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  • 151.
    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, p. 109-112Conference 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.

  • 152.
    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, p. 1637-1642Conference 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.

  • 153.
    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, p. 1715-1720Chapter 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.

  • 154.
    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: Chemical Engineering Transactions, ISSN 1974-9791, E-ISSN 2283-9216, Vol. 23, p. 273-278Article in journal (Refereed)
    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 Örebro, Sweden.

  • 155.
    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, article id 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.

  • 156.
    Saarinen, Jari
    et al.
    Department of Automation and Systems Technology, Aalto University, Alto, Finland.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Independent Markov Chain Occupancy Grid Maps for Representation of Dynamic Environments2012In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, New York, USA: IEEE, 2012, p. 3489-3495Conference paper (Refereed)
    Abstract [en]

    In this paper we propose a new grid based approach to model a dynamic environment. Each grid cell is assumed to be an independent Markov chain (iMac) with two states. The state transition parameters are learned online and modeled as two Poisson processes. As a result, our representation not only encodes the expected occupancy of the cell, but also models the expected dynamics within the cell. The paper also presents a strategy based on recency weighting to learn the model parameters from observations that is able to deal with non-stationary cell dynamics. Moreover, an interpretation of the model parameters with discussion about the convergence rates of the cells is presented. The proposed model is experimentally validated using offline data recorded with a Laser Guided Vehicle (LGV) system running in production use.

  • 157.
    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, p. 2233-2238Conference 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.

  • 158.
    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, p. 1627-1644Article 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.

  • 159.
    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, p. 382-389Conference paper (Refereed)
  • 160.
    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, p. 4694-4701Conference paper (Refereed)
  • 161.
    Schaffernicht, Erik
    et al.
    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.
    Mobile robots for learning spatio-temporal interpolation models in sensor networks - The Echo State map approach: The Echo State map approach2017In: 2017 IEEE International Conference on Robotics and Automation (ICRA), Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 2659-2665Conference paper (Refereed)
    Abstract [en]

    Sensor networks have limited capabilities to model complex phenomena occuring between sensing nodes. Mobile robots can be used to close this gap and learn local interpolation models. In this paper, we utilize Echo State Networks in order to learn the calibration and interpolation model between sensor nodes using measurements collected by a mobile robot. The use of Echo State Networks allows to deal with temporal dependencies implicitly, while the spatial mapping with a Gaussian Process estimator exploits the fact that Echo State Networks learn linear combinations of complex temporal dynamics. The resulting Echo State Map elegantly combines spatial and temporal cues into a single representation. We showcase the method in the exposure modeling task of building dust distribution maps for foundries, a challenge which is of great interest to occupational health researchers. Results from simulated data and real world experiments highlight the potential of Echo State Maps. While we focus on particulate matter measurements, the method can be applied for any other environmental variables like temperature or gas concentration.

  • 162.
    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, p. 1142-1146Article 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.

  • 163.
    Schindler, Maike
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Eye-Tracking and its Domain-Specific Interpretation: A Stimulated Recall Study on Eye Movements in Geometrical Tasks2017In: Proceedings of the 41st Conference of the International Group for the Psychology of Mathematics Education / [ed] Kaur, B., Ho, W.K., Toh, T.L., & Choy, B.H, Singapore: PME , 2017, Vol. 4, p. 153-160Conference paper (Refereed)
    Abstract [en]

    Eye-tracking offers various possibilities for mathematics education. Yet, even in suitably visually presented tasks, interpretation of eye-tracking data is non-trivial. A key reason is that the interpretation of eye-tracking data is context-sensitive. To reduce ambiguity and uncertainty, we studied the interpretation of eye movements in a specific domain: geometrical mathematical creativity tasks. We present results from a qualitative empirical study in which we analyzed a Stimulated Recall Interview where a student watched the eye-tracking overlaid video of his work on a task. Our results hint at how eye movements can be interpreted and show limitations and opportunities of eye tracking in the domain of mathematical geometry tasks and beyond.

  • 164.
    Schindler, Maike
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Eye-Tracking As A Tool For Investigating Mathematical Creativity2017In: The 10th Mathematical Creativity and Giftedness International Conference: Proceedings, Nicosia, Cyprus: Department of Education, University of Cyprus , 2017, p. 45-50Conference paper (Refereed)
    Abstract [en]

    Mathematical creativity as a key ability in our increasingly automated and interconnected, high-technology based society and economy is increasingly in the focus of mathematics education research. The recent scientific discussion in this domain is shifting from a product view, on written solutions and drawings, to a process view, which aims to investigate the different stages of how students come up with creative ideas. The latter is, however, a challenge. In this theoretical-methodological paper, we present and discuss the opportunities that eye-tracking offers for studying creativity in a process view. We discuss in which way eye-tracking allows to obtain novel answers to the questions of how original ideas come up, how they evolve and what leads to the so-called Eureka!-moment. We focus on video-based eye tracking approaches, discuss pros and cons of screen-based and mobile eye tracking, and illustrate methods of data analysis and their benefits for research on mathematical creativity.

  • 165.
    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.

  • 166.
    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, p. 5766-5773, article id 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.

  • 167.
    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.

  • 168.
    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, p. 310-317Conference 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.

