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  • 1.
    Fan, Hongqi
    et al.
    Örebro University, School of Science and Technology. National Laboratory of Science and Technology on Automatic Target Recognition, National University of Defense Technology, Changsha, China.
    Kucner, Tomasz Piotr
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Li, Tiancheng
    School of Sciences, University of Salamanca, Salamanca, Spain.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    A Dual PHD Filter for Effective Occupancy Filtering in a Highly Dynamic Environment2018In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 19, no 9, p. 2977-2993Article in journal (Refereed)
    Abstract [en]

    Environment monitoring remains a major challenge for mobile robots, especially in densely cluttered or highly populated dynamic environments, where uncertainties originated from environment and sensor significantly challenge the robot's perception. This paper proposes an effective occupancy filtering method called the dual probability hypothesis density (DPHD) filter, which models uncertain phenomena, such as births, deaths, occlusions, false alarms, and miss detections, by using random finite sets. The key insight of our method lies in the connection of the idea of dynamic occupancy with the concepts of the phase space density in gas kinetic and the PHD in multiple target tracking. By modeling the environment as a mixture of static and dynamic parts, the DPHD filter separates the dynamic part from the static one with a unified filtering process, but has a higher computational efficiency than existing Bayesian Occupancy Filters (BOFs). Moreover, an adaptive newborn function and a detection model considering occlusions are proposed to improve the filtering efficiency further. Finally, a hybrid particle implementation of the DPHD filter is proposed, which uses a box particle filter with constant discrete states and an ordinary particle filter with a time-varying number of particles in a continuous state space to process the static part and the dynamic part, respectively. This filter has a linear complexity with respect to the number of grid cells occupied by dynamic obstacles. Real-world experiments on data collected by a lidar at a busy roundabout demonstrate that our approach can handle monitoring of a highly dynamic environment in real time.

  • 2.
    Fan, Hongqi
    et al.
    National University of Defense Technology, Changsa, P. R. China.
    Lu, Dawei
    National University of Defense Technology, Changsa, P. R. China.
    Kucner, Tomasz Piotr
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    2D Spatial Keystone Transform for Sub-Pixel Motion Extraction from Noisy Occupancy Grid Map2018In: Proceedings of 21st International Conference on Information Fusion (FUSION), 2018, p. 2400-2406Conference paper (Refereed)
    Abstract [en]

    In this paper, we propose a novel sub-pixel motionextraction method, called as Two Dimensional Spatial KeystoneTransform (2DS-KST), for the motion detection and estimationfrom successive noisy Occupancy Grid Maps (OGMs). It extendsthe KST in radar imaging or motion compensation to 2Dreal spatial case, based on multiple hypotheses about possibledirections of moving obstacles. Simulation results show that 2DSKSThas a good performance on the extraction of sub-pixelmotions in very noisy environment, especially for those slowlymoving obstacles.

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