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A Dual PHD Filter for Effective Occupancy Filtering in a Highly Dynamic Environment
Örebro University, School of Science and Technology. National Laboratory of Science and Technology on Automatic Target Recognition, National University of Defense Technology, Changsha, China. (Center of Applied Autonomous Sensor Systems)ORCID iD: 0000-0002-9990-9163
Örebro University, School of Science and Technology. (Center of Applied Autonomous Sensor Systems)ORCID iD: 0000-0002-9503-0602
Örebro University, School of Science and Technology. (Center of Applied Autonomous Sensor Systems)ORCID iD: 0000-0001-8658-2985
School of Sciences, University of Salamanca, Salamanca, Spain.
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2017 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. PP, no 99, p. 1-17Article in journal (Refereed) Epub ahead of print
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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017. Vol. PP, no 99, p. 1-17
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-63981DOI: 10.1109/TITS.2017.2770152OAI: oai:DiVA.org:oru-63981DiVA, id: diva2:1172125
Available from: 2018-01-09 Created: 2018-01-09 Last updated: 2018-02-08Bibliographically approved

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Fan, HongqiKucner, Tomasz PiotrMagnusson, MartinLilienthal, Achim

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