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. 22, 2025

Filed:

Dec. 29, 2023
Applicant:

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

Inventors:

Saumitro Dasgupta, Redwood City, CA (US);

Hayk Martirosyan, San Francisco, CA (US);

Hema Koppula, Palo Alto, CA (US);

Alex Kendall, Cambridge, GB;

Austin Stone, San Francisco, CA (US);

Matthew Donahoe, Redwood City, 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:
Assistant Examiner:
Int. Cl.
CPC ...
G06V 20/13 (2022.01); B64C 39/02 (2023.01); B64U 10/14 (2023.01); G05D 1/00 (2024.01); G06F 18/2431 (2023.01); G06N 3/045 (2023.01); G06T 3/60 (2024.01); G06T 7/10 (2017.01); G06T 7/11 (2017.01); G06T 7/20 (2017.01); G06T 7/292 (2017.01); G06T 7/579 (2017.01); G06T 7/73 (2017.01); G06V 10/82 (2022.01); G06V 20/17 (2022.01); G06V 30/262 (2022.01); H04N 13/00 (2018.01); H04N 13/239 (2018.01); H04N 13/243 (2018.01); H04N 13/282 (2018.01); B64U 101/00 (2023.01); B64U 101/30 (2023.01); B64U 101/31 (2023.01);
U.S. Cl.
CPC ...
G06V 20/13 (2022.01); B64C 39/024 (2013.01); B64U 10/14 (2023.01); G05D 1/0011 (2013.01); G05D 1/0094 (2013.01); G06F 18/2431 (2023.01); G06T 3/60 (2013.01); G06T 7/10 (2017.01); G06T 7/11 (2017.01); G06T 7/20 (2013.01); G06T 7/292 (2017.01); G06T 7/579 (2017.01); G06T 7/75 (2017.01); G06V 10/82 (2022.01); G06V 20/17 (2022.01); G06V 30/274 (2022.01); H04N 13/239 (2018.05); H04N 13/243 (2018.05); H04N 13/282 (2018.05); B64U 2101/00 (2023.01); B64U 2101/30 (2023.01); B64U 2101/31 (2023.01); B64U 2201/10 (2023.01); G06N 3/045 (2023.01); G06T 2207/10012 (2013.01); G06T 2207/10028 (2013.01); G06T 2207/10032 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20088 (2013.01); G06T 2207/30196 (2013.01); G06T 2207/30241 (2013.01); H04N 2013/0081 (2013.01); H04N 2013/0085 (2013.01); H04N 2013/0092 (2013.01);
Abstract

Systems and methods are disclosed for tracking objects in a physical environment using visual sensors onboard an autonomous unmanned aerial vehicle (UAV). In certain embodiments, images of the physical environment captured by the onboard visual sensors are processed to extract semantic information about detected objects. Processing of the captured images may involve applying machine learning techniques such as a deep convolutional neural network to extract semantic cues regarding objects detected in the images. The object tracking can be utilized, for example, to facilitate autonomous navigation by the UAV or to generate and display augmentative information regarding tracked objects to users.


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