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    UNDISTORTED RAW LiDAR SCANS AND STATIC POINT EXTRACTIONS SYSTEM FOR GROUND TRUTH STATIC SCENE SPARSE FLOW GENERATION
    11.
    发明申请
    UNDISTORTED RAW LiDAR SCANS AND STATIC POINT EXTRACTIONS SYSTEM FOR GROUND TRUTH STATIC SCENE SPARSE FLOW GENERATION 审中-公开

    公开(公告)号：US20180357315A1

    公开(公告)日：2018-12-13

    申请号：US15621931

    申请日：2017-06-13

    Applicant: TUSIMPLE

    Inventor： YI LUO ,  YI WANG ,  PANQU WANG ,  KE XU

    IPC: G06F17/30 ,  G05D1/02 ,  G06T7/20 ,  G06T3/40

    CPC classification number: G06F17/30811 ,  B60W2420/52 ,  G01S17/89 ,  G05D1/024 ,  G06T3/4007 ,  G06T7/20

    Abstract: A system for generating a ground truth dataset for motion planning of a vehicle is disclosed. The system includes an internet server that further includes an I/O port, configured to transmit and receive electrical signals to and from a client device; a memory; one or more processing units; and one or more programs stored in the memory and configured for execution by the one or more processing units, the one or more programs including instructions for: an identifying module configured to identify, for a pair of undistorted LiDAR scans, points belonging to a static object in an environment; an aligning module configured to align close points based on pose estimates; and a transforming module configured to transform a reference scan that is close in time to a target undistorted LiDAR scan so as to align the reference scan with the target undistorted LiDAR scan.

    CORNER POINT EXTRACTION SYSTEM AND METHOD FOR IMAGE GUIDED STEREO CAMERA OPTICAL AXES ALIGNMENT
    15.
    发明申请
    CORNER POINT EXTRACTION SYSTEM AND METHOD FOR IMAGE GUIDED STEREO CAMERA OPTICAL AXES ALIGNMENT 审中-公开

    公开(公告)号：US20190082156A1

    公开(公告)日：2019-03-14

    申请号：US15701398

    申请日：2017-09-11

    Applicant: TUSIMPLE

    Inventor： BOLUN ZHANG ,  YI WANG ,  KE XU

    IPC: H04N13/02 ,  G06T7/13 ,  G05D1/02

    Abstract: A method of aligning optical axes of cameras for a non-transitory computer readable storage medium storing one or more programs is disclosed. The one or more programs comprise instructions, which when executed by a computing device, cause the computing device to perform by one or more autonomous vehicle driving modules execution of processing of images using the following steps comprising: calibrating intrinsic parameters of a set of cameras; extracting corner points associated with a pattern; and computing a vanishing point based on information on the extracted corner points.

    VANISHING POINT COMPUTATION AND ONLINE ALIGNMENT SYSTEM AND METHOD FOR IMAGE GUIDED STEREO CAMERA OPTICAL AXES ALIGNMENT
    16.
    发明申请
    VANISHING POINT COMPUTATION AND ONLINE ALIGNMENT SYSTEM AND METHOD FOR IMAGE GUIDED STEREO CAMERA OPTICAL AXES ALIGNMENT 审中-公开

    公开(公告)号：US20190080478A1

    公开(公告)日：2019-03-14

    申请号：US15701409

    申请日：2017-09-11

    Applicant: TUSIMPLE

    Inventor： BOLUN ZHANG ,  YI WANG ,  KE XU

    IPC: G06T7/80 ,  B60R1/00

    Abstract: A method of aligning optical axes of cameras for a non-transitory computer readable storage medium storing one or more programs is disclosed. The one or more programs comprise instructions, which when executed by a computing device, cause the computing device to perform by one or more autonomous vehicle driving modules execution of processing of images using the following steps comprising: disposing a planar pattern that is viewable to a set of cameras; recovering boundary corner points of the planar pattern from a pair of images; constructing a pair of parallel lines based on 2D positions of the recovered boundary corner points; and determining an intersection point of the pair of parallel lines to be a vanishing point in a pixel coordinate.

    TRAINING AND TESTING OF A NEURAL NETWORK METHOD FOR DEEP ODOMETRY ASSISTED BY STATIC SCENE OPTICAL FLOW
    17.
    发明申请
    TRAINING AND TESTING OF A NEURAL NETWORK METHOD FOR DEEP ODOMETRY ASSISTED BY STATIC SCENE OPTICAL FLOW 审中-公开

    公开(公告)号：US20190079535A1

    公开(公告)日：2019-03-14

    申请号：US15703896

    申请日：2017-09-13

    Applicant: TUSIMPLE

    Inventor： WENTAO ZHU ,  YI WANG ,  YI LUO

    IPC: G05D1/02 ,  G06T7/207 ,  G06N3/08

    Abstract: A method of visual odometry for a non-transitory computer readable storage medium storing one or more programs is disclosed. The one or more programs include instructions, which when executed by a computing device, causes the computing device to perform the following steps comprising: in response to images in pairs, generating a prediction of static scene optical flow for each pair of the images in a visual odometry model; generating a set of motion parameters for each pair of the images in the visual odometry model; training the visual odometry model by using the prediction of static scene optical flow and the motion parameters; and predicting motion between a pair of consecutive image frames by the trained visual odometry model.

    NEURAL NETWORK ARCHITECTURE METHOD FOR DEEP ODOMETRY ASSISTED BY STATIC SCENE OPTICAL FLOW
    18.
    发明申请
    NEURAL NETWORK ARCHITECTURE METHOD FOR DEEP ODOMETRY ASSISTED BY STATIC SCENE OPTICAL FLOW 审中-公开

    公开(公告)号：US20190079533A1

    公开(公告)日：2019-03-14

    申请号：US15703874

    申请日：2017-09-13

    Applicant: TUSIMPLE

    Inventor： WENTAO ZHU ,  YI WANG ,  YI LUO

    IPC: G05D1/02 ,  G01S17/89 ,  G01C21/16 ,  G06K9/00 ,  G06N5/02 ,  G06N99/00

    Abstract: A method of visual odometry for a non-transitory computer readable storage medium storing one or more programs is disclosed. The one or more programs includes instructions, which when executed by a computing device, causes the computing device to perform the following steps comprising: extracting representative features from a pair input images in a first convolution neural network (CNN) in a visual odometry model; merging, in a first merge module, outputs from the first CNN; decreasing feature map size in a second CNN; generating a first flow output for each layer in a first deconvolution neural network (DNN); merging, in a second merge module, outputs from the second CNN and the first DNN; generating a second flow output for each layer in a second DNN; and reducing accumulated errors in a recurrent neural network (RNN).

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