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. 04, 2022

Filed:

Jul. 19, 2018
Applicant:

California Institute of Technology, Pasadena, CA (US);

Inventors:

Larry H. Matthies, Northridge, CA (US);

Cevahir Cigla, Pasadena, CA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G05D 1/00 (2006.01); G05D 3/00 (2006.01); G06F 7/00 (2006.01); G06F 17/00 (2019.01); G06T 7/579 (2017.01); G08G 5/04 (2006.01); G08G 5/00 (2006.01); G06K 9/62 (2022.01); B64C 39/02 (2006.01); B64D 47/08 (2006.01); G06V 10/75 (2022.01); G06V 20/10 (2022.01); G06V 20/13 (2022.01); G06V 20/64 (2022.01);
U.S. Cl.
CPC ...
G06T 7/579 (2017.01); B64C 39/024 (2013.01); B64C 39/028 (2013.01); B64D 47/08 (2013.01); G06K 9/6226 (2013.01); G06K 9/6289 (2013.01); G06V 10/751 (2022.01); G06V 20/10 (2022.01); G06V 20/13 (2022.01); G06V 20/64 (2022.01); G08G 5/0069 (2013.01); G08G 5/045 (2013.01); B64C 2201/027 (2013.01); B64C 2201/123 (2013.01); B64C 2201/127 (2013.01); G06T 2207/10021 (2013.01); G06T 2207/10028 (2013.01); G06T 2207/10032 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/30261 (2013.01);
Abstract

A method and system provide for temporal fusion of depth maps in an image space representation. A series of depth maps are obtained/acquired from one or more depth sensors at a first time. A first Gaussian mixture model (GMM) is initialized using one of the series of depth maps. A second depth map is obtained from the depth sensors at a second time. An estimate of the motion of the depth sensors, from the first time to the second time, is received. A predictive GMM at the second time is created based on a transform of the first GMM and the estimate of the motion. The predictive GMM is updated based on the second depth map.


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