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:
Mar. 28, 2023

Filed:

Jul. 31, 2019
Applicant:

Brain Corporation, San Diego, CA (US);

Inventors:

Ali Mirzaei, San Diego, CA (US);

Oleg Sinyavskiy, San Diego, CA (US);

Assignee:

Brain Corporation, San Diego, CA (US);

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 19/00 (2018.01); B25J 9/16 (2006.01); G06N 3/08 (2023.01); G06T 7/10 (2017.01); G06T 7/90 (2017.01); G06K 9/62 (2022.01); G05D 1/02 (2020.01); G06V 10/56 (2022.01); G06V 20/10 (2022.01);
U.S. Cl.
CPC ...
B25J 9/1666 (2013.01); G05D 1/0221 (2013.01); G06K 9/6263 (2013.01); G06K 9/6267 (2013.01); G06N 3/08 (2013.01); G06T 7/10 (2017.01); G06T 7/90 (2017.01); G06V 10/56 (2022.01); G06V 20/10 (2022.01); G05D 1/0246 (2013.01); G05D 2201/0203 (2013.01); G05D 2201/0215 (2013.01); G06K 9/6271 (2013.01);
Abstract

Systems, apparatuses, and methods for rapid machine learning for floor segmentation for robotic devices are disclosed herein. According to at least one non-limiting exemplary embodiment, a robotic system is disclosed. The robotic system may comprise a neural network embodied therein capable of learning associations between color values of pixels and corresponding classifications of those pixels, wherein neural network is trained initially to identify floor and non-floor pixels within images. A user input may be provided to the neural network to further configure the neural network to be able to identify navigable floors and unnavigable floors unique to an environment without a need for additional annotated training images specific to the environment.


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