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  • 1.
    Olofsson, Jonatan
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Intergrated Computer systems. Linköping University, The Institute of Technology.
    Towards Autonomous Landing of a Quadrotorusing Monocular SLAM Techniques2012Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Use of Unmanned Aerial Vehicles have seen enormous growth in recent years due to the advances in related scientific and technological fields. This fact combined with decreasing costs of using UAVs enables their use in new application areas. Many of these areas are suitable for miniature scale UAVs - Micro Air Vehicles(MAV) - which have the added advantage of portability and ease of deployment. One of the main functionalities necessary for successful MAV deployment in real-world applications is autonomous landing. Landing puts particularly high requirements on positioning accuracy, especially in indoor confined environments where the common global positioning technology is unavailable. For that reason using an additional sensor, such as a camera, is beneficial. In this thesis, a set of technologies for achieving autonomous landing is developed and evaluated. In particular, state estimation based on monocular vision SLAM techniques is fused with data from onboard sensors. This is then used as the basis for nonlinear adaptive control as well trajectory generation for a simple landing procedure. These components are connected using a new proposed framework for robotic development. The proposed system has been fully implemented and tested in a simulated environment and validated using recorded data. Basic autonomous landing was performed in simulation and the result suggests that the proposed system is a viable solution for achieving a fully autonomous landing of a quadrotor.

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    MonoSLAMLanding.pdf
  • 2.
    Olofsson, Jonatan
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Forssén, Tomas
    Recco.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. Linköping University.
    Skog, Isaac
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    UAS-supported Digitalized Search-And-Rescue using Harmonic Radar Reflection2020In: Proceedings of 2020 IEEE Aerospace Conference, IEEE, 2020Conference paper (Refereed)
    Abstract [en]

    Search-And-Rescue (SAR) is one of manyfields with applications benefiting from the increasingavailability of Unmanned Aerial Systems (UASs).  Most UAS applications rely on the UAS’s capabilityto carry a camera and stream video data for manualor automated processing. However, this relies onunobstructed views of the target, which limits the applicability of these systems.  In this paper, we instead describe the development and initial application testing of a system with a UAS-carried harmonic radar. This sensor is designed to detect the presence of Recco radar reflectors, commonly found integrated into alpine clothes and gear. The reflectors can be detected through vegetation and snow and is independent of many external factors such as lighting conditions. The paper describesthe system design and provides initial real-world results. The initial tests show fruitful results and opens up several avenues of continued research and development.

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    fulltext
  • 3.
    Olofsson, Jonatan
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Maas, Deran
    ABB Future Labs Switzerland, ABB Switzerland Ltd., Baden-Dätwill, Switzerland.
    Marano, Stefano
    ABB Future Labs Switzerland, ABB Switzerland Ltd., Baden-Dätwill, Switzerland.
    GNSS-Free Maritime Navigation using Radar and Digital Elevation Models2020In: Proceedings 2020 IEEE 23rd International Conference on Information Fusion (FUSION), Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 789-796Conference paper (Refereed)
    Abstract [en]

    Modern maritime navigation is heavily dependent on satellite systems. Availability of an accurate position is critical for safe operations, but satellite-based navigation systems are vulnerable to interference, jamming, and spoofing. In this work, we propose a method for maritime navigation independent of GNSS, able to provide absolute positioning of the vessel based on marine radar scans.

    A measurement model is presented where a Digital Elevation Model is used to predict the output of a marine radar, given a hypothetical position. The model, as used by an on-line particle filter, is used to track the movements of a ship from real recorded data. This demonstrates the feasibility of this method for robust positioning, without the need of external positioning signals, in a maritime environment. The tracking only uses sensors commonly available on maritime vessels, and demonstrates its application using freely available elevation data.

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    fulltext
  • 4.
    Olofsson, Jonatan
    et al.
    Norwegian Univ Sci and Technol, Norway.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Lauknes, Tom Rune
    Norut, Norway.
    Johansen, Tor Arne
    Norwegian Univ Sci and Technol, Norway.
    Multi-agent informed path planning using the probability hypothesis density2020In: Autonomous Robots, ISSN 0929-5593, E-ISSN 1573-7527, Vol. 44, p. 913-925Article in journal (Refereed)
    Abstract [en]

    An Informed Path Planning algorithm for multiple agents is presented. It can be used to efficiently utilize available agents when surveying large areas, when total coverage is unattainable. Internally the algorithm has a Probability Hypothesis Density (PHD) representation, inspired by modern multi-target tracking methods, to represent unseen objects. Using the PHD, the expected number of observed objects is optimized. In a sequential manner, each agent maximizes the number of observed new targets, taking into account the probability of undetected objects due to previous agents actions and the probability of detection, which yields a scalable algorithm. Algorithm properties are evaluated in simulations, and shown to outperform a greedy base line method. The algorithm is also evaluated by applying it to a sea ice tracking problem, using two datasets collected in the Arctic, with reasonable results. An implementation is provided under an Open Source license.

