US 12,283,099 B2
Semantic abort of unmanned aerial vehicle deliveries
Ali Shoeb, San Rafael, CA (US); Marcus Hammond, Redwood City, CA (US); Christopher Cobar, Shoreview, MN (US); Kyle Krafka, Los Altos, CA (US); Kyle Julian, Mountain View, CA (US); and Kevin Jenkins, Dallas, TX (US)
Assigned to Wing Aviation LLC, Palo Alto, CA (US)
Filed by Wing Aviation LLC, Mountain View, CA (US)
Filed on Mar. 31, 2022, as Appl. No. 17/657,558.
Prior Publication US 2023/0316739 A1, Oct. 5, 2023
Int. Cl. G06V 20/17 (2022.01); B64C 39/02 (2023.01); G05D 1/00 (2006.01); G06Q 10/0832 (2023.01); G06T 7/11 (2017.01); G06T 7/70 (2017.01); G06V 10/774 (2022.01); B64U 101/30 (2023.01); B64U 101/60 (2023.01)
CPC G06V 20/17 (2022.01) [B64C 39/024 (2013.01); G05D 1/0094 (2013.01); G05D 1/0607 (2013.01); G06Q 10/0832 (2013.01); G06T 7/11 (2017.01); G06T 7/70 (2017.01); G06V 10/774 (2022.01); B64U 2101/30 (2023.01); B64U 2101/60 (2023.01); B64U 2201/10 (2023.01); G06T 2207/10028 (2013.01); G06T 2207/10032 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30261 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
capturing, by a sensor on an unmanned aerial vehicle (UAV), an image of a delivery location;
determining, based on the image of the delivery location, a segmentation image, wherein the segmentation image segments the delivery location into a plurality of pixel areas with corresponding semantic classifications;
determining, based on the segmentation image, a percentage of obstacle pixels within a surrounding area of a delivery point at the delivery location, wherein each obstacle pixel has a semantic classification indicative of an obstacle in the delivery location; and
based on the percentage of obstacle pixels being above a threshold percentage, aborting a delivery process of the UAV.