liu.seSearch for publications in DiVA
Change search
Refine search result
1 - 36 of 36
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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.
    Ardeshiri, Tohid
    et al.
    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.
    Özkan, Emre
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Middle E Technical University, Turkey.
    Greedy Reduction Algorithms for Mixtures of Exponential Family2015In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 22, no 6, p. 676-680Article in journal (Refereed)
    Abstract [en]

    In this letter, we propose a general framework for greedy reduction of mixture densities of exponential family. The performances of the generalized algorithms are illustrated both on an artificial example where randomly generated mixture densities are reduced and on a target tracking scenario where the reduction is carried out in the recursion of a Gaussian inverse Wishart probability hypothesis density (PHD) filter.

  • 2.
    Callmer, Jonas
    et al.
    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.
    Nieto, Juan
    University of Sydney, Australia.
    Ramos, Fabio
    University of Sydney, Australia.
    Tree of Words for Visual Loop Closure Detection in Urban SLAM2008In: Proceedings of the '08 Australasian Conference on Robotics and Automation, 2008, p. 102-Conference paper (Refereed)
    Abstract [en]

    This paper introduces vision based loop closure detection in Simultaneous Localisation And Mapping (SLAM) using Tree of Words. The loop closure performance in a complex urban environment is examined and an additional feature is suggested for safer matching. A SLAM ground experiment in an urban area is performed using Tree of Words, a delayed state information filter and planar laser scans for relative pose estimation. Results show that a good map estimation using our vision based loop closure detection can be obtained in near real, yet constant, time. It is shown that an odometry supported recall rate of almost 70% can be obtained with a false detection rate of about 0.01%.

  • 3.
    Danelljan, Martin
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Granström, Karl
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Heintz, Fredrik
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Rudol, Piotr
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Wzorek, Mariusz
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Kvarnström, Jonas
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Doherty, Patrick
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    A Low-Level Active Vision Framework for Collaborative Unmanned Aircraft Systems2015In: COMPUTER VISION - ECCV 2014 WORKSHOPS, PT I / [ed] Lourdes Agapito, Michael M. Bronstein and Carsten Rother, Springer Publishing Company, 2015, Vol. 8925, p. 223-237Conference paper (Refereed)
    Abstract [en]

    Micro unmanned aerial vehicles are becoming increasingly interesting for aiding and collaborating with human agents in myriads of applications, but in particular they are useful for monitoring inaccessible or dangerous areas. In order to interact with and monitor humans, these systems need robust and real-time computer vision subsystems that allow to detect and follow persons.

    In this work, we propose a low-level active vision framework to accomplish these challenging tasks. Based on the LinkQuad platform, we present a system study that implements the detection and tracking of people under fully autonomous flight conditions, keeping the vehicle within a certain distance of a person. The framework integrates state-of-the-art methods from visual detection and tracking, Bayesian filtering, and AI-based control. The results from our experiments clearly suggest that the proposed framework performs real-time detection and tracking of persons in complex scenarios

  • 4.
    Edman, Viktor
    et al.
    Swedish Defence Research Agency.
    Maria, Andersson
    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.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Pedestrian Group Tracking Using the GM-PHD Filter2013In: Proceedings of the 21st European Signal Processing Conference, 2013Conference paper (Other academic)
    Abstract [en]

    A GM-PHD filter is used for pedestrian tracking in a crowdsurveillance application. The purpose is to keep track of thedifferent groups over time as well as to represent the shape ofthe groups and the number of people within the groups. In-put data to the GM-PHD filter are detections using a state ofthe art algorithm applied to video frames from the PETS 2012benchmark data. In a first step, the detections in the framesare converted from image coordinates to world coordinates.This implies that groups can be defined in physical units interms of distance in meters and speed differences in metersper second. The GM-PHD filter is a Bayesian framework thatdoes not form tracks of individuals. Its output is well suitedfor clustering of individuals into groups. The results demon-strate that the GM-PHD filter has the capability of estimatingthe correct number of groups with an accurate representationof their sizes and shapes.

  • 5.
    Granström, Karl
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Extended target tracking using PHD filters2012Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The world in which we live is becoming more and more automated, exemplified by the numerous robots, or autonomous vehicles, that operate in air, on land, or in water. These robots perform a wide array of different tasks, ranging from the dangerous, such as underground mining, to the boring, such as vacuum cleaning. In common for all different robots is that they must possess a certain degree of awareness, both of themselves and of the world in which they operate. This thesis considers aspects of two research problems associated with this, more specifically the Simultaneous Localization and Mapping (SLAM) problem and the Multiple Target Tracking (MTT) problem.

    The SLAM problem consists of having the robot create a map of an environment and simultaneously localize itself in the same map. One way to reduce the effect of small errors that inevitably accumulate over time, and could significantly distort the SLAM result, is to detect loop closure. In this thesis loop closure detection is considered for robots equipped with laser range sensors. Machine learning is used to construct a loop closure detection classifier, and experiments show that the classifier compares well to related work.

    The resulting SLAM map should only contain stationary objects, however the world also contains moving objects, and to function well a robot should be able to handle both types of objects. The MTT problem consists of having the robot keep track of where the moving objects, called targets, are located, and how these targets are moving. This function has a wide range of applications, including tracking of pedestrians, bicycles and cars in urban environments. Solving the MTT problem can be decomposed into two parts: one part is finding out the number of targets, the other part is finding out what the states of the individual targets are.

