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:
Oct. 21, 2025

Filed:

May. 26, 2023
Applicant:

Ford Global Technologies, Llc, Dearborn, MI (US);

Inventors:

Shenbagaraj Kannapiran, Tempe, AZ (US);

Nalin Bendapudi, Ann Arbor, MI (US);

Devarth Parikh, Ann Arbor, MI (US);

Ankit Girish Vora, Northville, MI (US);

Assignee:

Ford Global Technologies, LLC, Dearborn, MI (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/73 (2017.01); G05D 1/00 (2006.01); G06T 7/174 (2017.01);
U.S. Cl.
CPC ...
G06T 7/74 (2017.01); G05D 1/0251 (2013.01); G06T 7/174 (2017.01); G06T 2207/20084 (2013.01); G06T 2207/30244 (2013.01);
Abstract

A computer that includes a processor and a memory, the memory including instructions executable by the processor to select first side stereo images of first and second pairs of stereo images acquired at first and second time steps, respectively, and mask first and second first side stereo images, determine point features in masked first and second first side stereo images and determine line features in masked first and second first side stereo images. Matching point features in the masked first and second first side stereo images can be determined using a first attention/graph neural network (attn/GNN) based on keypoints determined based on the line features. Matching line features in the masked first and second first side stereo images can be determined using a second attn/GNN based on keypoints determined based on the line features. Three-dimensional (3D) locations in a scene can be determined by determining stereo disparity based on the matched point features included in the first first side stereo image and point features determined in a first second side stereo image of first and second stereo pairs of images and a 3D stereo camera pose can be determined by determining a perspective-n-point and line algorithm on the 3D locations.


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