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. 23, 2020

Filed:

Sep. 20, 2018
Applicant:

Nvidia Corporation, Santa Clara, CA (US);

Inventors:

Jinwei Gu, San Jose, CA (US);

Samarth Manoj Brahmbhatt, Decatur, GA (US);

Kihwan Kim, Campbell, CA (US);

Jan Kautz, Lexington, MA (US);

Assignee:

NVIDIA Corporation, Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/80 (2017.01); G06T 7/00 (2017.01); G06K 9/00 (2006.01); G06K 9/20 (2006.01); G06K 9/46 (2006.01); G06N 3/00 (2006.01); G06T 7/579 (2017.01); G06T 7/20 (2017.01);
U.S. Cl.
CPC ...
G06T 7/80 (2017.01); G06K 9/00 (2013.01); G06K 9/00986 (2013.01); G06K 9/209 (2013.01); G06K 9/4628 (2013.01); G06N 3/00 (2013.01); G06T 7/20 (2013.01); G06T 7/579 (2017.01); G06T 7/97 (2017.01); G06T 2200/28 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30244 (2013.01);
Abstract

A deep neural network (DNN) system learns a map representation for estimating a camera position and orientation (pose). The DNN is trained to learn a map representation corresponding to the environment, defining positions and attributes of structures, trees, walls, vehicles, etc. The DNN system learns a map representation that is versatile and performs well for many different environments (indoor, outdoor, natural, synthetic, etc.). The DNN system receives images of an environment captured by a camera (observations) and outputs an estimated camera pose within the environment. The estimated camera pose is used to perform camera localization, i.e., recover the three-dimensional (3D) position and orientation of a moving camera, which is a fundamental task in computer vision with a wide variety of applications in robot navigation, car localization for autonomous driving, device localization for mobile navigation, and augmented/virtual reality.


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