    In this thesis the emphasis is on tracking of so called extended targets. An extended target is a target that can generate any number of measurements, as opposed to a point target that generates at most one measurement. More than one measurement per target raise interesting possibilities to estimate the size and the shape of the target. One way to model the number of targets and the target states is to use random finite sets, which leads to the Probability Hypothesis Density (PHD) filters. Two implementations of an extended target PHD filter are given, one using Gaussian mixtures and one using Gaussian inverse Wishart (GIW) mixtures. Two models for the size and shape of an extended target measured with laser range sensors are suggested. A framework for estimation of the number of measurements generated by the targets is presented, and reduction of GIW mixtures is addressed. Prediction, spawning and combination of extended targets modeled using GIW distributions is also presented. The extended target tracking functions are evaluated in simulations and in experiments with laser range data.

    List of papers
    1. Learning to Close Loops from Range Data
    Open this publication in new window or tab >>Learning to Close Loops from Range Data
    2011 (English)In: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176, Vol. 30, no 14, p. 1728-1754Article in journal (Refereed) Published
    Abstract [en]

    In this paper we address the loop closure detection problem in simultaneous localization and mapping (SLAM), and present a method for solving the problem using pairwise comparison of point clouds in both two and three dimensions. The point clouds are mathematically described using features that capture important geometric and statistical properties. The features are used as input to the machine learning algorithm AdaBoost, which is used to build a non-linear classifier capable of detecting loop closure from pairs of point clouds. Vantage point dependency in the detection process is eliminated by only using rotation invariant features, thus loop closure can be detected from an arbitrary direction. The classifier is evaluated using publicly available data, and is shown to generalize well between environments. Detection rates of 66%, 63% and 53% for 0% false alarm rate are achieved for 2D outdoor data, 3D outdoor data and 3D indoor data, respectively. In both two and three dimensions, experiments are performed using publicly available data, showing that the proposed algorithm compares favourably with related work.

    Place, publisher, year, edition, pages
    Sage Publications, 2011
    Keywords
    Place recognition, Loop closure, Laser, SLAM, Robotics, Learning
    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:liu:diva-74163 (URN)10.1177/0278364911405086 (DOI)000298258500005 ()
    Available from: 2012-01-20 Created: 2012-01-20 Last updated: 2017-12-08
    2. Extended Target Tracking Using a Gaussian-Mixture PHD Filter
    Open this publication in new window or tab >>Extended Target Tracking Using a Gaussian-Mixture PHD Filter
    2012 (English)In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 48, no 4, p. 3268-3286Article in journal (Refereed) Published
    Abstract [en]

    This paper presents a Gaussian-mixture implementation of the phd filter for tracking extended targets. The exact filter requires processing of all possible measurement set partitions, which is generally infeasible to implement. A method is proposed for limiting the number of considered partitions and possible alternatives are discussed. The implementation is used on simulated data and in experiments with real laser data, and the advantage of the filter is illustrated. Suitable remedies are given to handle spatially close targets and target occlusion.

    Keywords
    Target tracking, Extended target, PHD filter, Random set, Gaussian-mixture, Laser sensor
    National Category
    Signal Processing Control Engineering
    Identifiers
    urn:nbn:se:liu:diva-71866 (URN)10.1109/TAES.2012.6324703 (DOI)000309865600030 ()
    Projects
    CADICSETTCUAS
    Funder
    Swedish Foundation for Strategic Research Swedish Research Council
    Available from: 2012-10-01 Created: 2011-11-08 Last updated: 2017-12-08Bibliographically approved
    3. Tracking Rectangular and Elliptical Extended Targets Using Laser Measurements
    Open this publication in new window or tab >>Tracking Rectangular and Elliptical Extended Targets Using Laser Measurements
    2011 (English)In: Proceedings of the 14th International Conference on Information Fusion, 2011, p. 592-599Conference paper, Published paper (Refereed)
    Abstract [en]

    This paper considers tracking of extended targets using data from laser range sensors. Two types of extended target shapes are considered, rectangular and elliptical, and a method to compute predicted measurements and corresponding innovation covariances is suggested. The proposed method can easily be integrated into any tracking framework that relies on the use of an extended Kalman filter. Here, it is used together with a recently proposed Gaussian mixture probability hypothesis density (GM-PHD) filter for extended target tracking, which enables estimation of not only position, orientation, and size of the extended targets, but also estimation of extended target type (i.e. rectangular or elliptical). In both simulations and experiments using laser data, the versatility of the proposed tracking framework is shown. In addition, a simple measure to evaluate the extended target tracking results is suggested.

    Keywords
    Multiple target tracking, Extended targets, Probability hypothesis density, PHD, Gaussian mixture, Kalman filter, Laser range data, Rectangle, Ellipse, Intersection over union
    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:liu:diva-70031 (URN)978-1-4577-0267-9 (ISBN)
    Conference
    14th International Conference on Information Fusion, Chicago, IL, USA, 5-8 July, 2011
    Funder
    Swedish Research Council
    Note

    ©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Karl Granström, Christian Lundquist and Umut Orguner, Tracking Rectangular and Elliptical Extended Targets Using Laser Measurements, 2011, Proceedings of the 14th International Conference on Information Fusion, 592-599.

    Available from: 2011-08-23 Created: 2011-08-15 Last updated: 2014-03-27Bibliographically approved
    4. A PHD Filter for Tracking Multiple Extended Targets using Random Matrices
    Open this publication in new window or tab >>A PHD Filter for Tracking Multiple Extended Targets using Random Matrices
    2012 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 11, p. 5657-5671Article in journal (Refereed) Published
    Abstract [en]

    This paper presents a random set based approach to tracking of an unknown number of extended targets, in the presence of clutter measurements and missed detections, where the targets extensions are modeled as random matrices. For this purpose, the random matrix framework developed recently by Koch et al. is adapted into the extended target PHD framework, resulting in the Gaussian inverse Wishart PHD (GIW-PHD) filter. A suitable multiple target likelihood is derived, and the main filter recursion is presented along with the necessary assumptions and approximations. The particularly challenging case of close extended targets is addressed with practical measurement clustering algorithms. The capabilities and limitations of the resulting extended target tracking framework are illustrated both in simulations and in experiments based on laser scans.

