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:
Jul. 28, 2020

Filed:

May. 03, 2018
Applicant:

Zoox, Inc., Foster City, CA (US);

Inventors:

Tencia Lee, San Francisco, CA (US);

Sabeek Mani Pradhan, Stanford, CA (US);

Dragomir Dimitrov Anguelov, San Francisco, CA (US);

Assignee:

Zoox, Inc., Foster City, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/521 (2017.01); G06T 7/11 (2017.01); G06T 7/77 (2017.01); G06N 20/00 (2019.01); G05D 1/02 (2020.01); G01S 17/931 (2020.01);
U.S. Cl.
CPC ...
G06T 7/521 (2017.01); G01S 17/931 (2020.01); G05D 1/024 (2013.01); G05D 1/0212 (2013.01); G06N 20/00 (2019.01); G06T 7/11 (2017.01); G06T 7/77 (2017.01); G06T 2207/20081 (2013.01); G06T 2207/30261 (2013.01);
Abstract

A monocular image often does not contain enough information to determine, with certainty, the depth of an object in a scene reflected in the image. Combining image data and LIDAR data may enable determining a depth estimate of the object relative to the camera. Specifically, LIDAR points corresponding to a region of interest ('ROI') in the image that corresponds to the object may be combined with the image data. These LIDAR points may be scored according to a monocular image model and/or a factor based on a distance between projections of the LIDAR points into the ROI and a center of the region of interest may improve the accuracy of the depth estimate. Using these scores as weights in a weighted median of the LIDAR points may improve the accuracy of the depth estimate, for example, by discerning between a detected object and an occluding object and/or background.


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