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  • 1.
    Gholami Shahbandi, Saeed
    et al.
    Center for Applied Intelligent Systems Research, Halmstad University, Halmstad, Sweden.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Iagnemma, Karl
    Robotic Mobility Group, Massachusetts Institute of Technology, Cambridge MA, USA.
    Nonlinear Optimization of Multimodal Two-Dimensional Map Alignment With Application to Prior Knowledge Transfer2018In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 3, no 3, p. 2040-2047Article in journal (Refereed)
    Abstract [en]

    We propose a method based on a nonlinear transformation for nonrigid alignment of maps of different modalities, exemplified with matching partial and deformed two-dimensional maps to layout maps. For two types of indoor environments, over a dataset of 40 maps, we have compared the method to state-of-the-art map matching and nonrigid image registration methods and demonstrate a success rate of 80.41% and a mean point-to-point alignment error of 1.78 m, compared to 31.9% and 10.7 m for the best alternative method. We also propose a fitness measure that can quite reliably detect bad alignments. Finally, we show a use case of transferring prior knowledge (labels/segmentation), demonstrating that map segmentation is more consistent when transferred from an aligned layout map than when operating directly on partial maps (95.97% vs. 81.56%).

  • 2.
    Hernandez Bennetts, Victor
    et al.
    Örebro University, School of Science and Technology.
    Kucner, Tomasz Piotr
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Neumann, Patrick P.
    Bundesanstalt für Materialforschung und -prüfung, Berlin, Germany.
    Fan, Han
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Probabilistic Air Flow Modelling Using Turbulent and Laminar Characteristics for Ground and Aerial Robots2017In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 2, no 2, p. 1117-1123Article in journal (Refereed)
    Abstract [en]

    For mobile robots that operate in complex, uncontrolled environments, estimating air flow models can be of great importance. Aerial robots use air flow models to plan optimal navigation paths and to avoid turbulence-ridden areas. Search and rescue platforms use air flow models to infer the location of gas leaks. Environmental monitoring robots enrich pollution distribution maps by integrating the information conveyed by an air flow model. In this paper, we present an air flow modelling<?brk?> algorithm that uses wind data collected at a sparse number of locations to estimate joint probability distributions over wind speed and direction at given query locations. The algorithm uses a novel extrapolation approach that models the air flow as a linear combination of laminar and turbulent components. We evaluated the prediction capabilities of our algorithm with data collected with an aerial robot during several exploration runs. The results show that our algorithm has a high degree of stability with respect to parameter selection while outperforming conventional extrapolation approaches. In addition, we applied our proposed approach in an industrial application, where the characterization of a ventilation system is supported by a ground mobile robot. We compared multiple air flow maps recorded over several months by estimating stability maps using the Kullback&ndash;Leibler divergence between the distributions. The results show that, despite local differences, similar air flow patterns prevail over time. Moreover, we corroborated the validity of our results with knowledge from human experts.

  • 3.
    Kucner, Tomasz Piotr
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor Manuel
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Enabling Flow Awareness for Mobile Robots in Partially Observable Environments2017In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 2, no 2, p. 1093-1100Article in journal (Refereed)
    Abstract [en]

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

  • 4.
    Lagriffoul, Fabien
    et al.
    Örebro University, School of Science and Technology.
    Dantam, Neil T.
    Colorado School of Mines, Golden CO, USA.
    Garrett, Caelan
    Massachusetts Institute of Technology, Cambridge MA, USA.
    Akbari, Aliakbar
    Universidad Politécnica de Catalunya, Barcelona, Spain.
    Srivastava, Siddharth
    Arizona State University, Tempe AZ, USA.
    Kavraki, Lydia E.
    Rice University, Houston TX, USA .
    Platform-Independent Benchmarks for Task and Motion Planning2018In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 3, no 4, p. 3765-3772Article in journal (Refereed)
    Abstract [en]

    We present the first platform-independent evaluation method for task and motion planning (TAMP). Previously point, various problems have been used to test individual planners for specific aspects of TAMP. However, no common set of metrics, formats, and problems have been accepted by the community. We propose a set of benchmark problems covering the challenging aspects of TAMP and a planner-independent specification format for these problems. Our objective is to better evaluate and compare TAMP planners, foster communication, and progress within the field, and lay a foundation to better understand this class of planning problems.

  • 5.
    Lowry, Stephanie
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lightweight, Viewpoint-Invariant Visual Place Recognition in Changing Environments2018In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 3, no 2, p. 957-964Article in journal (Refereed)
    Abstract [en]

    This paper presents a viewpoint-invariant place recognition algorithm which is robust to changing environments while requiring only a small memory footprint. It demonstrates that condition-invariant local features can be combined with Vectors of Locally Aggregated Descriptors (VLAD) to reduce high-dimensional representations of images to compact binary signatures while retaining place matching capability across visually dissimilar conditions. This system provides a speed-up of two orders of magnitude over direct feature matching, and outperforms a bag-of-visual-words approach with near-identical computation speed and memory footprint. The experimental results show that single-image place matching from non-aligned images can be achieved in visually changing environments with as few as 256 bits (32 bytes) per image.

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