    Place, publisher, year, edition, pages
    IEEE Signal Processing Society, 2012
    Keywords
    Gaussian distribution, PHD filter, Target tracking, Extended target, Inverse Wishart distribution, Laser sensor, Occlusion, Probability of detection, Random matrix, Random set
    National Category
    Signal Processing Control Engineering
    Identifiers
    urn:nbn:se:liu:diva-82000 (URN)10.1109/TSP.2012.2212888 (DOI)000310139900004 ()
    Projects
    CADICSETTCUAS
    Funder
    Swedish Research Council, 621-2010-4301Swedish Foundation for Strategic Research
    Note

    funding agencies|Swedish Research Council|621-2010-4301|Foundation for Strategic Research (SSF)||

    Available from: 2012-10-01 Created: 2012-09-27 Last updated: 2017-12-07Bibliographically approved
    5. Estimation and Maintenance of Measurement Rates for Multiple Extended Target Tracking
    Open this publication in new window or tab >>Estimation and Maintenance of Measurement Rates for Multiple Extended Target Tracking
    2012 (English)In: Proceedings of the International Conference on Information Fusion (FUSION), IEEE Press, 2012, p. 2170-2176Conference paper, Published paper (Refereed)
    Abstract [en]

    In Gilholm et al.'s extended target model, the number of measurements generated by a target is Poisson distributed with measurement rate γ. Practical use of this extended target model in multiple extended target tracking algorithms requires a good estimate of γ. In this paper, we first give a Bayesian recursion for estimating γ using the well-known conjugate prior Gamma-distribution. In multiple extended target tracking, consideration of different measurement set associations to a single target makes Gamma-mixtures arise naturally. This causes a need for mixture reduction, and we consider the reduction of Gamma-mixtures by means of merging. Analytical minimization of the Kullback-Leibler divergence is used to compute the single Gamma distribution that best approximates a weighted sum of Gamma distributions. Results from simulations show the merits of the presented multiple target measurement-rate estimator. The Bayesian recursion and presented reduction algorithm have important implications for multiple extended target tracking, e.g. using the implementations of the extended target PHD filter.

    Place, publisher, year, edition, pages
    IEEE Press, 2012
    National Category
    Signal Processing
    Identifiers
    urn:nbn:se:liu:diva-82001 (URN)978-0-9824438-4-2 (ISBN)978-1-4673-0417-7 (ISBN)
    Conference
    15th International Conference on Information Fusion, July 9-12, Singapore
    Projects
    CADICSETTCUAS
    Available from: 2012-10-01 Created: 2012-09-27 Last updated: 2014-03-27Bibliographically approved
    6. On the Reduction of Gaussian inverse Wishart Mixtures
    Open this publication in new window or tab >>On the Reduction of Gaussian inverse Wishart Mixtures
    2012 (English)In: Proceedings of the International Conference on Information Fusion (FUSION), IEEE Press, 2012, p. 2162-2169Conference paper, Published paper (Refereed)
    Abstract [en]

    This paper presents an algorithm for reduction of Gaussian inverse Wishart mixtures. Sums of an arbitrary number of mixture components are approximated with single components by analytically minimizing the Kullback-Leibler divergence. The Kullback-Leibler difference is used as a criterion for deciding whether or not two components should be merged, and a simple reduction algorithm is given. The reduction algorithm is tested in simulation examples in both one and two dimensions. The results presented in the paper are useful in extended target tracking using the random matrix framework.

    Place, publisher, year, edition, pages
    IEEE Press, 2012
    National Category
    Signal Processing
    Identifiers
    urn:nbn:se:liu:diva-82002 (URN)978-0-9824438-4-2 (ISBN)978-1-4673-0417-7 (ISBN)
    Conference
    15th International Conference on Information Fusion, July 9-12, Singapore
    Projects
    CADICSETTCUAS
    Available from: 2012-10-01 Created: 2012-09-27 Last updated: 2014-03-27Bibliographically approved
    7. A New Prediction for Extended Targets with Random Matrices
    Open this publication in new window or tab >>A New Prediction for Extended Targets with Random Matrices
    2012 (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    This paper presents a new prediction update for extended targets whose extensions are modeled as random matrices. The prediction is based on several minimizations of the Kullback-Leibler divergence and allows for a kinematic state dependent transformation of the target extension. The results show that the extension prediction is a significant improvement over the previous work carried out on the topic.

    National Category
    Signal Processing Control Engineering
    Identifiers
    urn:nbn:se:liu:diva-82004 (URN)
    Projects
    CADICSETTCUAS
    Available from: 2012-09-27 Created: 2012-09-27 Last updated: 2014-03-27Bibliographically approved
    8. On Spawning and Combination of Extended/Group Targets Modeled with Random Matrices
    Open this publication in new window or tab >>On Spawning and Combination of Extended/Group Targets Modeled with Random Matrices
    2013 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 61, no 3, p. 678-692Article in journal (Refereed) Published
    Abstract [en]

    In extended/group target tracking, where the extensions of the targets are estimated, target spawning and combination events might have significant implications on the extensions. This paper investigates target spawning and combination events for the case that the target extensions are modeled in a random matrix framework. The paper proposes functions that should be provided by the tracking filter in such a scenario. The results, which are obtained by a gamma Gaussian inverse Wishart implementation of an extended target probability hypothesis density filter, confirms that the proposed functions improve the performance of the tracking filter for spawning and combination events.