  • 169.
    Stachniss, Cyrill
    et al.
    University of Freiburg.
    Plagemann, Christian
    Stanford University.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Learning Gas Distribution Models Using Sparse Gaussian Process Mixtures2009In: Autonomous Robots, ISSN 0929-5593, E-ISSN 1573-7527, Vol. 26, no 2-3, p. 187-202Article in journal (Refereed)
    Abstract [en]

    In this paper, we consider the problem of learning two-dimensional spatial models of gas distributions. To build models of gas distributions 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 formulate this task as a regression problem. To deal with the specific properties of gas distributions, we propose a sparse Gaussian process mixture model, which allows us to accurately represent the smooth background signal and the areas with patches of high concentrations. We furthermore integrate the sparsification of the training data into an EM procedure that we apply 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. The experiments demonstrate that our technique is well-suited for predicting gas concentrations at new query locations and that it outperforms alternative and previously proposed methods in robotics.

  • 170.
    Stoyanov, Todor
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Maximum Likelihood Point Cloud Acquisition from a Rotating Laser Scanner on a Moving Platform2009In: Proceedings of the IEEE International Conference on Advanced Robotics (ICAR), IEEE conference proceedings, 2009Conference paper (Refereed)
    Abstract [en]

    This paper describes an approach to acquire locally consistent range data scans from a moving sensor platform. Data from a vertically mounted rotating laser scanner and odometry position estimates are fused and used to estimate maximum likelihood point clouds. An estimation algorithm is applied to reduce the accumulated error after a full rotation of the range finder. A configuration consisting of a SICK laser scanner mounted on a rotational actuator is described and used to evaluate the proposed approach. The data sets analyzed suggest a significant improvement in point cloud consistency, even over a short travel distance.

  • 171.
    Stoyanov, Todor
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Maximum likelihood point cloud acquisition from a mobile platform2009In: International conference on advanced robotics, ICAR 2009., New York: IEEE conference proceedings, 2009, p. 1-6Conference paper (Refereed)
    Abstract [en]

    This paper describes an approach to acquire locally consistent range data scans from a moving sensor platform. Data from a vertically mounted rotating laser scanner and odometry position estimates are fused and used to estimate maximum likelihood point clouds. An estimation algorithm is applied to reduce the accumulated error after a full rotation of the range finder. A configuration consisting of a SICK laser scanner mounted on a rotational actuator is described and used to evaluate the proposed approach. The data sets analyzed suggest a significant improvement in point cloud consistency, even over a short travel distance.

  • 172.
    Stoyanov, Todor
    et al.
    Örebro University, School of Science and Technology.
    Louloudi, Athanasia
    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.
    Comparative evaluation of range sensor accuracy in indoor environments2011In: Proceedings of the 5th European Conference on Mobile Robots, ECMR 2011 / [ed] Achim J. Lilienthal, Tom Duckett, 2011, p. 19-24Conference paper (Refereed)
    Abstract [en]

    3D range sensing is one of the important topics in robotics, as it is often a component in vital autonomous subsystems like collision avoidance, mapping and semantic perception. The development of affordable, high frame rate and precise 3D range sensors is thus of considerable interest. Recent advances in sensing technology have produced several novel sensors that attempt to meet these requirements. This work is concerned with the development of a holistic method for accuracy evaluation of the measurements produced by such devices. A method for comparison of range sensor output to a set of reference distance measurements is proposed. The approach is then used to compare the behavior of three integrated range sensing devices, to that of a standard actuated laser range sensor. Test cases in an uncontrolled indoor environment are performed in order to evaluate the sensors’ performance in a challenging, realistic application scenario.

  • 173.
    Stoyanov, Todor
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Almqvist, Håkan
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    On the Accuracy of the 3D Normal Distributions Transform as a Tool for Spatial Representation2011In: 2011 IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings, 2011Conference paper (Refereed)
    Abstract [en]

    The Three-Dimensional Normal Distributions Transform (3D-NDT) is a spatial modeling technique with applications in point set registration, scan similarity comparison, change detection and path planning. This work concentrates on evaluating three common variations of the 3D-NDT in terms of accuracy of representing sampled semi-structured environments. In a novel approach to spatial representation quality measurement, the 3D geometrical modeling task is formulated as a classification problem and its accuracy is evaluated with standard machine learning performance metrics. In this manner the accuracy of the 3D-NDT variations is shown to be comparable to, and in some cases to outperform that of the standard occupancy grid mapping model.

  • 174.
    Stoyanov, Todor
    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 J.
    Örebro University, School of Science and Technology.
    Path planning in 3D environments using the normal distributions transform2010In: IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems (IROS 2010), IEEE conference proceedings, 2010, p. 3263-3268Conference paper (Refereed)
    Abstract [en]

    Planning feasible paths in fully three-dimensional environments is a challenging problem. Application of existing algorithms typically requires the use of limited 3D representations that discard potentially useful information. This article proposes a novel approach to path planning that utilizes a full 3D representation directly: the Three-Dimensional Normal Distributions Transform (3D-NDT). The well known wavefront planner is modified to use 3D-NDT as a basis for map representation and evaluated using both indoor and outdoor data sets. The use of 3D-NDT for path planning is thus demonstrated to be a viable choice with good expressive capabilities.

