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    Systems, methods, and devices for geo-localization
    1.
    发明授权
    Systems, methods, and devices for geo-localization 有权

    公开(公告)号：US10677932B2

    公开(公告)日：2020-06-09

    申请号：US16141015

    申请日：2018-09-25

    Applicant: Ohio State Innovation Foundation

    Inventor： Alper Yilmaz ,  Ashish Gupta

    IPC: G01S19/51 ,  G01C21/20 ,  G01C21/30 ,  G01S19/26 ,  G01S19/45 ,  G01S19/39 ,  G01S5/16

    Abstract: According to the embodiments provided herein, a trajectory determination device for geo-localization can include one or more relative position sensors, one or more processors, and memory. The one or more processors can execute machine readable instructions to receive the relative position signals from the one or more relative position sensors. The relative position signals can be transformed into a sequence of relative trajectories. Each of the relative trajectories can include a distance and directional information indicative of a change in orientation of the trajectory determination device. A progressive topology can be created based upon the sequence of relative trajectories; this progressive topology can be compared to map data. A geolocation of the trajectory determination device can be determined.

    Systems, Methods and Devices for Map-Based Object's Localization Deep Learning and Object's Motion Trajectories on Geospatial Maps Using Neural Network
    3.
    发明公开
    Systems, Methods and Devices for Map-Based Object's Localization Deep Learning and Object's Motion Trajectories on Geospatial Maps Using Neural Network 审中-公开

    公开(公告)号：US20230243658A1

    公开(公告)日：2023-08-03

    申请号：US18004614

    申请日：2021-07-06

    Applicant: Ohio State Innovation Foundation

    Inventor： Alper Yilmaz ,  Bing Zha

    IPC: G01C21/30 ,  G01C21/16 ,  G06N3/0442 ,  G06N3/063

    CPC classification number: G01C21/30 ,  G01C21/1656 ,  G06N3/0442 ,  G06N3/063

    Abstract: An object of initial unknown position on a map may be determined by traversing through moving and turning to establish motion trajectory to reduce its spatial uncertainty to a single location that would fit only to a certain map trajectory. A artificial neural network model learns from object motion on different map topologies may establish the object's end-to-end positioning from embedding map topologies and object motion. The proposed method includes learning potential motion patterns from the map and perform trajectory classification in the map's edge-space. Two different trajectory representations, namely angle representation and augmented angle representation (incorporates distance traversed) are considered and both a Graph Neural Network and an RNN are trained from the map for each representation to compare their performances. The results from the actual visual-inertial odometry have shown that the proposed approach is able to learn the map and localize the object based on its motion trajectories.

    Systems and methods for real-time data processing and for emergency planning
    6.
    发明授权
    Systems and methods for real-time data processing and for emergency planning 有权

    公开(公告)号：US11156995B2

    公开(公告)日：2021-10-26

    申请号：US17264122

    申请日：2019-08-22

    Applicant: Ohio State Innovation Foundation

    Inventor： Alper Yilmaz ,  Nima Ajam Gard ,  Ji Hyun Lee ,  Tunc Aldemir ,  Richard Denning

    IPC: G05B13/02 ,  G05B23/02 ,  G06N3/04 ,  G21D3/00 ,  G21D3/04

    Abstract: Systems and methods are described herein for real-time data processing and for emergency planning. Scenario test data may be collected in real-time based on monitoring local or regional data to ascertain any anomaly phenomenon that may indicate an imminent danger or of concern. A computer-implemented method may include filtering a plurality of different test scenarios to identify a sub-set of test scenarios from the plurality of different test scenarios that may have similar behavior characteristics. A sub-set of test scenarios is provided to a trained neural network to identify one or more sub-set of test scenarios. The one or more identified sub-set of test scenarios may correspond to one or more anomaly test scenarios from the sub-set of test scenarios that is most likely to lead to an undesirable outcome. The neural network may be one of: a conventional neural network and a modular neural network.

    SYSTEMS, METHODS, AND DEVICES FOR GEO-LOCALIZATION
    8.
    发明申请
    SYSTEMS, METHODS, AND DEVICES FOR GEO-LOCALIZATION 审中-公开

    公开(公告)号：US20190025439A1

    公开(公告)日：2019-01-24

    申请号：US16141015

    申请日：2018-09-25

    Applicant: Ohio State Innovation Foundation

    Inventor： Alper Yilmaz ,  Ashish Gupta

    IPC: G01S19/51 ,  G01C21/30 ,  G01S5/16 ,  G01S19/39 ,  G01S19/26 ,  G01S19/45 ,  G01C21/20

    Abstract: According to the embodiments provided herein, a trajectory determination device for geo-localization can include one or more relative position sensors, one or more processors, and memory. The one or more processors can execute machine readable instructions to receive the relative position signals from the one or more relative position sensors. The relative position signals can be transformed into a sequence of relative trajectories. Each of the relative trajectories can include a distance and directional information indicative of a change in orientation of the trajectory determination device. A progressive topology can be created based upon the sequence of relative trajectories; this progressive topology can be compared to map data. A geolocation of the trajectory determination device can be determined.

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