    Place, publisher, year, edition, pages
    IEEE Signal Processing Society, 2013
    Keywords
    Extended target, Random matrix, Kullback-Leibler divergence, Target spawning, Target combination
    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:liu:diva-82005 (URN)10.1109/TSP.2012.2230171 (DOI)000314719100013 ()
    Projects
    CADICSETTCUAS
    Funder
    Swedish Research Council, 621-2010-4301Swedish Foundation for Strategic Research
    Note

    Funding Agencies|Swedish Research Council|621-2010-4301|Swedish Foundation for Strategic Research (SSF)||

    Available from: 2012-09-27 Created: 2012-09-27 Last updated: 2017-12-07Bibliographically approved
  • 6.
    Granström, Karl
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Loop detection and extended target tracking using laser data2011Licentiate thesis, monograph (Other academic)
    Abstract [en]

    In the past two decades, robotics and autonomous vehicles have received ever increasing research attention. For an autonomous robot to function fully autonomously alongside humans, it must be able to solve the same tasks as humans do, and it must be able to sense the surrounding environment. Two such tasks are addressed in this thesis, using data from laser range sensors.

    The first task is recognising that the robot has returned to a previously visited location, a problem called loop closure detection. Loop closure detection is a fundamental part of the simultaneous localisation and mapping problem, which consists of mapping an unknown area and simultaneously localise in the same map. In this thesis, a classification approach is taken to the loop closure detection problem. The laser range data is described in terms of geometrical and statistical properties, called features. Pairs of laser range data from two different locations are compared by using adaptive boosting to construct a classifier that takes as input the computed features. Experiments using real world laser data are used to evaluate the properties of the classifier, and the classifier is shown to compare well to existing solutions.

    The second task is keeping track of objects that surround the robot, a problem called target tracking. Target tracking is an estimation problem in which data association between the estimates and measurements is of high importance. The data association is complicated by things such as noise and false measurements. In this thesis, extended targets, i.e. targets that potentially generate more than one measurement per time step, are considered. The multiple measurements per time step further complicate the data association. Tracking of extended targets is performed using an implementation of a probability hypothesis density filter, which is evaluated in simulations using the optimal sub-pattern assignment metric. The filter is also used to track humans with real world laser range data, and the experiments show that the filter can handle the so called occlusion problem.

  • 7.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    Callmer, Jonas
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    Learning to Detect Loop Closure from Range Data2009Report (Other academic)
    Abstract [en]

    Despite signicant developments in the Simultaneous Localisation and Map- ping (slam) problem, loop closure detection is still challenging in large scale unstructured environments. Current solutions rely on heuristics that lack generalisation properties, in particular when range sensors are the only source of information about the robot's surrounding environment. This paper presents a machine learning approach for the loop closure detection problem using range sensors. A binary classier based on boosting is used to detect loop closures. The algorithm performs robustly, even under potential occlusions and signicant changes in rotation and translation. We devel- oped a number of features, extracted from range data, that are invariant to rotation. Additionally, we present a general framework for scan-matching slam in outdoor environments. Experimental results in large scale urban environments show the robustness of the approach, with a detection rate of 85% and a false alarm rate of only 1%. The proposed algorithm can be computed in real-time and achieves competitive performance with no manual specication of thresholds given the features.

  • 8.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Callmer, Jonas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ramos, Fabio
    University of Sydney, Australian Centre for Field Robotics.
    Nieto, Juan
    University of Sydney, Australian Centre for Field Robotics.
    Learning to Detect Loop Closure from Range Data2009In: Proceedings of '09 IEEE International Conference on Robotics and Automation, 2009, p. 15-22Conference paper (Refereed)
    Abstract [en]

    Despite significant developments in the Simultaneous Localisation and Mapping (SLAM) problem, loop closure detection is still challenging in large scale unstructured environments. Current solutions rely on heuristics that lack generalisation properties, in particular when range sensors are the only source of information about the robot's surrounding environment. This paper presents a machine learning approach for the loop closure detection problem using range sensors. A binary classifier based on boosting is used to detect loop closures. The algorithm performs robustly, even under potential occlusions and significant changes in rotation and translation. We developed a number of features, extracted from range data, that are invariant to rotation. Additionally, we present a general framework for scan-matching SLAM in outdoor environments. Experimental results in large scale urban environments show the robustness of the approach, with a detection rate of 85% and a false alarm rate of only 1%. The proposed algorithm can be computed in real-time and achieves competitive performance with no manual specification of thresholds given the features.

  • 9.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Lundquist, Christian
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    On the Use of Multiple Measurement Models for Extended Target Tracking2013In: Proceedings of the 16th International Conference on Information Fusion, 2013, p. 1534-1541Conference paper (Refereed)
    Abstract [en]

    This paper considers extended targets that have constant extension shapes, but generate measurements whose appearance can change abruptly. The problem is approached using multiple measurement models, where each model corresponds to a measurement appearance mode. Mode transitions are modeled as dependent on the extended target kinematic state, and a multiple model extended target PHD filter is used to handle multiple targets with multiple appearance modes. The extended target tracking is evaluated using real world data where a laser range sensor is used to track multiple bicycles.