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    fulltext
  • 5. Olofsson, Jonatan
    et al.
    Lindahl Flåten, Andreas
    Veibäck, Clas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Lauknes, Tom Rune
    Gaussian Field Current Estimation from Drift Sea Ice Tracking with the Labeled Multi-Bernoulli Filter2017In: Proceedings of the 2017 OCEANS 17 conference, 2017Conference paper (Refereed)
    Abstract [en]

    In polar region operations, drift sea ice positioning and tracking is useful for both scientific and safety reasons. Modeling ice movements has proven difficult, not least due to the lack of information of currents and winds of high enough resolution. Thus, observations of drift ice is essential to an up-to-date ice-tracking estimate.

    As an inverse problem, it is possible to extract current and wind estimates from the tracked objects of a Multi-Target Tracking (MTT) filter. By inserting the track estimates into a Gaussian field, we obtain a two-dimensional current estimate over a region of interest.

    The algorithm is applied to a Terrestrial Radar Interferometer (TRI) dataset from Kongsfjorden, Svalbard, to show the practical application of the current estimation.

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    fulltext
  • 6.
    Olofsson, Jonatan
    et al.
    Norwegian University of Science and Technology, Norway.
    Veibäck, Clas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Sea Ice Tracking with a Spatially Indexed Labeled Multi-Bernoulli Filter2017In: Proceedings of the 2017 20th International Conference on Information Fusion, Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 376-383Conference paper (Refereed)
    Abstract [en]

    In polar region operations, drift ice positioning and tracking is useful for both scientific and safety reasons. At its core is a Multi-Target Tracking (MTT) problem in which currents and winds make motion modeling difficult. One recent algorithm in the MTT field, employed in this paper, is the Labeled Multi-Bernoulli (LMB) filter. In particular, a proposed reformulation of the LMB equations exposes a structure which is exploited to propose a compact algorithm for the generation of the filter's posterior distribution. Further, spatial indexing is applied to the clustering process of the filter, allowing efficient separation of the filter into smaller, independent parts with lesser total complexity than that of an unclustered filter. Many types of sensors can be employed to generate detections of sea ice, and in this paper a recorded dataset from a Terrestrial Radar Interferometer (TRI) is used to demonstrate the application of the Spatially Indexed Labeled Multi-Bernoulli filter to estimate the currents of an observed area in Kongsfjorden, Svalbard.

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    Sea Ice Tracking with a Spatially Indexed Labeled Multi-Bernoulli Filter
  • 7. Olofsson, Jonatan
    et al.
    Veibäck, Clas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Johansen, Tor Arne
    Outline of a System for Integrated Adaptive Ice Tracking and Multi-Agent Path Planning2017In: Proceedings of the 2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS), IEEE, 2017, p. 13-18Conference paper (Refereed)
    Abstract [en]

    In polar region operations, drift sea ice positioning and tracking is useful for both scientific and safety reasons. Modeling ice movements has proven difficult, not least due to the lack of information of currents and winds of high enough resolution. Thus, observations of drift ice is essential to an up-to-date ice-tracking estimate.

    Recent years have seen the rise of Unmanned Aerial Systems (UAS) as a platform for geoobservation, and so too for the tracking of sea ice. Being a mobile platform, the research on UAS path-planning is extensive and usually involves an objective-function to minimize. For the purpose of observation however, the objective-function typically changes as observations are made along the path.

    Further, the general problem involves multiple UAS and—in the case of sea ice tracking—vast geographical areas.

    In this paper we discuss the architectural outline of a system capable of fusing data from multiple sources—UAS’s and others—as well as incorporating that data for both path-planning, sea ice movement prediction and target initialization. The system contains tracking of sea ice objects, situation map logic and is expandable as discussed with path-planning capabilities for closing the loop of optimizing paths for information acquisition.

    Download full text (pdf)
    fulltext
  • 8.
    Veibäck, Clas
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Olofsson, Jonatan
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Lauknes, Tom Rune
    Norwegian Res Ctr, Norway.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Learning Target Dynamics While Tracking Using Gaussian Processes2020In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 56, no 4, p. 2591-2602Article in journal (Refereed)
    Abstract [en]

    Tracked targets often exhibit common behaviors due to influences from the surrounding environment, such as wind or obstacles, which are usually modeled as noise. Here, these influences are modeled using sparse Gaussian processes that are learned online together with the state inference using an extended Kalman filter. The method can also be applied to time-varying influences and identify simple dynamic systems. The method is evaluated with promising results in a simulation and a real-world application.

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    fulltext
1 - 8 of 8
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