  • 175.
    Stoyanov, Todor
    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.
    Comparative evaluation of the consistency of three-dimensional spatial representations used in autonomous robot navigation2013In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 30, no 2, p. 216-236Article in journal (Refereed)
    Abstract [en]

    An increasing number of robots for outdoor applications rely on complex three-dimensional (3D) environmental models. In many cases, 3D maps are used for vital tasks, such as path planning and collision detection in challenging semistructured environments. Thus, acquiring accurate three-dimensional maps is an important research topic of high priority for autonomously navigating robots. This article proposes an evaluation method that is designed to compare the consistency with which different representations model the environment. In particular, the article examines several popular (probabilistic) spatial representations that are capable of predicting the occupancy of any point in space, given prior 3D range measurements. This work proposes to reformulate the obtained environmental models as probabilistic binary classifiers, thus allowing for the use of standard evaluation and comparison procedures. To avoid introducing localization errors, this article concentrates on evaluating models constructed from measurements acquired at fixed sensor poses. Using a cross-validation approach, the consistency of different representations, i.e., the likelihood of correctly predicting unseen measurements in the sensor field of view, can be evaluated. Simulated and real-world data sets are used to benchmark the precision of four spatial models—occupancy grid, triangle mesh, and two variations of the three-dimensional normal distributions transform (3D-NDT)—over various environments and sensor noise levels. Overall, the consistency of representation of the 3D-NDT is found to be the highest among the tested models, with a similar performance over varying input data.

  • 176.
    Stoyanov, Todor
    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.
    Point Set Registration through Minimization of the L-2 Distance between 3D-NDT Models2012In: 2012 IEEE International Conference on Robotics and Automation (ICRA), IEEE conference proceedings, 2012, p. 5196-5201Conference paper (Refereed)
    Abstract [en]

    Point set registration — the task of finding the best fitting alignment between two sets of point samples, is an important problem in mobile robotics. This article proposes a novel registration algorithm, based on the distance between Three- Dimensional Normal Distributions Transforms. 3D-NDT models — a sub-class of Gaussian Mixture Models with uniformly weighted, largely disjoint components, can be quickly computed from range point data. The proposed algorithm constructs 3DNDT representations of the input point sets and then formulates an objective function based on the L2 distance between the considered models. Analytic first and second order derivatives of the objective function are computed and used in a standard Newton method optimization scheme, to obtain the best-fitting transformation. The proposed algorithm is evaluated and shown to be more accurate and faster, compared to a state of the art implementation of the Iterative Closest Point and 3D-NDT Point-to-Distribution algorithms.

  • 177.
    Stoyanov, Todor
    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.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Fast and accurate scan registration through minimization of the distance between compact 3D NDT Representations2012In: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176, Vol. 31, no 12, p. 1377-1393Article in journal (Refereed)
    Abstract [en]

    Registration of range sensor measurements is an important task in mobile robotics and has received a lot of attention. Several iterative optimization schemes have been proposed in order to align three-dimensional (3D) point scans. With the more widespread use of high-frame-rate 3D sensors and increasingly more challenging application scenarios for mobile robots, there is a need for fast and accurate registration methods that current state-of-the-art algorithms cannot always meet. This work proposes a novel algorithm that achieves accurate point cloud registration an order of a magnitude faster than the current state of the art. The speedup is achieved through the use of a compact spatial representation: the Three-Dimensional Normal Distributions Transform (3D-NDT). In addition, a fast, global-descriptor based on the 3D-NDT is defined and used to achieve reliable initial poses for the iterative algorithm. Finally, a closed-form expression for the covariance of the proposed method is also derived. The proposed algorithms are evaluated on two standard point cloud data sets, resulting in stable performance on a par with or better than the state of the art. The implementation is available as an open-source package for the Robot Operating system (ROS).

  • 178.
    Stoyanov, Todor
    et al.
    Örebro University, School of Science and Technology.
    Mojtahedzadeh, Rasoul
    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.
    Comparative evaluation of range sensor accuracy for indoor mobile robotics and automated logistics applications2013In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 61, no 10, p. 1094-1105Article in journal (Refereed)
    Abstract [en]

    3D range sensing is an important topic in robotics, as it is a component in vital autonomous subsystems such as for collision avoidance, mapping and perception. The development of affordable, high frame rate and precise 3D range sensors is thus of considerable interest. Recent advances in sensing technology have produced several novel sensors that attempt to meet these requirements. This work is concerned with the development of a holistic method for accuracy evaluation of the measurements produced by such devices. A method for comparison of range sensor output to a set of reference distance measurements, without using a precise ground truth environment model, is proposed. This article presents an extensive evaluation of three novel depth sensors — the Swiss Ranger SR-4000, Fotonic B70 and Microsoft Kinect. Tests are concentrated on the automated logistics scenario of container unloading. Six different setups of box-, cylinder-, and sack-shaped goods inside a mock-up container are used to collect range measurements. Comparisons are performed against hand-crafted ground truth data, as well as against a reference actuated Laser Range Finder (aLRF) system. Additional test cases in an uncontrolled indoor environment are performed in order to evaluate the sensors’ performance in a challenging, realistic application scenario.