  • 10.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Lundquist, Christian
    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.
    Orguner, Umut
    Department of Electrical and Electronics Engineering, Middle East Technical University.
    On Extended Target Tracking Using PHD Filters2012Other (Other academic)
    Abstract [en]

    This paper presents an overview of the extended target tracking research undertaken at the division of Automatic Control at Linköping University. The PHD and CPHD filters for multiple extended target tracking under clutter and unknown association are summarized, with focus on the Gaussian mixture and Gaussian inverse Wishart implementations. The paper elaborates on measurement set partitioning, the measurement generating Poisson rates, the probability of detection, and practical examples of measurement models.

  • 11.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Lundquist, Christian
    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.
    Orguner, Umut
    Department of Electrical and Electronics Engineering, Middle East Technical University.
    Random Set Methods: Estimation of Multiple Extended Objects2014In: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223X, Vol. 21, no 2, p. 73-82Article in journal (Refereed)
    Abstract [en]

    Random set based methods have provided a rigorous Bayesian framework and have been used extensively in the last decade for point object estimation. In this paper, we emphasize that the same methodology offers an equally powerful approach to estimation of so called extended objects, i.e., objects that result in multiple detections on the sensor side. Building upon the analogy between Bayesian state estimation of a single object and random finite set estimation for multiple objects, we give a tutorial on random set methods with an emphasis on multiple extended object estimation. The capabilities are illustrated on a simple yet insightful real life example with laser range data containing several occlusions.

  • 12.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Lundquist, Christian
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    A Gaussian Mixture PHD Filter for Extended Target Tracking2010Report (Other academic)
    Abstract [en]

    In extended target tracking, targets potentially produce more than one measurement per time step. Multiple extended targets are therefore usually hard to track, due to the resulting complex data association. The main contribution of this paper is the implementation of a Probability Hypothesis Density (PHD) filter for tracking of multiple extended targets. A general modification of the PHD filter to handle extended targets has been presented recently by Mahler, and the novelty in this work lies in the realisation of a Gaussian mixture PHD filter for extended targets. Furthermore, we propose a method to easily partition the measurements into a number of subsets, each of which is supposed to contain measurements that all stem from the same source. The method is illustrated in simulation examples, and the advantage of the implemented extended target PHD filter is shown in a comparison with a standard PHD filter.

  • 13.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Lundquist, Christian
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    A Gaussian Mixture PHD Filter for Extended Target Tracking2010In: Proceedings of the 13th International Conference on Information Fusion, 2010Conference paper (Refereed)
    Abstract [en]

    In extended target tracking, targets potentially produce more than one measurement per time step. Multiple extended targets are therefore usually hard to track, due to the resulting complex data association. The main contribution of this paper is the implementation of a Probability Hypothesis Density (PHD) filter for tracking of multiple extended targets. A general modification of the PHD filter to handle extended targets has been presented recently by Mahler, and the novelty in this work lies in the realisation of a Gaussian mixture PHD filter for extended targets. Furthermore, we propose a method to easily partition the measurements into a number of subsets, each of which is supposed to contain measurements that all stem from the same source. The method is illustrated in simulation examples, and the advantage of the implemented extended target PHD filter is shown in a comparison with a standard PHD filter.

  • 14.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Lundquist, Christian
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Extended Target Tracking using a Gaussian-Mixture PHD Filter2011Report (Other academic)
    Abstract [en]

    This paper presents a Gaussian-mixture implementation of the phd filter for tracking extended targets. The exact filter requires processing of all possible measurement set partitions, which is generally infeasible to implement. A method is proposed for limiting the number of considered partitions and possible alternatives are discussed. The implementation is used on simulated data and in experiments with real laser data, and the advantage of the filter is illustrated. Suitable remedies are given to handle spatially close targets and target occlusion.

  • 15.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Lundquist, Christian
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Extended Target Tracking Using a Gaussian-Mixture PHD Filter2012In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 48, no 4, p. 3268-3286Article in journal (Refereed)
    Abstract [en]

    This paper presents a Gaussian-mixture implementation of the phd filter for tracking extended targets. The exact filter requires processing of all possible measurement set partitions, which is generally infeasible to implement. A method is proposed for limiting the number of considered partitions and possible alternatives are discussed. The implementation is used on simulated data and in experiments with real laser data, and the advantage of the filter is illustrated. Suitable remedies are given to handle spatially close targets and target occlusion.

  • 16.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Lundquist, Christian
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Tracking Rectangular and Elliptical Extended Targets Using Laser Measurements2011In: Proceedings of the 14th International Conference on Information Fusion, 2011, p. 592-599Conference paper (Refereed)
    Abstract [en]

    This paper considers tracking of extended targets using data from laser range sensors. Two types of extended target shapes are considered, rectangular and elliptical, and a method to compute predicted measurements and corresponding innovation covariances is suggested. The proposed method can easily be integrated into any tracking framework that relies on the use of an extended Kalman filter. Here, it is used together with a recently proposed Gaussian mixture probability hypothesis density (GM-PHD) filter for extended target tracking, which enables estimation of not only position, orientation, and size of the extended targets, but also estimation of extended target type (i.e. rectangular or elliptical). In both simulations and experiments using laser data, the versatility of the proposed tracking framework is shown. In addition, a simple measure to evaluate the extended target tracking results is suggested.

