SYSTEMS AND METHODS FOR USING A SLIDING WINDOW OF GLOBAL POSITIONING EPOCHS IN VISUAL-INERTIAL ODOMETRY

    公开(公告)号:US20180188384A1

    公开(公告)日:2018-07-05

    申请号:US15703588

    申请日:2017-09-13

    CPC classification number: G01S19/45 G01S11/12 G01S19/47 G01S19/52 G01S19/53

    Abstract: A method for visual inertial odometry (VIO)-aided global positioning is described. The method includes updating an extended Kalman filter (EKF) state including a current pose and a sliding window of multiple prior poses. The sliding window includes poses at a number of most recent global positioning system (GPS) time epochs. Updating the EKF includes updating an EKF covariance matrix for the prior poses and the current pose in the EKF state. The method also includes determining, at a GPS epoch, a relative displacement between each of the updated prior poses and the current pose. The method further includes determining an error covariance of each of the relative displacements based on cross-covariances between each of the updated prior poses and the current pose in the EKF covariance matrix. The method additionally includes using the relative displacements and the error covariances to fuse pseudorange measurements taken over multiple epochs.

    SELF-SUPERVISED MULTI-FRAME DEPTH ESTIMATION WITH ODOMETRY FUSION

    公开(公告)号:US20240362807A1

    公开(公告)日:2024-10-31

    申请号:US18309444

    申请日:2023-04-28

    CPC classification number: G06T7/55 G06T7/254 G06T2207/20084 G06T2207/30252

    Abstract: An example device for processing image data includes a processing unit configured to: receive, from a camera of a vehicle, a first image frame at a first time and a second image frame at a second time; receive, from an odometry unit of the vehicle, a first position of the vehicle at the first time and a second position of the vehicle at a second time; calculate a pose difference value representing a difference between the second and first positions; form a pose frame having a size corresponding to the first and second image frames and sample values including the pose difference value; and provide the first and second image frames and the pose frame to a neural networking unit configured to calculate depth for objects in the first image frame and the second image frame, the depth for the objects representing distances between the objects and the vehicle.

    Static occupancy tracking
    4.
    发明授权

    公开(公告)号:US12026954B2

    公开(公告)日:2024-07-02

    申请号:US17452552

    申请日:2021-10-27

    CPC classification number: G06V20/58 G06N3/02 G06V20/588

    Abstract: Techniques and systems are provided for determining static occupancy. For example, an apparatus can be configured to determine one or more pixels associated with one or more static objects depicted in one or more images of a three-dimensional space. The apparatus can be configured to obtain a point map including a plurality of map points, the plurality of map points corresponding to a portion of the three-dimensional space. The apparatus can be configured to determine, based on the point map and the one or more pixels associated with the one or more static objects, a probability of occupancy by the one or more static objects in the portion of the three-dimensional space. The apparatus can be configured to combine information across multiple images of the three-dimensional space, and can determine probabilities of occupancy for all cells in a static occupancy grid that is associated with the three-dimensional space.

    DIRECT DEPTH PREDICTION
    6.
    发明申请

    公开(公告)号:US20250094796A1

    公开(公告)日:2025-03-20

    申请号:US18467804

    申请日:2023-09-15

    Abstract: Example systems and techniques are described for training a machine learning model. A system includes memory configured to store image data captured by a plurality of cameras and one or more processors communicatively coupled to the memory. The one or more processors are configured to execute a machine learning model on the image data, the machine learning model including a plurality of layers. The one or more processors are configured to apply a non-linear mapping function to output of one layer of the plurality of layers to generate depth data. The one or more processors are configured to train the machine learning model based on the depth data to generate a trained machine learning model.

    Systems and methods for using a global positioning system velocity in visual-inertial odometry

    公开(公告)号:US10371530B2

    公开(公告)日:2019-08-06

    申请号:US15703483

    申请日:2017-09-13

    Abstract: A method performed by an electronic device is described. The method includes determining a predicted velocity relative to Earth corresponding to a first epoch using a camera and an inertial measurement unit (IMU). The method also includes determining, using a Global Positioning System (GPS) receiver, a GPS velocity relative to Earth. The method further includes determining a difference vector between the predicted velocity and the GPS velocity. The method additionally includes refining a bias estimate and a scale factor estimate of IMU measurements proportional to the difference vector. The method also includes refining a misalignment estimate between the camera and the IMU based on the difference vector. The method further includes providing pose information based on the refined bias estimate, the refined scale factor, and the refined misalignment estimate.

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