  • 179.
    Stoyanov, Todor
    et al.
    Örebro University, School of Science and Technology.
    Saarinen, Jari
    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.
    Normal distributions transform occupancy map fusion: simultaneous mapping and tracking in large scale dynamic environments2013In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2013, p. 4702-4708Conference paper (Refereed)
  • 180.
    Stoyanov, Todor
    et al.
    Örebro University, School of Science and Technology.
    Vaskevicius, Narunas
    Jacobs University Bremen, Bremen, Germany.
    Mueller, Christian Atanas
    Jacobs University Bremen, Bremen, Germany.
    Fromm, Tobias
    Jacobs University Bremen, Bremen, Germany.
    Krug, Robert
    Örebro University, School of Science and Technology.
    Tincani, Vinicio
    University of Pisa, Pisa, Italy.
    Mojtahedzadeh, Rasoul
    Örebro University, School of Science and Technology.
    Kunaschk, Stefan
    Bremer Institut für Produktion und Logistik (BIBA), Bremen, Germany.
    Ernits, R. Mortensen
    Bremer Institut für Produktion und Logistik (BIBA), Bremen, Germany.
    Canelhas, Daniel R.
    Örebro University, School of Science and Technology.
    Bonilla, Manuell
    University of Pisa, Pisa, Italy.
    Schwertfeger, Soeren
    ShanghaiTech University, Shanghai, China.
    Bonini, Marco
    Reutlingen University, Reutlingen, Germany.
    Halfar, Harry
    Reutlingen University, Reutlingen, Germany.
    Pathak, Kaustubh
    Jacobs University Bremen, Bremen, Germany.
    Rohde, Moritz
    Bremer Institut für Produktion und Logistik (BIBA), Bremen, Germany.
    Fantoni, Gualtiero
    University of Pisa, Pisa, Italy.
    Bicchi, Antonio
    Università di Pisa & Istituto Italiano di Tecnologia, Genova, Italy.
    Birk, Andreas
    Jacobs University, Bremen, Germany.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Echelmeyer, Wolfgang
    Reutlingen University, Reutlingen, Germany.
    No More Heavy Lifting: Robotic Solutions to the Container-Unloading Problem2016In: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223X, Vol. 23, no 4, p. 94-106Article in journal (Refereed)
  • 181.
    Tincani, Vinicio
    et al.
    University of Pisa, Pisa, Italy.
    Catalano, Manuel
    University of Pisa, Pisa, Italy.
    Grioli, Giorgio
    University of Pisa, Pisa, Italy.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Krug, Robert
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Fantoni, Gualtiero
    University of Pisa, Pisa, Italy.
    Bicchi, Antonio
    University of Pisa, Pisa, Italy; Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genova, Italy.
    Sensitive Active Surfaces on the Velvet II Dexterous Gripper2015Conference paper (Refereed)
  • 182.
    Tincani, Vinicio
    et al.
    University of Pisa, Pisa, Italy.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Krug, Robert
    Örebro University, School of Science and Technology.
    Catalano, Manuel
    University of Pisa, Pisa, Italy.
    Grioli, Giorgio
    University of Pisa, Pisa, Italy.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Fantoni, Gualtiero
    University of Pisa, Pisa, Italy.
    Bicchi, Antonio
    Istituto Italiano di Tecnologia, Genoa, Italy.
    The Grasp Acquisition Strategy of the Velvet II2015Conference paper (Refereed)
  • 183.
    Triebel, Rudolph
    et al.
    Department of Computer Science, TU, Munich, Germany.
    Arras, Kai
    Social Robotics Lab, University of Freiburg, Freiburg im Breisgau, Germany.
    Alami, Rachid
    LAAS-CNRS: Laboratory for Analysis and Architecture of Systems, Toulouse, France.
    Beyer, Lucas
    RWTH Aachen, Aachen, Germany.
    Breuers, Stefan
    RWTH Aachen, Aachen, Germany.
    Chatila, Raja
    ISIR-CNRS: Institute for Intelligent Systems and Robotics, Paris, France.
    Chetouani, Mohamed
    ISIR-CNRS: Institute for Intelligent Systems and Robotics, Paris, France.
    Cremers, Daniel
    Department of Computer Science, TU, Munich, Germany.
    Evers, Vanessa
    University of Twente, Enschede, Netherlands.
    Fiore, Michelangelo
    LAAS-CNRS: Laboratory for Analysis and Architecture of Systems, Toulouse, France.
    Hung, Hayley
    Delft University of Technology, Delft, Netherlands.
    Ramirez, Omar A. Islas
    ISIR-CNRS: Institute for Intelligent Systems and Robotics, Paris, France.
    Joosse, Michiel
    University of Twente, Enschede, Netherlands.
    Khambhaita, Harmish
    LAAS-CNRS: Laboratory for Analysis and Architecture of Systems, Toulouse, France.
    Kucner, Tomasz
    Leibe, Bastian
    RWTH Aachen, Aachen, Germany.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Linder, Timm
    Social Robotics Lab, University of Freiburg, Freiburg im Breisgau, Germany.
    Lohse, Manja
    University of Twente, Enschede, Netherlands.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Okal, Billy
    Social Robotics Lab, University of Freiburg, Freiburg im Breisgau, Germany.
    Palmieri, Luigi
    Social Robotics Lab, University of Freiburg, Freiburg im Breisgau, Germany.
    Rafi, Umer
    RWTH Aachen, Aachen, Germany.
    van Rooij, Marieke
    University of Amsterdam, Amsterdam, Netherlands.
    Zhang, Lu
    University of Twente, Enschede, Netherlands; Delft University of Technology, Delft, Netherlands.
    SPENCER: A Socially Aware Service Robot for Passenger Guidance and Help in Busy Airports2016In: Field and Service Robotics: Results of the 10th International Conference / [ed] David S. Wettergreen, Timothy D. Barfoot, Springer, 2016, p. 607-622Conference paper (Refereed)
    Abstract [en]

