中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

13 records (took 0.019 seconds)

    Hybrid Time-of-Flight and Imager Module
    1.
    发明公开
    Hybrid Time-of-Flight and Imager Module 审中-公开

    公开(公告)号：US20230194681A1

    公开(公告)日：2023-06-22

    申请号：US18171883

    申请日：2023-02-21

    Applicant: Waymo LLC

    Inventor： Caner Onal ,  David Schleuning ,  Brendan Hermalyn ,  Simon Verghese ,  Alex McCauley ,  Brandyn White ,  Ury Zhilinsky

    IPC: G01S7/4865 ,  G01S7/48 ,  G01S7/481 ,  G01S17/931

    CPC classification number: G01S7/4865 ,  G01S7/4804 ,  G01S7/4816 ,  G01S17/931

    Abstract: The present disclosure relates to systems and methods that provide both an image of a scene and depth information for the scene. An example system includes at least one time-of-flight (ToF) sensor and an imaging sensor. The ToF sensor and the imaging sensor are configured to receive light from a scene. The system also includes at least one light source and a controller that carries out operations. The operations include causing the at least one light source to illuminate at least a portion of the scene with illumination light according to an illumination schedule. The operations also include causing the at least one ToF sensor to provide information indicative of a depth map of the scene based on the illumination light. The operations additionally include causing the imaging sensor to provide information indicative of an image of the scene based on the illumination light.

    Hybrid time-of-flight and imager module
    3.
    发明授权
    Hybrid time-of-flight and imager module 有权

    公开(公告)号：US11609313B2

    公开(公告)日：2023-03-21

    申请号：US16229193

    申请日：2018-12-21

    Applicant: Waymo LLC

    Inventor： Caner Onal ,  David Schleuning ,  Brendan Hermalyn ,  Simon Verghese ,  Alex McCauley ,  Brandyn White ,  Ury Zhilinsky

    IPC: G01S7/48 ,  G01S17/89 ,  G06T1/00 ,  G01S7/4865 ,  G01S7/481 ,  G01S17/931

    Abstract: The present disclosure relates to systems and methods that provide both an image of a scene and depth information for the scene. An example system includes at least one time-of-flight (ToF) sensor and an imaging sensor. The ToF sensor and the imaging sensor are configured to receive light from a scene. The system also includes at least one light source and a controller that carries out operations. The operations include causing the at least one light source to illuminate at least a portion of the scene with illumination light according to an illumination schedule. The operations also include causing the at least one ToF sensor to provide information indicative of a depth map of the scene based on the illumination light. The operations additionally include causing the imaging sensor to provide information indicative of an image of the scene based on the illumination light.

    Object detection neural networks
    4.
    发明授权
    Object detection neural networks 有权

    公开(公告)号：US11113548B2

    公开(公告)日：2021-09-07

    申请号：US16436754

    申请日：2019-06-10

    Applicant: Waymo LLC

    Inventor： Zhaoyin Jia ,  Ury Zhilinsky ,  Yun Jiang ,  Yimeng Zhang

    IPC: G06K9/00 ,  G06K9/46 ,  G06K9/62 ,  G06N3/08 ,  G06N3/04 ,  G06N3/00

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating object detection predictions from a neural network. In some implementations, an input characterizing a first region of an environment is obtained. The input includes a projected laser image generated from a three-dimensional laser sensor reading of the first region, a camera image patch generated from a camera image of the first region, and a feature vector of features characterizing the first region. The input is processed using a high precision object detection neural network to generate a respective object score for each object category in a first set of one or more object categories. Each object score represents a respective likelihood that an object belonging to the object category is located in the first region of the environment.

