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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.

Patent No.:

US 11715012 B1

PDFFull Text
Date of Patent:
Aug. 01, 2023

Filed:

Oct. 10, 2019

Feature compression and localization for autonomous devices

Applicant:

Uatc, Llc, San Francisco, CA (US);

Inventors:

Raquel Urtasun, Toronto, CA;

Xinkai Wei, Toronto, CA;

Ioan Andrei Barsan, Toronto, CA;

Julieta Martinez Covarrubias, Toronto, CA;

Shenlong Wang, Toronto, CA;

Assignee:

UATC, LLC, Mountain View, CA (US);

Attorney:

Dority & Manning, P.A.

Primary Examiner:

Liton Miah

Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06T 7/155 (2017.01); G06K 9/62 (2022.01); G06T 9/00 (2006.01); G06N 3/084 (2023.01); G06N 3/04 (2023.01); G06N 20/00 (2019.01); G06N 3/08 (2023.01); G06F 18/213 (2023.01); G06F 18/214 (2023.01); G06F 18/21 (2023.01); G06N 3/045 (2023.01); G06V 30/19 (2022.01); G06V 10/77 (2022.01); G06V 20/56 (2022.01);
U.S. Cl.
CPC ...
G06N 3/084 (2013.01); G06F 18/213 (2023.01); G06F 18/214 (2023.01); G06F 18/217 (2023.01); G06N 3/04 (2013.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06V 10/7715 (2022.01); G06V 20/56 (2022.01); G06V 30/1916 (2022.01); G06V 30/19127 (2022.01); G06V 30/19147 (2022.01);
Abstract

Systems, methods, tangible non-transitory computer-readable media, and devices associated with object localization and generation of compressed feature representations are provided. For example, a computing system can access source data and target data. The source data can include a source representation of an environment including a source object. The target data can include a compressed target feature representation of the environment. The compressed target feature representation can be based on compression of a target feature representation of the environment produced by machine-learned models. A source feature representation can be generated based on the source representation and the machine-learned models. The machine-learned models can include machine-learned feature extraction models or machine-learned attention models. A localized state of the source object with respect to the environment can be determined based on the source feature representation and the compressed target feature representation.

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