    We present an ample description of a socially compliant mobile robotic platform, which is developed in the EU-funded project SPENCER. The purpose of this robot is to assist, inform and guide passengers in large and busy airports. One particular aim is to bring travellers of connecting flights conveniently and efficiently from their arrival gate to the passport control. The uniqueness of the project stems from the strong demand of service robots for this application with a large potential impact for the aviation industry on one side, and on the other side from the scientific advancements in social robotics, brought forward and achieved in SPENCER. The main contributions of SPENCER are novel methods to perceive, learn, and model human social behavior and to use this knowledge to plan appropriate actions in real-time for mobile platforms. In this paper, we describe how the project advances the fields of detection and tracking of individuals and groups, recognition of human social relations and activities, normative human behavior learning, socially-aware task and motion planning, learning socially annotated maps, and conducting empirical experiments to assess socio-psychological effects of normative robot behaviors.

  • 184.
    Trincavelli, Marco
    et al.
    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.
    A Least Squares approach for learning gas distribution maps from a set of integral gas concentration measurements obtained with a TDLAS sensor2012In: Proceedings of the IEEE Sensors Conference, 2012, IEEE Sensors Council, 2012, p. 550-553Conference paper (Refereed)
    Abstract [en]

    Applications related to industrial plant surveillance and environmental monitoring often require the creation of gas distribution maps (GDM). In this paper an approach for creating a gas distribution map using a Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor and a laser range scanner mounted on a pan tilt unit is presented. The TDLAS sensor can remotely sense the target gas, in this case methane, requiring novel GDM algorithms compared to the ones developed for traditional in-situ chemical sensors. The presented setup makes it possible to create a 3D model of the environment and to calculate the path travelled by the TDLAS beam. The knowledge of the beam path is of crucial importance since a TDLAS sensor provides an integral measurement of the gas concentration over that path. An efficient GDM algorithm based on a quadratic programming formulation is proposed. The approach is tested in an indoor scenario where transparent bottles filled with methane are successfully localized.

  • 185.
    Trincavelli, Marco
    et al.
    Örebro University, Department of Technology.
    Reggente, Matteo
    Örebro University, Department of Technology.
    Coradeschi, Silvia
    Örebro University, Department of Technology.
    Loutfi, Amy
    Örebro University, Department of Technology.
    Ishida, Hiroshi
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Towards environmental monitoring with mobile robots2008In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2008, New York: IEEE , 2008, p. 2210-2215Conference paper (Refereed)
    Abstract [en]

    In this paper we present initial experiments towards environmental monitoring with a mobile platform. A prototype of a pollution monitoring robot was set up which measures the gas distribution using an “electronic nose” and provides three dimensional wind measurements using an ultrasonic anemometer. We describe the design of the robot and the experimental setup used to run trials under varying environmental conditions. We then present the results of the gas distribution mapping. The trials which were carried out in three uncontrolled environments with very different properties:

    an enclosed indoor area, a part of a long corridor with open ends and a high ceiling, and an outdoor scenario are presented and discussed.

  • 186.
    Trincavelli, Marco
    et al.
    Örebro University, School of Science and Technology.
    Vergara, A.
    Rulkov, N.
    Murguia, J. S.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Huerta, R.
    Optimizing the operating temperature for an array of MOX sensors on an open sampling system2011In: Olfaction and electronic nose: Proceedings of the 14th international symposium on olfaction and electonic nose, 2011, p. 225-227Conference paper (Refereed)
    Abstract [en]

    Chemo-resistive transduction is essential for capturing the spatio-temporal structure of chemical compounds dispersed in different environments. Due to gas dispersion mechanisms, namely diffusion, turbulence and advection, the sensors in an open sampling system, i.e. directly exposed to the environment to be monitored, are exposed to low concentrations of gases with many fluctuations making, as a consequence, the identification and monitoring of the gases even more complicated and challenging than in a controlled laboratory setting. Therefore, tuning the value of the operating temperature becomes crucial for successfully identifying and monitoring the pollutant gases, particularly in applications such as exploration of hazardous areas, air pollution monitoring, and search and rescue I. In this study we demonstrate the benefit of optimizing the sensor's operating temperature when the sensors are deployed in an open sampling system, i.e. directly exposed to the environment to be monitored.

