US 11,787,543 B2
Image space motion planning of an autonomous vehicle
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); and Adam Parker Bry, Redwood City, CA (US)
Assigned to Skydio, Inc., San Mateo, CA (US)
Filed by Skydio, Inc., Redwood City, CA (US)
Filed on Jan. 31, 2023, as Appl. No. 18/162,227.
Application 17/513,179 is a division of application No. 16/789,176, filed on Feb. 12, 2020, granted, now 11,347,244, issued on May 31, 2022.
Application 18/162,227 is a continuation of application No. 17/513,179, filed on Oct. 28, 2021, granted, now 11,592,845.
Application 16/789,176 is a continuation of application No. 15/671,743, filed on Aug. 8, 2017, granted, now 10,599,161, issued on Mar. 24, 2020.
Prior Publication US 2023/0257116 A1, Aug. 17, 2023
Int. Cl. G01C 21/34 (2006.01); B64C 39/02 (2023.01); G08G 5/00 (2006.01); G06T 7/593 (2017.01); G06T 17/05 (2011.01); G06T 7/246 (2017.01); G08G 5/04 (2006.01); G06T 7/277 (2017.01); G05D 1/10 (2006.01); G06V 20/13 (2022.01); G06V 20/17 (2022.01)
CPC B64C 39/024 (2013.01) [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 2201/10 (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)] 20 Claims
OG exemplary drawing
 
1. A system comprising:
a processing unit; and
a memory unit coupled to the processing unit, the memory unit including instructions stored thereon, which when executed by the processing unit, cause the system to:
generate a cost function map that associates a cost value with each of multiple regions of an image of a physical environment,
wherein the cost value is indicative of a level of risk associated with navigating in an area of the physical environment corresponding to the region of the image;
identify, based on the cost function map, a particular region of the multiple regions of the image that is associated with a higher cost value than one or more other regions of the multiple regions of the image; and
generate a 3D trajectory for an autonomous vehicle through the physical environment that avoids the particular region of the image to lesson an overall cost of the 3D trajectory.