Change search
Refine search result
1 - 17 of 17
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Axholt, Magnus
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Skoglund, Martin A.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    O’Connell, Stephen D.
    Swedish Air Force Combat Simulation Center at the Swedish Defence Research Agency.
    Cooper, Matthew D.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Ellis, Stephen R.
    Human Systems Integration Division at NASA Ames Research Center.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Accuracy of Eyepoint Estimation in Optical See-Through Head-Mounted Displays Using the Single Point Active Alignment Method2011Conference paper (Other academic)
    Abstract [en]

    This paper studies the accuracy of the estimated eyepoint of an Optical See-Through Head-Mounted Display (OST HMD) calibrated using the Single Point Active Alignment Method (SPAAM). Quantitative evaluation of calibration procedures for OST HMDs is complicated as it is currently not possible to share the subject’s view. Temporarily replacing the subject’s eye with a camera during the calibration or evaluation stage has been proposed, but the uncertainty of a correct eyepoint estimation remains. In the experiment reported in this paper, subjects were used for all stages of calibration and the results were verified with a 3D measurement device. The nine participants constructed 25 visual alignments per calibration after which the estimated pinhole camera model was decomposed into its intrinsic and extrinsic parameters using two common methods. Unique to this experiment, compared to previous evaluations, is the measurement device used to cup the subject’s eyeball. It measures the eyepoint location relative to the head tracker, thereby establishing the calibration accuracy of the estimated eyepoint location. As the results on accuracy are expressed as individual pinhole camera parameters, rather than a compounded registration error, this paper complements  previously published work on parameter variance as the former denotes bias and the latter represents noise. Results indicate that the calibrated eyepoint is on average 5 cm away from its measured location and exhibits a vertical bias which potentially causes dipvergence for stereoscopic vision for objects located further away than 5.6 m. Lastly, this paper closes with a discussion on the suitability of the traditional pinhole camera model for OST HMD calibration.

  • 2.
    Axholt, Magnus
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Skoglund, Martin
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    O'Connell, Stephen
    Swedish Defence Research Agency.
    Cooper, Matthew
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Ellis, Stephen
    NASA Ames Research Center.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Parameter Estimation Variance of the Single Point Active Alignment Method in Optical See-Through Head Mounted Display Calibration2011In: Proceedings of the IEEE Virtual Reality Conference / [ed] Michitaka Hirose, Benjamin Lok, Aditi Majumder and Dieter Schmalstieg, Piscataway, NJ, USA: IEEE , 2011, p. 27-24Conference paper (Refereed)
    Abstract [en]

    The parameter estimation variance of the Single Point Active Alignment Method (SPAAM) is studied through an experiment where 11 subjects are instructed to create alignments using an Optical See-Through Head Mounted Display (OSTHMD) such that three separate correspondence point distributions are acquired. Modeling the OSTHMD and the subject's dominant eye as a pinhole camera, findings show that a correspondence point distribution well distributed along the user's line of sight yields less variant parameter estimates. The estimated eye point location is studied in particular detail. Thefindings of the experiment are complemented with simulated datawhich show that image plane orientation is sensitive to the numberof correspondence points. The simulated data also illustrates someinteresting properties on the numerical stability of the calibrationproblem as a function of alignment noise, number of correspondencepoints, and correspondence point distribution.

  • 3.
    Axholt, Magnus
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA).
    Skoglund, Martin
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Peterson, Stephen
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA).
    Cooper, Matthew
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA).
    Schön, Thomas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA).
    Ellis, Stephen
    NASA Ames Research Center, USA.
    Optical See-Through Head Mounted Display: Direct Linear Transformation Calibration Robustness in the Presence of User Alignment Noise2010Report (Other academic)
    Abstract [en]

    The correct spatial registration between virtual and real objects in optical see-through augmented reality implies accurate estimates of the user’s eyepoint relative to the location and orientation of the display surface. A common approach is to estimate the display parameters through a calibration procedure involving a subjective alignment exercise. Human postural sway and targeting precision contribute to imprecise alignments, which in turn adversely affect the display parameter estimation resulting in registration errors between virtual and real objects. The technique commonly used has its origin incomputer vision, and calibrates stationary cameras using hundreds of correspondence points collected instantaneously in one video frame where precision is limited only by pixel quantization and image blur. Subsequently the input noise level is several order of magnitudes greater when a human operator manually collects correspondence points one by one. This paper investigates the effect of human alignment noise on view parameter estimation in an optical see-through head mounted display to determine how well astandard camera calibration method performs at greater noise levels than documented in computer vision literature. Through Monte-Carlo simulations we show that it is particularly difficult to estimate the user’s eyepoint in depth, but that a greater distribution of correspondence points in depth help mitigate the effects of human alignment noise.