  • 17.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering.
    Natale, Antonio
    Italian National Research Council CNR, Italy.
    Braca, Paolo
    NATO Science and Technology Org, Italy.
    Ludeno, Giovanni
    Italian National Research Council CNR, Italy.
    Serafino, Francesco
    Italian National Research Council CNR, Italy.
    Gamma Gaussian Inverse Wishart Probability Hypothesis Density for Extended Target Tracking Using X-Band Marine Radar Data2015In: IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, E-ISSN 1558-0644, Vol. 53, no 12, p. 6617-6631Article in journal (Refereed)
    Abstract [en]

    X-band marine radar systems represent a flexible and low-cost tool for the tracking of multiple targets in a given region of interest. Although suffering several sources of interference, e.g., the sea clutter, these systems can provide high-resolution measurements, both in space and time. Such features offer the opportunity to get accurate information not only about the target position/motion but also about the targets size. Accordingly, in this paper, we exploit emergent extended target tracking (ETT) methodologies in which the target state, typically position/velocity/acceleration, is augmented with the target length and width. In this paper, we propose an ETT procedure based on the popular probability hypothesis density filter, and in particular, we describe the extended target state through the gamma Gaussian inverse Wishart model. The comparative simplicity of the used models allows us to meet the real-time processing constraint required for the practical surveillance purposes. Real-world data from an experimental and operational campaign, collected during the recovery operations of the Costa Concordia wreckage in October 2013, are used to assess the performance of the proposed target tracking methodology. The full signal processing chain is implemented, and considerations of the experimental results are provided. Important nonideal effects, common to every marine radar, are observed and discussed in relation to the assumptions made for the tracking procedure.

  • 18.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering. Linköping University, Faculty of Science & Engineering.
    Natale, Antonio
    IREA CNR, Italy.
    Braca, Paolo
    NATO STO CMRE, Italy.
    Ludeno, Giovanni
    IREA CNR, Italy.
    Serafino, Francesco
    IREA CNR, Italy.
    PHD Extended Target Tracking Using an Incoherent X-band Radar: Preliminary Real-World Experimental Results2014In: 2014 17TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), IEEE , 2014Conference paper (Refereed)
    Abstract [en]

    X-band radar systems represent a flexible and low-cost tool for ship detection and tracking. These systems suffer the interference of the sea-clutter but at the same time they can provide high measurement resolutions, both in space and time. Such features offer the opportunity to get accurate information about the targets state and shape. Accordingly, here we exploit an extended target tracking methodology based on the popular Probability Hypothesis Density to get information about the targets observed in an actual X-band radar dataset. For each target track we estimate the targets position, velocity and acceleration, as well as its size and the expected number of radar returns.

  • 19.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, U.
    Middle E Technical University, Turkey.
    New Prediction for Extended Targets With Random Matrices2014In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 50, no 2, p. 1577-1589Article in journal (Refereed)
    Abstract [en]

    This paper presents a new prediction update for extended targets whose extensions are modeled as random matrices. The prediction is based on several minimizations of the Kullback-Leibler divergence (KL-div) and allows for a kinematic state dependent transformation of the target extension. The results show that the extension prediction is a significant improvement over the previous work carried out on the topic.

  • 20.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Department of Electrical and Electronics Engineering, Middle East Technical University.
    A New Prediction for Extended Targets with Random Matrices2012Manuscript (preprint) (Other academic)
    Abstract [en]

    This paper presents a new prediction update for extended targets whose extensions are modeled as random matrices. The prediction is based on several minimizations of the Kullback-Leibler divergence and allows for a kinematic state dependent transformation of the target extension. The results show that the extension prediction is a significant improvement over the previous work carried out on the topic.

  • 21.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Department of Electrical and Electronics Engineering, Middle East Technical University.
    A PHD Filter for Tracking Multiple Extended Targets using Random Matrices2012In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 11, p. 5657-5671Article in journal (Refereed)
    Abstract [en]

    This paper presents a random set based approach to tracking of an unknown number of extended targets, in the presence of clutter measurements and missed detections, where the targets extensions are modeled as random matrices. For this purpose, the random matrix framework developed recently by Koch et al. is adapted into the extended target PHD framework, resulting in the Gaussian inverse Wishart PHD (GIW-PHD) filter. A suitable multiple target likelihood is derived, and the main filter recursion is presented along with the necessary assumptions and approximations. The particularly challenging case of close extended targets is addressed with practical measurement clustering algorithms. The capabilities and limitations of the resulting extended target tracking framework are illustrated both in simulations and in experiments based on laser scans.

  • 22.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Department of Electrical and Electronics Engineering, Middle East Technical University, Turkey.
    Estimation and Maintenance of Measurement Rates for Multiple Extended Target Tracking2012In: Proceedings of the International Conference on Information Fusion (FUSION), IEEE Press, 2012, p. 2170-2176Conference paper (Refereed)
    Abstract [en]

    In Gilholm et al.'s extended target model, the number of measurements generated by a target is Poisson distributed with measurement rate γ. Practical use of this extended target model in multiple extended target tracking algorithms requires a good estimate of γ. In this paper, we first give a Bayesian recursion for estimating γ using the well-known conjugate prior Gamma-distribution. In multiple extended target tracking, consideration of different measurement set associations to a single target makes Gamma-mixtures arise naturally. This causes a need for mixture reduction, and we consider the reduction of Gamma-mixtures by means of merging. Analytical minimization of the Kullback-Leibler divergence is used to compute the single Gamma distribution that best approximates a weighted sum of Gamma distributions. Results from simulations show the merits of the presented multiple target measurement-rate estimator. The Bayesian recursion and presented reduction algorithm have important implications for multiple extended target tracking, e.g. using the implementations of the extended target PHD filter.

  • 23.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Department of Electrical and Electronics Engineering, Middle East Technical University.
    Implementation of the GIW-PHD filter2012Report (Other academic)
    Abstract [en]

    This report contains pseudo-code for, and a computational complexity analysis of, the Gaussian inverse Wishart Probability Hypothesis Density filter.

