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    NEURAL NETWORK ARCHITECTURE SYSTEM FOR DEEP ODOMETRY ASSISTED BY STATIC SCENE OPTICAL FLOW
    13.
    发明申请
    NEURAL NETWORK ARCHITECTURE SYSTEM FOR DEEP ODOMETRY ASSISTED BY STATIC SCENE OPTICAL FLOW 审中-公开

    公开(公告)号：US20190079534A1

    公开(公告)日：2019-03-14

    申请号：US15703879

    申请日：2017-09-13

    Applicant: TUSIMPLE

    Inventor： WENTAO ZHU ,  YI WANG ,  YI LUO

    IPC: G05D1/02 ,  G06K9/46 ,  G06K9/62 ,  G06T7/246 ,  G01C21/16 ,  G01C21/32 ,  G01S17/89 ,  G06N5/02 ,  G06N99/00

    Abstract: A system for visual odometry is disclosed. The system includes: an internet server, comprising: 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: 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).

    SYSTEM AND METHOD FOR CENTIMETER PRECISION LOCALIZATION USING CAMERA-BASED SUBMAP AND LIDAR-BASED GLOBAL MAP
    14.
    发明申请
    SYSTEM AND METHOD FOR CENTIMETER PRECISION LOCALIZATION USING CAMERA-BASED SUBMAP AND LIDAR-BASED GLOBAL MAP 审中-公开

    公开(公告)号：US20190066329A1

    公开(公告)日：2019-02-28

    申请号：US15684339

    申请日：2017-08-23

    Applicant: TUSIMPLE

    Inventor： YI LUO ,  YI WANG ,  KE XU

    IPC: G06T7/73 ,  G06T17/05 ,  G06K9/46 ,  G06T7/579 ,  G06K9/62 ,  G06K9/00

    Abstract: A method of localization 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 from a camera and data from a LiDAR using the following steps comprising: constructing a 3D submap and a global map; extracting features from the 3D submap and the global map; matching features extracted from the 3D submap against features extracted from the global map; refining feature correspondence; and refining location of the 3D submap.

    UNDISTORTED RAW LiDAR SCANS AND STATIC POINT EXTRACTIONS SYSTEM FOR GROUND TRUTH STATIC SCENE SPARSE FLOW GENERATION
    16.
    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.

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