  • 4.
    Axholt, Magnus
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Skoglund, Martin
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Peterson, Stephen
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Cooper, Matthew
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Schön, Thomas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Ellis, Stephen
    NASA Ames Research Center, USA.
    Optical See-Through Head Mounted Display: Direct Linear Transformation Calibration Robustness in the Presence of User Alignment Noise2010In: Proceedings of the 54th Annual Meeting of the Human Factors and Ergonomics Society, 2010Conference paper (Refereed)
    Abstract [en]

    The correct spatial registration between virtual and real objects in optical see-through augmented reality implies accurate estimates of the user’s eyepoint relative to the location and orientation of the display surface. A common approach is to estimate the display parameters through a calibration procedure involving a subjective alignment exercise. Human postural sway and targeting precision contribute to imprecise alignments, which in turn adversely affect the display parameter estimation resulting in registration errors between virtual and real objects. The technique commonly used has its origin incomputer vision, and calibrates stationary cameras using hundreds of correspondence points collected instantaneously in one video frame where precision is limited only by pixel quantization and image blur. Subsequently the input noise level is several order of magnitudes greater when a human operator manually collects correspondence points one by one. This paper investigates the effect of human alignment noise on view parameter estimation in an optical see-through head mounted display to determine how well astandard camera calibration method performs at greater noise levels than documented in computer vision literature. Through Monte-Carlo simulations we show that it is particularly difficult to estimate the user’s eyepoint in depth, but that a greater distribution of correspondence points in depth help mitigate the effects of human alignment noise.

  • 5.
    Callmer, Jonas
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Skoglund, Martin
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Silent Localization of Underwater Sensors Using Magnetometers2010In: EURASIP Journal on Advances in Signal Processing, ISSN 1687-6172, E-ISSN 1687-6180, Vol. 2010, no 1Article in journal (Refereed)
    Abstract [en]

    Sensor localization is a central problem for sensor networks. If the sensor positions are uncertain, the target tracking ability of the sensor network is reduced. Sensor localization in underwater environments is traditionally addressed using acoustic range measurements involving known anchor or surface nodes. We explore the usage of triaxial magnetometers and a friendly vessel with known magnetic dipole to silently localize the sensors. The ferromagnetic field created by the dipole is measured by the magnetometers and is used to localize the sensors. The trajectory of the vessel and the sensor positions are estimated simultaneously using an Extended Kalman Filter (EKF). Simulations show that the sensors can be accurately positioned using magnetometers.

  • 6.
    Lundquist, Christian
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Skoglund, Martin
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Granström, Karl
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Glad, Torkel
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Insights from Implementing a System for Peer-Review2013In: IEEE Transactions on Education, ISSN 0018-9359, E-ISSN 1557-9638, Vol. 56, no 3, p. 261-267Article in journal (Refereed)
    Abstract [en]

    Courses at the Master’s level in automatic control and signal processing cover mathematical theories and algorithms for control, estimation, and filtering. However, giving students practical experience in how to use these algorithms is also an important part of these courses. A goal is that the students should not only be able to understand and derive these algorithms, but also be able to apply them to real-life technical problems. The latter is achieved by assigning more time to the laboratory tutorials and designing them in such a way that the exercises are open for interpretation; an example of this would be giving the students more freedom to decide how to acquire the data needed to solve the given exercises.The students are asked to hand in a laboratory report in which they describe how they solved the exercises. This paper presents a double-blind peer-review process for laboratory reports, introduced at the Department of Electrical Engineering, Linköping University, Sweden. A survey was administered to students, and the results are summarized in this paper. Also discussed are the teachers’ experiences of peer review and of how students perform later in their education in writing their Master’s theses.