  • 187.
    Valencia, Rafael
    et al.
    Örebro University, School of Science and Technology.
    Saarinen, Jari
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Vallvé, Joan
    CSIC-UPC, Barcelona,Spain.
    Andrade-Cetto, Juan
    CSIC-UPC, Barcelona, Spain.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Localization in highly dynamic environments using dual-timescale NDT-MCL2014In: 2014 IEEE International Conference on Robotics and Automation (ICRA), IEEE Robotics and Automation Society, 2014, p. 3956-3962Conference paper (Refereed)
    Abstract [en]

    Industrial environments are rarely static and oftentheir configuration is continuously changing due to the materialtransfer flow. This is a major challenge for infrastructure freelocalization systems. In this paper we address this challengeby introducing a localization approach that uses a dualtimescaleapproach. The proposed approach - Dual-TimescaleNormal Distributions Transform Monte Carlo Localization (DTNDT-MCL) - is a particle filter based localization method,which simultaneously keeps track of the pose using an aprioriknown static map and a short-term map. The short-termmap is continuously updated and uses Normal DistributionsTransform Occupancy maps to maintain the current state ofthe environment. A key novelty of this approach is that it doesnot have to select an entire timescale map but rather use thebest timescale locally. The approach has real-time performanceand is evaluated using three datasets with increasing levels ofdynamics. We compare our approach against previously proposedNDT-MCL and commonly used SLAM algorithms andshow that DT-NDT-MCL outperforms competing algorithmswith regards to accuracy in all three test cases.

  • 188.
    Valgren, Christoffer
    et al.
    Örebro University, Department of Technology.
    Duckett, Tom
    University of Lincoln, United Kingdom.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Incremental spectral clustering and its application to topological mapping2007In: 2007 IEEE international conference on robotics and automation (ICRA), 2007, p. 4283-4288Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel use of spectral clustering algorithms to support cases where the entries in the affinity matrix are costly to compute. The method is incremental – the spectral clustering algorithm is applied to the affinity matrix after each row/column is added – which makes it possible to inspect the clusters as new data points are added. The method is well suited to the problem of appearance-based, on-line topological mapping for mobile robots. In this problem domain, we show that we can reduce environment-dependent parameters of the clustering algorithm to just a single, intuitive parameter. Experimental results in large outdoor and indoor environments show that we can close loops correctly by computing only a fraction of the entries in the affinity matrix. The accompanying video clip shows how an example map is produced by the algorithm.

  • 189.
    Valgren, Christoffer
    et al.
    Örebro University, Department of Technology.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Incremental spectral clustering and seasons: appearance-based localization in outdoor environments2008In: IEEE international conference on robotics and automation, ICRA 2008, New York: IEEE , 2008, p. 1856-1861Conference paper (Refereed)
    Abstract [en]

    The problem of appearance-based mapping and navigation in outdoor environments is far from trivial. In this paper, an appearance-based topological map, covering a large, mixed indoor and outdoor environment, is built incrementally by using panoramic images. The map is based on image similarity, so that the resulting segmentation of the world corresponds closely to the human concept of a place. Using high-resolution images and the epipolar constraint, the resulting map is shown to be very suitable for localization, even when the environment has undergone seasonal changes.

  • 190.
    Valgren, Christoffer
    et al.
    Department of Computer Science, Örebro University, Örebro, Sweden.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    SIFT, SURF & seasons: Appearance-based long-term localization in outdoor environments2010In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 58, no 2, p. 149-156Article in journal (Refereed)
    Abstract [en]

    In this paper, we address the problem of outdoor, appearance-based topological localization, particularly over long periods of time where seasonal changes alter the appearance of the environment. We investigate a straight-forward method that relies on local image features to compare single image pairs. We rst look into which of the dominating image feature algorithms, SIFT or the more recent SURF, that is most suitable for this task. We then ne-tune our localization algorithm in terms of accuracy, and also introduce the epipolar constraint to further improve the result. The nal localization algorithm is applied on multiple data sets, each consisting of a large number of panoramic images, which have been acquired over a period of nine months with large seasonal changes. The nal localization rate in the single-image matching, cross-seasonal case is between 80 to 95%.

  • 191.
    Valgren, Christoffer
    et al.
    Örebro University, Department of Technology.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    SIFT, SURF and seasons: long-term outdoor localization using local features2007In: Proceedings of the European Conference on Mobile Robots, ECMR (2007), 2007, p. 253-258Conference paper (Refereed)
    Abstract [en]

    Local feature matching has become a commonly used method to compare images. For mobile robots, a reliable method for comparing images can constitute a key component for localization and loop closing tasks. In this paper, we address the issues of outdoor appearance-based topological localization for a mobile robot over time. Our data sets, each consisting of a large number of panoramic images, have been acquired over a period of nine months with large seasonal changes (snowcovered ground, bare trees, autumn leaves, dense foliage, etc.). Two different types of image feature algorithms, SIFT and the more recent SURF, have been used to compare the images. We show that two variants of SURF, called U-SURF and SURF-128, outperform the other algorithms in terms of accuracy and speed.

  • 192.
    Valgren, Christoffer
    et al.
    Örebro University, Department of Technology.
    Lilienthal, Achim J.
    Örebro University, Department of Technology.
    Duckett, Tom
    University of Lincoln.
    Incremental topological mapping using omnidirectional vision2006In: 2006 IEEE/RSJ international conference on intelligent robots and systems, 2006, p. 3441-3447Conference paper (Refereed)
    Abstract [en]

    This paper presents an algorithm that builds topological maps, using omnidirectional vision as the only sensor modality. Local features are extracted from images obtained in sequence, and are used both to cluster the images into nodes and to detect links between the nodes. The algorithm is incremental, reducing the computational requirements of the corresponding batch algorithm. Experimental results in a complex, indoor environment show that the algorithm produces topologically correct maps, closing loops without suffering from perceptual aliasing or false links. Robustness to lighting variations was further demonstrated by building correct maps from combined multiple datasets collected over a period of 2 months.

