SENSOR TIMING CORRELATION
    1.
    发明公开

    公开(公告)号:US20240119629A1

    公开(公告)日:2024-04-11

    申请号:US18373772

    申请日:2023-09-27

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for sensor timing correlation. One of the methods includes obtaining (i) first sensor data from a first sensor and (ii) second sensor data from a second sensor; detecting a representation of an object in both the first sensor data and the second sensor data; determining a predicted physical distance between a first representation of the object in the first sensor data and a second representation of the object in the second sensor data; determining a timing offset between the different sensors using the predicted physical distance between the first representation of the object and the second representation of the object; and adjusting, using the timing offset, subsequent data from the first sensor or the second sensor.

    Robot localization
    2.
    发明授权

    公开(公告)号:US11880212B2

    公开(公告)日:2024-01-23

    申请号:US17371454

    申请日:2021-07-09

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining that current data captured at a current location of a drone satisfies localization adjustment criteria; in response to determining that the current data captured at the current location of the drone satisfies the localization adjustment criteria, identifying previously captured image data; determining a previous expected location of the drone based on both an expected change in location of the drone and a first previous location determined from other image data captured before the previously captured image data; determining a location difference between the previous expected location of the drone and a second previous location determined from the previously captured image data; and determining the current location of the drone based on the location difference.

    EXEMPLAR ROBOT LOCALIZATION
    3.
    发明申请

    公开(公告)号:US20230099968A1

    公开(公告)日:2023-03-30

    申请号:US17952937

    申请日:2022-09-26

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for exemplar generation and localization. In some implementations, a method includes obtaining sensor data from a robot traversing a route at a property; determining sampling rates along the route using the sensor data obtained from the robot; selecting images from the sensor data as exemplars for robot localization using the sampling rates along the route; determining that a second robot is in a localization phase at the property; and providing representations of the exemplars for robot localization to the second robot.

    Lighting adaptive navigation
    6.
    发明授权

    公开(公告)号:US11480431B1

    公开(公告)日:2022-10-25

    申请号:US17004778

    申请日:2020-08-27

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for lighting adaptive navigation. In some implementations, map data associated with a property is received. Sensor data is obtained. Based on the map data and the sensor data, a lighting scenario is determined. Based on the lighting scenario, a modification to at least one of the lighting scenario, to a planned navigation path for the robotic device, to settings for a sensor of the robotic device, to a position for a sensor of the robotic device, or to a position of the robotic device is determined. An action is performed by based on the one or more modifications.

    ROBOT LOCALIZATION
    7.
    发明申请

    公开(公告)号:US20220012493A1

    公开(公告)日:2022-01-13

    申请号:US17371454

    申请日:2021-07-09

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining that current data captured at a current location of a drone satisfies localization adjustment criteria; in response to determining that the current data captured at the current location of the drone satisfies the localization adjustment criteria, identifying previously captured image data; determining a previous expected location of the drone based on both an expected change in location of the drone and a first previous location determined from other image data captured before the previously captured image data; determining a location difference between the previous expected location of the drone and a second previous location determined from the previously captured image data; and determining the current location of the drone based on the location difference.

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