  • 7.
    Nilsson, Martin
    et al.
    Swedish Defence Research Agency (FOI), Linköping, Sweden.
    Rantakokko, Jouni
    Swedish Defence Research Agency (FOI), Linköping, Sweden; KTH Royal Institute of Technology, Stockholm, Sweden.
    Skoglund, Martin A.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hendeby, Gustaf
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Swedish Defence Research Agency (FOI), Linköping, Sweden.
    Indoor Positioning Using Multi-Frequency RSS with Foot-Mounted INS2014In: Fifth International Conference on Indoor Positioning and Indoor Navigation, Institute of Electrical and Electronics Engineers (IEEE), 2014Conference paper (Refereed)
    Abstract [en]

    This paper presents a system which combines a zero-velocity-update-(ZUPT-)aided inertial navigation system (INS), using a foot-mounted inertial measurement unit (IMU), with opportunistic use of multi-frequency received signal strength (RSS) measurements. The system does not rely on maps or pre-collected data from surveys of the radio-frequency (RF) environment. Instead it builds its own database of collected RSS measurements during the course of the operation. New RSS measurements are continuously compared with the stored values in the database, and when the user returns to a previously visited area this can thus be detected. This enables loop-closures to be detected online and used for error drift correction. The system utilises a distributed particle simultaneous localization and mapping (DP-SLAM) algorithm which provides a flexible 2D navigation platform that can be extended with more sensors. The experimental results presented in this paper indicates that the developed RSS SLAM algorithm can, in many cases, significantly improve the positioning performance of a foot-mounted INS. 

  • 8.
    Nyqvist, Hanna E.
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Skoglund, Martin A.
    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.
    Pose Estimation Using Monocular Vision and Inertial Sensors Aided with Ultra Wide Band2015In: International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2015, IEEE , 2015Conference paper (Refereed)
    Abstract [en]

    This paper presents a method for global pose estimation using inertial sensors, monocular vision, and ultra wide band (UWB) sensors. It is demonstrated that the complementary characteristics of these sensors can be exploited to provide improved global pose estimates, without requiring the introduction of any visible infrastructure, such as fiducial markers. Instead, natural landmarks are jointly estimated with the pose of the platform using a simultaneous localization and mapping framework, supported by a small number of easy-to-hide UWB beacons with known positions. The method is evaluated with data from a controlled indoor experiment with high precision ground truth. The results show the benefit of the suggested sensor combination and suggest directions for further work.

  • 9.
    Sjanic, Zoran
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. Saab AB, Linköping, Sweden .
    Skoglund, Martin A.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Prediction Error Method Estimation for Simultaneous Localisation and Mapping2016In: Proceedings of the 19th International Conference on Information Fusion (FUSION), July 4-8 2016., Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 927-934Conference paper (Refereed)
    Abstract [en]

    This paper presents a batch estimation method for Simultaneous Localization and Mapping (SLAM) using the Prediction Error Method (PEM). The estimation problem considers landmarks as parameter while treating dynamics using state space models. The gradient needed for parameter estimation is computed recursively using an Extended Kalman Filter (EKF). Results using simulations with a monocular camera and inertial sensors are presented and compared to a Nonlinear Least- Squares (NLS) estimator. The presented method produce both lower RMSE’s and scale better to the batch length. 

  • 10.
    Sjanic, Zoran
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control.
    Skoglund, Martin A.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control.
    Gustafsson, Fredrik
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control.
    EM-SLAM with Inertial/Visual Applications2017In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 53, no 1, p. 273-285Article in journal (Refereed)
    Abstract [en]

    The general Simultaneous Localisation and Mapping (SLAM) problem aims at estimating the state of a moving platform simultaneously with building a map of the local environment. There are essentially three classes of algorithms. EKF- SLAM and FastSLAM solve the problem on-line, while Nonlinear Least Squares (NLS) is a batch method. All of them scales badly with either the state dimension, the map dimension or the batch length. We investigate the EM algorithm for solving a generalized version of the NLS problem. This EM-SLAM algorithm solves two simpler problems iteratively, hence it scales much better with dimensions. The iterations switch between state estimation, where we propose an Extended Rauch-Tung-Striebel smoother, and map estimation, where a quasi-Newton method is suggested. The proposed method is evaluated in real experiments and also in simulations on a platform with a monocular camera attached to an inertial measurement unit. It is demonstrated to produce lower RMSE than with a standard Levenberg-Marquardt solver of NLS problem, at a computational cost that increases considerably slower. 