  • 193.
    Vaskevicius, N.
    et al.
    Jacobs University, Bremen, Germany.
    Mueller, C. A.
    Jacobs University, Bremen, Germany.
    Bonilla, M.
    University of Pisa, Italy.
    Tincani, V.
    University of Pisa, Italy.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Fantoni, G.
    University of Pisa, Italy.
    Pathak, K.
    Jacobs University, Bremen, Germany.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Bicchi, A.
    University of Pisa, Italy.
    Birk, A.
    Jacobs University, Bremen, Germany.
    Object recognition and localization for robust grasping with a dexterous gripper in the context of container unloading2014Conference paper (Refereed)
    Abstract [en]

    The work presented here is embedded in research on an industrial application scenario, namely autonomous shipping-container unloading, which has several challenging constraints: the scene is very cluttered, objects can be much larger than in common table-top scenarios; the perception must be highly robust, while being as fast as possible. These contradicting goals force a compromise between speed and accuracy. In this work, we investigate a state of the art perception system integrated with a dexterous gripper. In particular, we are interested in pose estimation errors from the recognition module and whether these errors can be handled by the abilities of the gripper.

  • 194.
    Vuka, Mikel
    et al.
    Dipartitmento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Schmuker, Michael
    University of Hertfordshire, School of Computer Science, College Lane, Hatfield, Herts, United Kingdom.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Amigoni, Francesco
    Dipartitmento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy.
    Lilienthal, Achim J
    Örebro University, School of Science and Technology.
    Exploration and Localization of a Gas Source with MOX Gas Sensorson a Mobile Robot: A Gaussian Regression Bout Amplitude Approach2017In: 2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN 2017): Proceedings, IEEE, 2017, p. 164-166Conference paper (Refereed)
    Abstract [en]

    Mobile robot olfaction systems combine gas sensorswith mobility provided by robots. They relief humansof dull, dirty and dangerous tasks in applications such assearch & rescue or environmental monitoring. We address gassource localization and especially the problem of minimizingexploration time of the robot, which is a key issue due toenergy constraints. We propose an active search approach forrobots equipped with MOX gas sensors and an anemometer,given an occupancy map. Events of rapid change in the MOXsensor signal (“bouts”) are used to estimate the distance to agas source. The wind direction guides a Gaussian regression,which interpolates distance estimates. The contributions of thispaper are two-fold. First, we extend previous work on gassource distance estimation with MOX sensors and propose amodification to cope better with turbulent conditions. Second,we introduce a novel active search gas source localizationalgorithm and validate it in a real-world environment.

  • 195.
    Wandel, Michael
    et al.
    University of Tübingen, Tübingen, Germany.
    Lilienthal, Achim J.
    University of Tübingen, Tübingen, Germany.
    Duckett, Tom
    Örebro University, Department of Technology.
    Weimar, Udo
    University of Tübingen, Tübingen, Germany.
    Zell, Andreas
    University of Tübingen, Tübingen, Germany.
    Gas distribution in unventilated indoor environments inspected by a mobile robot2003In: Proceedings of the IEEE international conference on advanced robotics 2003, Coimbra, Portugal: University of Coimbra , 2003, Vol. 1-3, p. 507-512Conference paper (Refereed)
    Abstract [en]

    Gas source localisation with robots is usually performed in environments with a strong, unidirectional airflow created by artificial ventilation. This tends to create a strong, well defined analyte plume and enables upwind searching. By contrast, this paper presents experiments conducted in unventilated rooms. Here, the measured concentrations also indicate an analyte plume with, however, different properties concerning its shape, width, concentration profile and stability over time. In the results presented in this paper, two very different mobile robotic systems for odour sensing were investigated in different environments, and the similarities as well as differences in the analyte gas distributions measured are discussed.

  • 196.
    Wandel, Michael
    et al.
    University of Tübingen, Tübingen, Germany.
    Lilienthal, Achim J.
    University of Tübingen, Tübingen, Germany.
    Zell, Andreas
    University of Tübingen, Tübingen, Germany.
    Weimar, Udo
    University of Tübingen, Tübingen, Germany.
    Mobile robot using different senses2002In: Proceedings of the international symposium on olfaction and electronic nose: ISOEN 2002, 2002, p. 128-129Conference paper (Refereed)
  • 197.
    Wandel, Michael R.
    et al.
    University of Tübingen, Tübingen, Germany.
    Weimar, Udo
    University of Tübingen, Tübingen, Germany.
    Lilienthal, Achim J.
    University of Tübingen, Tübingen, Germany.
    Zell, Andreas
    University of Tübingen, Tübingen, Germany.
    Leakage localisation with a mobile robot carrying chemical sensors2001In: The 8th IEEE international conference on electronics, circuits and systems: ICECS 2001, Malta, Malta: IEEE, 2001, Vol. 3, p. 1247-1250, article id 957441Conference paper (Refereed)
    Abstract [en]

