The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Feb. 28, 2023

Filed:

Jan. 30, 2019
Applicants:

Baidu Usa Llc, Sunnyvale, CA (US);

Baidu.com Times Technology (Beijing) Co., Ltd., Beijing, CN;

Inventors:

Weixin Lu, Beijing, CN;

Yao Zhou, Beijing, CN;

Guowei Wan, Beijing, CN;

Shenhua Hou, Beijing, CN;

Shiyu Song, Sunnyvale, CA (US);

Assignees:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06V 10/46 (2022.01); G06T 7/70 (2017.01); G01S 17/89 (2020.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G06V 10/22 (2022.01);
U.S. Cl.
CPC ...
G06V 10/462 (2022.01); G01S 17/89 (2013.01); G06N 3/0454 (2013.01); G06N 3/08 (2013.01); G06T 7/70 (2017.01); G06V 10/22 (2022.01); G06T 2207/30248 (2013.01);
Abstract

In one embodiment, a method for extracting point cloud features for use in localizing an autonomous driving vehicle (ADV) includes selecting a first set of keypoints from an online point cloud, the online point cloud generated by a LiDAR device on the ADV for a predicted pose of the ADV; and extracting a first set of feature descriptors from the first set of keypoints using a feature learning neural network running on the ADV, The method further includes locating a second set of keypoints on a pre-built point cloud map, each keypoint of the second set of keypoints corresponding to a keypoint of the first set of keypoint; extracting a second set of feature descriptors from the pre-built point cloud map; and estimating a position and orientation of the ADV based on the first set of feature descriptors, the second set of feature descriptors, and a predicted pose of the ADV.


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