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:
May. 13, 2025

Filed:

Sep. 08, 2023
Applicant:

Skydio, Inc., San Mateo, CA (US);

Inventors:

Ryan David Kennedy, San Francisco, CA (US);

Peter Benjamin Henry, San Francisco, CA (US);

Hayk Martirosyan, San Francisco, CA (US);

Jack Louis Zhu, San Mateo, CA (US);

Abraham Galton Bachrach, Emerald Hills, CA (US);

Adam Parker Bry, Redwood City, CA (US);

Assignee:

Skydio, Inc., San Mateo, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01C 21/34 (2006.01); B64C 39/02 (2023.01); B64U 10/14 (2023.01); G05D 1/00 (2006.01); G06T 7/246 (2017.01); G06T 7/277 (2017.01); G06T 7/593 (2017.01); G06T 17/05 (2011.01); G06V 20/13 (2022.01); G06V 20/17 (2022.01); G08G 5/00 (2006.01); G08G 5/04 (2006.01); B64U 101/32 (2023.01);
U.S. Cl.
CPC ...
B64C 39/024 (2013.01); B64U 10/14 (2023.01); G01C 21/3453 (2013.01); G05D 1/106 (2019.05); G06T 7/246 (2017.01); G06T 7/277 (2017.01); G06T 7/593 (2017.01); G06T 17/05 (2013.01); G06V 20/13 (2022.01); G06V 20/17 (2022.01); G08G 5/0069 (2013.01); G08G 5/045 (2013.01); B64U 2101/32 (2023.01); B64U 2201/10 (2023.01); B64U 2201/20 (2023.01); G06T 2207/10021 (2013.01); G06T 2207/10032 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30188 (2013.01); G06T 2207/30241 (2013.01); G06T 2207/30252 (2013.01);
Abstract

An autonomous vehicle that is equipped with image capture devices can use information gathered from the image capture devices to plan a future three-dimensional (3D) trajectory through a physical environment. To this end, a technique is described for image-space based motion planning. In an embodiment, a planned 3D trajectory is projected into an image-space of an image captured by the autonomous vehicle. The planned 3D trajectory is then optimized according to a cost function derived from information (e.g., depth estimates) in the captured image. The cost function associates higher cost values with identified regions of the captured image that are associated with areas of the physical environment into which travel is risky or otherwise undesirable. The autonomous vehicle is thereby encouraged to avoid these areas while satisfying other motion planning objectives.


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