  • 11.
    Sjanic, Zoran
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Skoglund, Martin A.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Schön, Thomas B.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    A Nonlinear Least-Squares Approach to the SLAM Problem2011In: Proceedings of the 18th IFAC World Congress, 2011: World Congress, Volume # 18, Part 1 / [ed] Sergio Bittanti, Angelo Cenedese and Sandro Zampieri, IFAC Papers Online, 2011, p. 4759-4764Conference paper (Refereed)
    Abstract [en]

    In this paper we present a solution to the simultaneous localisation and mapping (SLAM) problem using a camera and inertial sensors. Our approach is based on the maximum a posteriori (MAP) estimate of the complete SLAM problem. The resulting problem is posed in a nonlinear least-squares framework which we solve with the Gauss-Newton method. The proposed algorithm is evaluated on experimental data using a sensor platform mounted on an industrial robot. In this way, accurate ground truth is available, and the results are encouraging.

  • 12.
    Skoglund, Martin
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Inertial Navigation and Mapping for Autonomous Vehicles2014Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Navigation and mapping in unknown environments is an important building block for increased autonomy of unmanned vehicles, since external positioning systems can be susceptible to interference or simply being inaccessible. Navigation and mapping require signal processing of vehicle sensor data to estimate motion relative to the surrounding environment and to simultaneously estimate various properties of the surrounding environment. Physical models of sensors, vehicle motion and external influences are used in conjunction with statistically motivated methods to solve these problems. This thesis mainly addresses three navigation and mapping problems which are described below.

    We study how a vessel with known magnetic signature and a sensor network with magnetometers can be used to determine the sensor positions and simultaneously determine the vessel's route in an extended Kalman filter (EKF). This is a so-called simultaneous localisation and mapping (SLAM) problem with a reversed measurement relationship.

    Previously determined hydrodynamic models for a remotely operated vehicle (ROV) are used together with the vessel's sensors to improve the navigation performance using an EKF. Data from sea trials is used to evaluate the system and the results show that especially the linear velocity relative to the water can be accurately determined.

    The third problem addressed is SLAM with inertial sensors, accelerometers and gyroscopes, and an optical camera contained in a single sensor unit. This problem spans over three publications.

    We study how a SLAM estimate, consisting of a point cloud map, the sensor unit's three dimensional trajectory and speed as well as its orientation, can be improved by solving a nonlinear least-squares (NLS) problem. NLS minimisation of the predicted motion error and the predicted point cloud coordinates given all camera measurements is initialised using EKF-SLAM.

    We show how NLS-SLAM can be initialised as a sequence of almost uncoupled problems with simple and often linear solutions. It also scales much better to larger data sets than EKF-SLAM. The results obtained using NLS-SLAM are significantly better using the proposed initialisation method than if started from arbitrary points. A SLAM formulation using the expectation maximisation (EM) algorithm is proposed. EM splits the original problem into two simpler problems and solves them iteratively. Here the platform motion is one problem and the landmark map is the other. The first problem is solved using an extended Rauch-Tung-Striebel smoother while the second problem is solved with a quasi-Newton method. The results using EM-SLAM are better than NLS-SLAM both in terms of accuracy and complexity.

  • 13.
    Skoglund, Martin A.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Visual Inertial Navigation and Calibration2011Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Processing and interpretation of visual content is essential to many systems and applications. This requires knowledge of how the content is sensed and also what is sensed. Such knowledge is captured in models which, depending on the application, can be very advanced or simple. An application example is scene reconstruction using a camera; if a suitable model of the camera is known, then a model of the scene can be estimated from images acquired at different, unknown, locations, yet, the quality of the scene model depends on the quality of the camera model. The opposite is to estimate the camera model and the unknown locations using a known scene model. In this work, two such problems are treated in two rather different applications.

    There is an increasing need for navigation solutions less dependent on external navigation systems such as the Global Positioning System (GPS). Simultaneous Localisation and Mapping (slam) provides a solution to this by estimating both navigation states and some properties of the environment without considering any external navigation systems.

    The first problem considers visual inertial navigation and mapping using a monocular camera and inertial measurements which is a slam problem. Our aim is to provide improved estimates of the navigation states and a landmark map, given a slam solution. To do this, the measurements are fused in an Extended Kalman Filter (ekf) and then the filtered estimates are used as a starting solution in a nonlinear least-squares problem which is solved using the Gauss-Newton method. This approach is evaluated on experimental data with accurate ground truth for reference.