    Recurrent neural network classifier
    5.
    发明授权
    Recurrent neural network classifier 有权

    公开(公告)号：US11880758B1

    公开(公告)日：2024-01-23

    申请号：US17391627

    申请日：2021-08-02

    Applicant: Waymo LLC

    Inventor： Congcong Li ,  Ury Zhilinsky ,  Yun Jiang ,  Zhaoyin Jia

    IPC: G06N3/04 ,  G06N3/08 ,  G06N3/044 ,  G06N3/045

    CPC classification number: G06N3/044 ,  G06N3/045 ,  G06N3/08

    Abstract: Disclosed herein are neural networks for generating target classifications for an object from a set of input sequences. Each input sequence includes a respective input at each of multiple time steps, and each input sequence corresponds to a different sensing subsystem of multiple sensing subsystems. For each time step in the multiple time steps and for each input sequence in the set of input sequences, a respective feature representation is generated for the input sequence by processing the respective input from the input sequence at the time step using a respective encoder recurrent neural network (RNN) subsystem for the sensing subsystem that corresponds to the input sequence. For each time step in at least a subset of the multiple time steps, the respective feature representations are processed using a classification neural network subsystem to select a respective target classification for the object at the time step.

    Classifying objects using recurrent neural network and classifier neural network subsystems
    7.
    发明授权
    Classifying objects using recurrent neural network and classifier neural network subsystems 有权

    公开(公告)号：US11093819B1

    公开(公告)日：2021-08-17

    申请号：US15381389

    申请日：2016-12-16

    Applicant: Waymo LLC

    Inventor： Congcong Li ,  Ury Zhilinsky ,  Yun Jiang ,  Zhaoyin Jia

    IPC: G06N3/04 ,  G06N3/08

    Abstract: Disclosed herein are neural networks for generating target classifications for an object from a set of input sequences. Each input sequence includes a respective input at each of multiple time steps, and each input sequence corresponds to a different sensing subsystem of multiple sensing subsystems. For each time step in the multiple time steps and for each input sequence in the set of input sequences, a respective feature representation is generated for the input sequence by processing the respective input from the input sequence at the time step using a respective encoder recurrent neural network (RNN) subsystem for the sensing subsystem that corresponds to the input sequence. For each time step in at least a subset of the multiple time steps, the respective feature representations are processed using a classification neural network subsystem to select a respective target classification for the object at the time step.

    OBJECT DETECTION NEURAL NETWORKS
    8.
    发明申请
    OBJECT DETECTION NEURAL NETWORKS 审中-公开

    公开(公告)号：US20190294896A1

    公开(公告)日：2019-09-26

    申请号：US16436754

    申请日：2019-06-10

    Applicant: Waymo LLC

    Inventor： Zhaoyin Jia ,  Ury Zhilinsky ,  Yun Jiang ,  Yimeng Zhang

    IPC: G06K9/00 ,  G06K9/62 ,  G06K9/46 ,  G06N3/00

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating object detection predictions from a neural network. In some implementations, an input characterizing a first region of an environment is obtained. The input includes a projected laser image generated from a three-dimensional laser sensor reading of the first region, a camera image patch generated from a camera image of the first region, and a feature vector of features characterizing the first region. The input is processed using a high precision object detection neural network to generate a respective object score for each object category in a first set of one or more object categories. Each object score represents a respective likelihood that an object belonging to the object category is located in the first region of the environment.

    Hybrid time-of-flight and imager module
    9.
    发明授权
    Hybrid time-of-flight and imager module 有权

    公开(公告)号：US12032097B2

    公开(公告)日：2024-07-09

    申请号：US18171883

    申请日：2023-02-21

    Applicant: Waymo LLC

    Inventor： Caner Onal ,  David Schleuning ,  Brendan Hermalyn ,  Simon Verghese ,  Alex McCauley ,  Brandyn White ,  Ury Zhilinsky

    IPC: G01S7/48 ,  G01S7/481 ,  G01S7/4865 ,  G01S17/931

    CPC classification number: G01S7/4865 ,  G01S7/4804 ,  G01S7/4816 ,  G01S17/931

    Abstract: The present disclosure relates to systems and methods that provide both an image of a scene and depth information for the scene. An example system includes at least one time-of-flight (ToF) sensor and an imaging sensor. The ToF sensor and the imaging sensor are configured to receive light from a scene. The system also includes at least one light source and a controller that carries out operations. The operations include causing the at least one light source to illuminate at least a portion of the scene with illumination light according to an illumination schedule. The operations also include causing the at least one ToF sensor to provide information indicative of a depth map of the scene based on the illumination light. The operations additionally include causing the imaging sensor to provide information indicative of an image of the scene based on the illumination light.

Patent Agency Ranking