    On the way to developing an electronic watchman one more sense, i.e. gas sensing facilities, are added to an autonomous mobile robot. For the gas detection, up to eight metal oxide sensors are operated using a commercial sensor system. The robot is able to move and navigate autonomously. The geometric information is extracted from laser range finder data. This input is used to build up an internal map while driving. Using the new sensor the localisation of a gas source in unventilated in-house environments is performed. First experiments in a one-dimensional case show a very good correlation between the peak and the gas source. The one-dimensional concentration profile is repeatedly recorded and stable for at least two hours. The two-dimensional experiments exhibit a circulation of the air within the room due to temperature and hence density effects. The latter is limiting the available recording time for the two-dimensional mapping

  • 198.
    Wiedemann, Thomas
    et al.
    Institute of Communications and Navigation of the German Aerospace Center (DLR), Oberpfaffenhofen, Wessling, Germany.
    Manss, Christoph
    Institute of Communications and Navigation of the German Aerospace Center (DLR), Oberpfaffenhofen, Wessling, Germany.
    Shutin, Dmitriy
    Institute of Communications and Navigation of the German Aerospace Center (DLR), Oberpfaffenhofen, Wessling, Germany.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Karolj, Valentina
    Institute of Communications and Navigation of the German Aerospace Center (DLR), Oberpfaffenhofen, Wessling, Germany.
    Viseras, Alberto
    Institute of Communications and Navigation of the German Aerospace Center (DLR), Oberpfaffenhofen, Wessling, Germany.
    Probabilistic modeling of gas diffusion with partial differential equations for multi-robot exploration and gas source localization2017In: 2017 European Conference on Mobile Robots (ECMR), Institute of Electrical and Electronics Engineers (IEEE), 2017, article id 8098707Conference paper (Refereed)
    Abstract [en]

    Employing automated robots for sampling gas distributions and for localizing gas sources is beneficial since it avoids hazards for a human operator. This paper addresses the problem of exploring a gas diffusion process using a multi-agent system consisting of several mobile sensing robots. The diffusion process is modeled using a partial differential equation (PDE). It is assumed that the diffusion process is driven by only a few spatial sources at unknown locations with unknown intensity. The goal of the multi-robot exploration is thus to identify source parameters, in particular, their number, locations and magnitudes. Therefore, this paper develops a probabilistic approach towards PDE identification under sparsity constraint using factor graphs and a message passing algorithm. Moreover, the message passing schemes permits efficient distributed implementation of the algorithm. This brings significant advantages with respect to scalability, computational complexity and robustness of the proposed exploration algorithm. Based on the derived probabilistic model, an exploration strategy to guide the mobile agents in real time to more informative sampling locations is proposed. Hardware- in-the-loop experiments with real mobile robots show that the proposed exploration approach accelerates the identification of the source parameters and outperforms systematic sampling.

  • 199.
    Wiedemann, Thomas
    et al.
    Institute of Communications and Navigation German Aerospace Center (DLR), Wessling, Germany.
    Shutin, Dmitri
    Institute of Communications and Navigation German Aerospace Center (DLR), Wessling, Germany.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Bayesian Gas Source Localization and Exploration with a Multi-Robot System Using Partial Differential Equation Based Modeling2017In: 2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN 2017): Proceedings, 2017, p. 122-124Conference paper (Refereed)
    Abstract [en]

    Here we report on active water sampling devices forunderwater chemical sensing robots. Crayfish generate jetlikewater currents during food search by waving theflagella of their maxillipeds. The jets generated toward theirsides induce an inflow from the surroundings to the jets.Odor sample collection from the surroundings to theirolfactory organs is promoted by the generated inflow.Devices that model the jet discharge of crayfish have beendeveloped to investigate the effectiveness of the activechemical sampling. Experimental results are presented toconfirm that water samples are drawn to the chemicalsensors from the surroundings more rapidly by using theaxisymmetric flow field generated by the jet discharge thanby centrosymmetric flow field generated by simple watersuction. Results are also presented to show that there is atradeoff between the angular range of chemical samplecollection and the sample collection time.

  • 200.
    Xing, Yuxin
    et al.
    School of Engineering, University of Warwick, Coventry, UK.
    Vincent, Timothy A.
    School of Engineering, University of Warwick, Coventry, UK.
    Cole, Marina
    School of Engineering, University of Warwick, Coventry, UK.
    Gardner, Julian W.
    School of Engineering, University of Warwick, Coventry, UK.
    Fan, Han
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Mobile robot multi-sensor unit for unsupervised gas discrimination in uncontrolled environments2017In: IEEE SENSORS 2017: Conference Proceedings, IEEE, 2017, p. 1-3Conference paper (Refereed)
    Abstract [en]

    In this work we present a novel multi-sensor unit to detect and discriminate unknown gases in uncontrolled environments. The unit includes three metal oxide (MOX) sensors with CMOS micro heaters, a plasmonic enhanced non-dispersive infra-red (NDIR) sensor, a commercial temperature humidity sensor, and a flow sensor. The proposed sensing unit was evaluated with plumes of gases (propanol, ethanol and acetone) in both, a laboratory setup on a gas testing bench and on-board a mobile robot operating in an indoor workshop. It offers significantly improved performance compared to commercial systems, in terms of power consumption, response time and physical size. We verified the ability to discriminate gases in an unsupervised manner, with data collected on the robot and high accuracy was obtained in the classification of propanol versus acetone (96%), and ethanol versus acetone (90%).

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