  • 24.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    On Spawning and Combination of Extended/Group Targets Modeled with Random Matrices2013In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 61, no 3, p. 678-692Article in journal (Refereed)
    Abstract [en]

    In extended/group target tracking, where the extensions of the targets are estimated, target spawning and combination events might have significant implications on the extensions. This paper investigates target spawning and combination events for the case that the target extensions are modeled in a random matrix framework. The paper proposes functions that should be provided by the tracking filter in such a scenario. The results, which are obtained by a gamma Gaussian inverse Wishart implementation of an extended target probability hypothesis density filter, confirms that the proposed functions improve the performance of the tracking filter for spawning and combination events.

  • 25.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Department of Electrical and Electronics Engineering, Middle East Technical University, Turkey.
    On the Reduction of Gaussian inverse Wishart Mixtures2012In: Proceedings of the International Conference on Information Fusion (FUSION), IEEE Press, 2012, p. 2162-2169Conference paper (Refereed)
    Abstract [en]

    This paper presents an algorithm for reduction of Gaussian inverse Wishart mixtures. Sums of an arbitrary number of mixture components are approximated with single components by analytically minimizing the Kullback-Leibler divergence. The Kullback-Leibler difference is used as a criterion for deciding whether or not two components should be merged, and a simple reduction algorithm is given. The reduction algorithm is tested in simulation examples in both one and two dimensions. The results presented in the paper are useful in extended target tracking using the random matrix framework.

  • 26.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Properties and Approximations of some Matrix Variate Probability Density Functions2011Report (Other academic)
    Abstract [en]

    This report contains properties and approximations of some matrix valued probability density functions. Expected values of functions of generalised Beta type II distributed random variables are derived. In two Theorems, approximations of matrix variate distributions are derived. A third theorem contain a marginalisation result.

  • 27.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering. Linköping University, Faculty of Science & Engineering.
    Reuter, Stephan
    University of Ulm, Germany.
    Meissner, Daniel
    University of Ulm, Germany.
    Scheel, Alexander
    University of Ulm, Germany.
    A multiple model PHD approach to tracking of cars under an assumed rectangular shape2014In: 2014 17TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), IEEE , 2014Conference paper (Refereed)
    Abstract [en]

    This paper presents an extended target tracking method for tracking cars in urban traffic using data from laser range sensors. Results are presented for three real world datasets that contain multiple cars, occlusions, and maneuver changes. The cars shape is approximated by a rectangle, and single track steering models are used for the target kinematics. A multiple model approach is taken for both the dynamics modeling and the measurement modeling. A comparison to ground truth shows that the estimation errors are generally very small: on average the absolute error is less than half a degree for the heading. Multiple cars are handled using a multiple model PHD filter, where a variable probability of detection is integrated to enable tracking of occluded cars.

  • 28.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Schön, Thomas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Learning to Close the Loop from 3D Point Clouds2010Report (Other academic)
    Abstract [en]

    This paper presents a new solution to the loop closing problem for 3D point clouds. Loop closing is the problem of detecting the return to a previously visited location, and constitutes an important part of the solution to the Simultaneous Localisation and Mapping (SLAM) problem. It is important to achieve a low level of false alarms, since closing a false loop can have disastrous effects in a SLAM algorithm. In this work, the point clouds are described using features, which efficiently reduces the dimension of the data by a factor of 300 or more. The machine learning algorithm AdaBoost is used to learn a classifier from the features. All features are invariant to rotation, resulting in a classifier that is invariant to rotation. The presented method does neither rely on the discretisation of 3D space, nor on the extraction of lines, corners or planes. The classifier is extensively evaluated on publicly available outdoor and indoor data, and is shown to be able to robustly and accurately determine whether a pair of point clouds is from the same location or not. Experiments show detection rates of 63% for outdoor and 53% for indoor data at a false alarm rate of 0%. Furthermore, the classifier is shown to generalise well when trained on outdoor data and tested on indoor data in a SLAM experiment.

  • 29.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Schön, Thomas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Learning to Close the Loop from 3D Point Clouds2010In: Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010, p. 2089-2095Conference paper (Refereed)
    Abstract [en]

    This paper presents a new solution to the loop closing problem for 3D point clouds. Loop closing is the problem of detecting the return to a previously visited location, and constitutes an important part of the solution to the Simultaneous Localisation and Mapping (SLAM) problem. It is important to achieve a low level of false alarms, since closing a false loop can have disastrous effects in a SLAM algorithm. In this work, the point clouds are described using features, which efficiently reduces the dimension of the data by a factor of 300 or more. The machine learning algorithm AdaBoost is used to learn a classifier from the features. All features are invariant to rotation, resulting in a classifier that is invariant to rotation. The presented method does neither rely on the discretisation of 3D space, nor on the extraction of lines, corners or planes. The classifier is extensively evaluated on publicly available outdoor and indoor data, and is shown to be able to robustly and accurately determine whether a pair of point clouds is from the same location or not. Experiments show detection rates of 63% for outdoor and 53% for indoor data at a false alarm rate of 0%. Furthermore, the classifier is shown to generalise well when trained on outdoor data and tested on indoor data in a SLAM experiment.

  • 30.
    Granström, Karl
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Schön, Thomas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ramos, Fabio T.
    University of Sydney, Australia.
    Nieto, Juan I.
    University of Sydney, Australia.
    Learning to Close Loops from Range Data2011In: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176, Vol. 30, no 14, p. 1728-1754Article in journal (Refereed)
    Abstract [en]

    In this paper we address the loop closure detection problem in simultaneous localization and mapping (SLAM), and present a method for solving the problem using pairwise comparison of point clouds in both two and three dimensions. The point clouds are mathematically described using features that capture important geometric and statistical properties. The features are used as input to the machine learning algorithm AdaBoost, which is used to build a non-linear classifier capable of detecting loop closure from pairs of point clouds. Vantage point dependency in the detection process is eliminated by only using rotation invariant features, thus loop closure can be detected from an arbitrary direction. The classifier is evaluated using publicly available data, and is shown to generalize well between environments. Detection rates of 66%, 63% and 53% for 0% false alarm rate are achieved for 2D outdoor data, 3D outdoor data and 3D indoor data, respectively. In both two and three dimensions, experiments are performed using publicly available data, showing that the proposed algorithm compares favourably with related work.

