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:
Aug. 30, 2022

Filed:

Mar. 17, 2020
Applicant:

Nexar Ltd., Tel Aviv, IL;

Inventors:

Lev Yitzhak Lavy, Misgav Dov, IL;

Shmuel Rippa, Ramat Gan, IL;

Elad Levi, Tel Aviv, IL;

Roei Herzig, Hod Hasharon, IL;

Eliahu Brosh, Ramat Hasharon, IL;

Assignee:

Nexar, Ltd., Tel Aviv, IL;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04N 5/225 (2006.01); G01C 21/32 (2006.01); G06K 9/62 (2022.01); G01S 19/42 (2010.01); G06V 20/10 (2022.01); G06V 20/56 (2022.01);
U.S. Cl.
CPC ...
G01C 21/32 (2013.01); G01S 19/42 (2013.01); G06K 9/6256 (2013.01); G06V 20/176 (2022.01); G06V 20/182 (2022.01); G06V 20/588 (2022.01); H04N 5/2253 (2013.01);
Abstract

A system for training simultaneous localization and mapping (SLAM) models, including a camera, mounted in a vehicle and in communication with an image server via a cellular connection, that captures images labeled with a geographic position system location and a timestamp, and uploads them to an image server, a storage device that stores geographical maps and images, and indexes the images geographically with reference to the geographical maps, an images server that receives uploaded images, labels the uploaded images with a GPS location and a timestamp, and stores the uploaded images on the storage device, and a training server that trains a SLAM model using images labeled with a GPS location and a timestamp, wherein the SLAM model (i) receives an image as input and predicts the image location as output, and/or (ii) receives an image having error as input and predicts a local correction for the image as output.


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