公开(公告)号：US11151393B2

公开(公告)日：2021-10-19

申请号：US16792129

申请日：2020-02-14

Applicant: TUSIMPLE, INC.

Inventor： Yi Luo ,  Yi Wang ,  Ke Xu

IPC: G06K9/00 ,  G06K9/62 ,  G06K9/46 ,  G01S17/89 ,  G01C21/16 ,  G01S17/86 ,  G01S17/931 ,  G06T17/05

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: computing, in response to features from a 3D submap and features from a global map, matching score between corresponding features of a same class between the 3D submap and the global map; selecting, for each feature in the 3D submap, a corresponding feature with the highest matching score from the global map; determining a feature correspondence to be invalid if a distance between corresponding features is larger than a threshold; and removing the invalid feature correspondence.

公开(公告)号：US10268205B2

公开(公告)日：2019-04-23

申请号：US15703896

申请日：2017-09-13

Applicant: TUSIMPLE

Inventor： Wentao Zhu ,  Yi Wang ,  Yi Luo

IPC: G06T7/00 ,  G05D1/02 ,  G06N3/08 ,  G06T7/207 ,  G06K9/32 ,  G06T7/246

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.