in avoiding sensory overload is a crucial factor which gives human vision an advantage over machine vision, the latter has otherwise powerful computational resources at its disposal given today’s technology. This stresses the need to focus on methods which extract a concise representation of the environment inorder to approach a complex problem such as visual mapping. This article is an attempt of creating a mapping system, which proposes an architecture that combines task-specific and saliency driven approaches. The proposed method is implemented on a warehouse robot. The proposed solution provide a priority framework which enables an industrial robot to build a concise visual representation of the environment. The method is evaluated on data collected by a RGBD sensor mounted on a fork-lift robot and shows promise for addressing visual mapping problems in industrial environments.

  • 149.
    Skoglund, Alexander
    et al.
    Örebro University, Department of Technology.
    Duckett, Tom
    Örebro University, Department of Technology.
    Iliev, Boyko
    Örebro University, Department of Technology.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Palm, Rainer
    Örebro University, Department of Technology.
    Teaching by demonstration of robotic manipulators in non-stationary environments2006Conference paper (Refereed)
    Abstract [en]

    In this paper we propose a system consisting of a manipulator equipped with range sensors, that is instructed to follow a trajectory demonstrated by a human teacher wearing a motion capturing device. During the demonstration a three dimensional occupancy grid of the environment is built using the range sensor information and the trajectory. The demonstration is followed by an exploration phase, where the robot undergoes self-improvement of the task, during which the occupancy grid is used to avoid collisions. In parallel a reinforcement learning (RL) agent, biased by the demonstration, learns a point-to-point task policy. When changes occur in the workspace, both the occupancy grid and the learned policy will be updated online by the system.

  • 150.
    Stachniss, Cyril
    et al.
    Dept. for Computer Science, Albert-Ludwigs-University Freiburg.
    Plagemann, Christian
    Dept. for Computer Science, Albert-Ludwigs-University Freiburg.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Burgard, Wolfram
    Dept. for Computer Science, Albert-Ludwigs-University Freiburg.
    Gas distribution modeling using sparse Gaussian process mixture models2008In: Robotics: science and systems IV / [ed] Oliver Brock, Jeff Trinkle, Fabio Ramos, Cambridge, MA: MIT press , 2008, 310-317 p.Conference paper (Other (popular science, discussion, etc.))
    Abstract [en]

    In this paper, we consider the problem of learning a two dimensional spatial model of a gas distribution with a mobile robot. Building maps that can be used to accurately predict the gas concentration at query locations is a challenging task due to the chaotic nature of gas dispersal. We present an approach that formulates this task as a regression problem. To deal with the specific properties of typical gas distributions, we propose a sparse Gaussian process mixture model. This allows us to accurately represent the smooth background signal as well as areas of high concentration. We integrate the sparsification of the training data into an EM procedure used for learning the mixture components and the gating function. Our approach has been implemented and tested using datasets recorded with a real mobile robot equipped with an electronic nose. We demonstrate that our models are well suited for predicting gas concentrations at new query locations and that they outperform alternative methods used in robotics to carry out in this task.

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