    In Augmented Reality (ar), additional information is superimposed onto the surrounding environment in real time to reinforce our impressions. For this to be a pleasant experience it is necessary to have a good models of the ar system and the environment.

    The second problem considers calibration of an Optical See-Through Head Mounted Display system (osthmd), which is a wearable ar system. We show and motivate how the pinhole camera model can be used to represent the osthmd and the user’s eye position. The pinhole camera model is estimated using the Direct Linear Transformation algorithm. Results are evaluated in experiments which also compare different data acquisition methods.

  • 14.
    Skoglund, Martin A.
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Jönsson, Kenny
    Saab Group, Linköping, Sweden.
    Fredrik, Gustafsson
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Modeling and Sensor Fusion of a Remotely Operated Underwater Vehicle2012In: Proceedings of the 15th International Conference on Information Fusion (FUSION), 2012, IEEE , 2012, p. 947-954Conference paper (Refereed)
    Abstract [en]

    We compare dead-reckoning of underwater vehicles based on inertial sensors and kinematic models on one hand, and control inputs and hydrodynamic model on the other hand. Both can be used in an inertial navigation system to provide relative motion and absolute orientation of the vehicle. The combination of them is particularly useful for robust navigation in the case of missing data from the crucial doppler log speedometer. As a concrete result, we demonstrate that the performance critical doppler log can be replaced with longitudinal dynamics in the case of missing data, based on field test data of a remotely operated vehicle.

  • 15.
    Skoglund, Martin
    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.
    Axehill, Daniel
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Extended Kalman Filter Modifications Based on an Optimization View Point2015In: 18th International Conference of Information Fusion, Institute of Electrical and Electronics Engineers (IEEE), 2015Conference paper (Refereed)
    Abstract [en]

    The extended Kalman filter (EKF) has been animportant tool for state estimation of nonlinear systems sinceits introduction. However, the EKF does not possess the same optimality properties as the Kalman filter, and may perform poorly. By viewing the EKF as an optimization problem it is possible to, in many cases, improve its performance and robustness. The paper derives three variations of the EKF by applying different optimisation algorithms to the EKF costfunction and relate these to the iterated EKF. The derived filters are evaluated in two simulation studies which exemplify the presented filters.

  • 16.
    Skoglund, Martin
    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.
    Nygårds, Jonas
    Swedish Defence Research Agency (FOI).
    Rantakokko, Jouni
    Swedish Defece Research Agency (FOI).
    Eriksson, Gunnar
    Swedish Defence Research Agency (FOI).
    Indoor Localization Using Multi-Frequency RSS2016In: Proceddings of the IEEE/ION Position Location and Navigation Symposium, IEEE conference proceedings, 2016, p. 177-186Conference paper (Refereed)
    Abstract [en]

    This paper investigates the usefulness of multi-frequency received signal strength (RSS) for indoor localization. Acollected set of data from four sites containing 7 frequencies fromdual receivers and a high accuracy reference positioning systemis presented. The collected data is also made publicly availablethrough ResearchGate. The data is analyzed with respect tospatial variations using Gaussian processes ( GP ). The resultsshow that there are more rapid signal variations across corridorsthan along them. The uniqueness of RSS fingerprints is analyzedsuggesting that sequences of measurements in smoothing, orsmoothing-like, algorithms that can handle temporary positionambiguities are likely the best choice for localization applications.

  • 17.
    Skoglund, Martin
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Sjanic, Zoran
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Initialisation and Estimation Methods for Batch Optimisation of Inertial/Visual SLAM2013Report (Other academic)
    Abstract [en]

    Simultaneous Localisation and Mapping (SLAM) denotes the problem of jointly localizing a moving platform and mapping the environment. This work studies the SLAM problem using a combination of inertial sensors, measuring the platform's accelerations and angular velocities, and a monocular camera observing the environment. We formulate the SLAM problem on a nonlinear least squares (NLS) batch form, whose solution provides a smoothed estimate of the motion and map. The NLS problem is highly nonconvex in practice, so a good initial estimate is required. We propose a multi-stage iterative procedure, that utilises the fact that the SLAM problem is linear if the platform's rotations are known. The map is initialised with camera feature detections only, by utilising feature tracking and clustering of  feature tracks. In this way, loop closures are automatically detected. The initialization method and subsequent NLS refinement is demonstrated on both simulated and real data.

1 - 17 of 17
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf