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:
Jun. 21, 2022

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 ...
G01S 17/10 (2020.01); G01S 17/89 (2020.01); G01S 17/931 (2020.01); G01S 17/86 (2020.01); G01C 21/30 (2006.01); G01C 23/00 (2006.01); G01C 3/08 (2006.01); B60W 60/00 (2020.01); G06N 3/08 (2006.01); G06V 20/56 (2022.01);
U.S. Cl.
CPC ...
B60W 60/001 (2020.02); G06N 3/08 (2013.01); G06V 20/56 (2022.01); B60W 2420/42 (2013.01); B60W 2420/52 (2013.01); B60W 2520/14 (2013.01);
Abstract

In one embodiment, a method for temporal smoothness in localization results for an autonomous driving vehicle includes: creating a probability offset volume that represents an overall matching cost between a first set of keypoints from the online point cloud and a second set of keypoints from a pre-built point cloud map for each of a series of sequential light detection and ranging (LiDAR) frames in an online point cloud. The method also includes compressing the probability offset volume into multiple probability vectors across a X dimension, a Y dimension and a yaw dimension; providing each probability vector of the probability offset volume to a number of recurrent neural networks (RNNs); and generating, by the RNNs, a trajectory of location results across the plurality of sequential LiDAR frames.


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