  • 31.
    Lundquist, Christian
    et al.
    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.
    Orguner, Umut
    Middle East Technical University, Ankara, Turkey.
    An Extended Target CPHD Filter and a Gamma Gaussian Inverse Wishart Implementation2013In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 7, no 3, p. 472-483Article in journal (Refereed)
    Abstract [en]

    This paper presents a cardinalized probability hypothesis density (CPHD) filter for extended targets that can result in multiple measurements at each scan. The probability hypothesis density (PHD) filter for such targets has been derived by Mahler, and different implementations have been proposed recently. To achieve better estimation performance this work relaxes the Poisson assumptions of the extended target PHD filter in target and measurement numbers. A gamma Gaussian inverse Wishart mixture implementation, which is capable of estimating the target extents and measurement rates as well as the kinematic state of the target, is proposed, and it is compared to its PHD counterpart in a simulation study. The results clearly show that the CPHD filter has a more robust cardinality estimate leading to smaller OSPA errors, which confirms that the extended target CPHD filter inherits the properties of its point target counterpart.

  • 32.
    Lundquist, Christian
    et al.
    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.
    Orguner, Umut
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Estimating the Shape of Targets with a PHD Filter2011In: Proceedings of the 14th International Conference on Information Fusion, 2011Conference paper (Refereed)
    Abstract [en]

    This paper presents a framework for tracking extended targets which give rise to a structured set of measurements per each scan. The concept of a measurement generating point (MGP) which is defined on the boundary of each target is introduced. The tracking framework contains an hybrid statespace where MGP:s and the measurements are modeled by random finite sets and target states by random vectors. The target states are assumed to be partitioned into linear and nonlinear components and a Rao-Blackwellized particle filter is used for their estimation. For each state particle, a probability hypothesis density (PHD) filter is utilized for estimating the conditional set of MGP:s given the target states. The PHD kept for each particle serves as a useful means to represent information in the set of measurements about the target states. The early results obtained show promising performance with stable target following capability and reasonable shape estimates.

  • 33.
    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.

  • 34.
    Orguner, Umut
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Lundquist, Christian
    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.
    Extended Target Tracking with a Cardinalized Probability Hypothesis Density Filter2011Report (Other academic)
    Abstract [en]

    This paper presents a cardinalized probability hypothesis density (CPHD) filter for extended targets that can result in multiple measurements at each scan. The probability hypothesis density (PHD) filter for such targets has already been derived by Mahler and a Gaussian mixture implementation has been proposed recently. This work relaxes the Poisson assumptions of the extended target PHD filter in target and measurement numbers to achieve better estimation performance. A Gaussian mixture implementation is described. The early results using real data from a laser sensor confirm that the sensitivity of the number of targets in the extended target PHD filter can be avoided with the added flexibility of the extended target CPHD filter.

  • 35.
    Orguner, Umut
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Lundquist, Christian
    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.
    Extended Target Tracking with a Cardinalized Probability Hypothesis Density Filter2011In: Proceedings of 2011 International Conference on Information Fusion (FUSION), 2011Conference paper (Refereed)
    Abstract [en]

    This paper presents a cardinalized probability hypothesis density (CPHD) filter for extended targets that can result in multiple measurements at each scan. The probability hypothesis density (PHD) filter for such targets has already been derived by Mahler and a Gaussian mixture implementation has been proposed recently. This work relaxes the Poisson assumptions of the extended target PHD filter in target and measurement numbers to achieve better estimation performance. A Gaussian mixture implementation is described. The early results using real data from a laser sensor confirm that the sensitivity of the number of targets in the extended target PHD filter can be avoided with the added flexibility of the extended target CPHD filter.

  • 36.
    Scheel, A.
    et al.
    Institute of Measurement, Control, and Microtechnology, Ulm UniversityUlm, Germany.
    Granström, Karl
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Meissner, D.
    Institute of Measurement, Control, and Microtechnology, Ulm UniversityUlm, Germany.
    Reuter, S.
    Institute of Measurement, Control, and Microtechnology, Ulm UniversityUlm, Germany.
    Dietmayer, K.
    Institute of Measurement, Control, and Microtechnology, Ulm UniversityUlm, Germany.
    Tracking and data segmentation using a GGIW filter with mixture clustering2014In: FUSION 2014 - 17th International Conference on Information Fusion, Institute of Electrical and Electronics Engineers Inc. , 2014, no 6916137Conference paper (Refereed)
    Abstract [en]

    Common data preprocessing routines often introduce considerable flaws in laser-based tracking of extended objects. As an alternative, extended target tracking methods, such as the Gamma-Gaussian-Inverse Wishart (GGIW) probability hypothesis density (PHD) filter, work directly on raw data. In this paper, the GGIW-PHD filter is applied to real world traffic scenarios. To cope with the large amount of data, a mixture clustering approach which reduces the combinatorial complexity and computation time is proposed. The effective segmentation of raw measurements with respect to spatial distribution and motion is demonstrated and evaluated on two different applications: pedestrian tracking from a vehicle and intersection surveillance.

1 - 36 of 36
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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