AUTOMATIC LABELING OF OBJECTS IN SENSOR DATA

    公开(公告)号:US20230019893A1

    公开(公告)日:2023-01-19

    申请号:US17947563

    申请日:2022-09-19

    Applicant: Waymo LLC

    Abstract: Aspects of the disclosure provide for automatically generating labels for sensor data. For instance first sensor data for a first vehicle is identified. The first sensor data is defined in both a global coordinate system and a local coordinate system for the first vehicle. A second vehicle is identified based on a second location of the second vehicle within a threshold distance of the first vehicle within the first timeframe. The second vehicle is associated with second sensor data that is further associated with a label identifying a location of an object, and the location of the object is defined in a local coordinate system of the second vehicle. A conversion from the local coordinate system of the second vehicle to the local coordinate system of the first vehicle may be determined and used to transfer the label from the second sensor data to the first sensor data.

    Hybrid time-of-flight and imager module

    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

    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
    4.
    发明公开

    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

    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.

    Automatic labeling of objects in sensor data

    公开(公告)号:US11481579B2

    公开(公告)日:2022-10-25

    申请号:US16833018

    申请日:2020-03-27

    Applicant: Waymo LLC

    Abstract: Aspects of the disclosure provide for automatically generating labels for sensor data. For instance first sensor data for a first vehicle is identified. The first sensor data is defined in both a global coordinate system and a local coordinate system for the first vehicle. A second vehicle is identified based on a second location of the second vehicle within a threshold distance of the first vehicle within the first timeframe. The second vehicle is associated with second sensor data that is further associated with a label identifying a location of an object, and the location of the object is defined in a local coordinate system of the second vehicle. A conversion from the local coordinate system of the second vehicle to the local coordinate system of the first vehicle may be determined and used to transfer the label from the second sensor data to the first sensor data.

    Hybrid time-of-flight and imager module

    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

    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.

    Thermal imaging for self-driving cars

    公开(公告)号:US11178348B2

    公开(公告)日:2021-11-16

    申请号:US17024485

    申请日:2020-09-17

    Applicant: Waymo LLC

    Abstract: The present disclosure relates to systems and methods that utilize machine learning techniques to improve object classification in thermal imaging systems. In an example embodiment, a method is provided. The method includes receiving, at a computing device, one or more infrared images of an environment. The method additionally includes, applying, using the computing device, a trained machine learning system on the one or more infrared images to determine an identified object type in the environment by at least: determining one or more prior thermal maps associated with the environment; using the one or more prior thermal maps and the one or more infrared images, determining a current thermal map associated with the environment; and determining the identified object type based on the current thermal map. The method also includes providing the identified object type using the computing device.

    Thermal imaging for self-driving cars

    公开(公告)号:US10819923B1

    公开(公告)日:2020-10-27

    申请号:US16688081

    申请日:2019-11-19

    Applicant: Waymo LLC

    Abstract: The present disclosure relates to systems and methods that utilize machine learning techniques to improve object classification in thermal imaging systems. In an example embodiment, a method is provided. The method includes receiving, at a computing device, one or more infrared images of an environment. The method additionally includes, applying, using the computing device, a trained machine learning system on the one or more infrared images to determine an identified object type in the environment by at least: determining one or more prior thermal maps associated with the environment; using the one or more prior thermal maps and the one or more infrared images, determining a current thermal map associated with the environment; and determining the identified object type based on the current thermal map. The method also includes providing the identified object type using the computing device.

    Automatic labeling of objects in sensor data

    公开(公告)号:US12159451B2

    公开(公告)日:2024-12-03

    申请号:US17947563

    申请日:2022-09-19

    Applicant: Waymo LLC

    Abstract: Aspects of the disclosure provide for automatically generating labels for sensor data. For instance first sensor data for a first vehicle is identified. The first sensor data is defined in both a global coordinate system and a local coordinate system for the first vehicle. A second vehicle is identified based on a second location of the second vehicle within a threshold distance of the first vehicle within the first timeframe. The second vehicle is associated with second sensor data that is further associated with a label identifying a location of an object, and the location of the object is defined in a local coordinate system of the second vehicle. A conversion from the local coordinate system of the second vehicle to the local coordinate system of the first vehicle may be determined and used to transfer the label from the second sensor data to the first sensor data.

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