US 11,887,323 B2
Self-supervised estimation of observed vehicle pose
Punarjay Chakravarty, Campbell, CA (US); Tinne Tuytelaars, Korbeek-Lo (BE); Cédric Picron, Meise (BE); and Tom Roussel, Heverlee (BE)
Assigned to Ford Global Technologies, LLC, Dearborn, MI (US)
Filed by Ford Global Technologies, LLC, Dearborn, MI (US)
Filed on Jun. 8, 2020, as Appl. No. 16/895,267.
Prior Publication US 2021/0383167 A1, Dec. 9, 2021
Int. Cl. G06T 7/70 (2017.01); G06N 3/08 (2023.01); G06V 20/58 (2022.01); G06V 10/82 (2022.01); G06F 18/214 (2023.01)
CPC G06T 7/70 (2017.01) [G06F 18/214 (2023.01); G06N 3/08 (2013.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01); G06V 20/584 (2022.01); G06T 2207/10024 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30252 (2013.01)] 16 Claims
OG exemplary drawing
 
1. A system comprising a computer including a processor and a memory, the memory including instructions such that the processor is programmed to:
receive a first image captured by a camera at a first time instance, wherein the first image includes at least a portion of an observed vehicle;
determine a first ray angle based on a coordinate system of an ego-vehicle and a first coordinate system of the observed vehicle based on the first image;
receive a second image captured by the camera at a second time instance, wherein the second image includes at least a portion of the observed vehicle oriented at a different viewpoint;
determine a second ray angle based on the coordinate system of the ego-vehicle and a second coordinate system of the observed vehicle based on the second image;
receive vehicle odometry data;
determine a local angle difference based on the first ray angle, the second ray angle, and the vehicle odometry data; and
train a deep neural network using the local angle difference, the first image, and the second image to output a position of the ego-vehicle with respect to the observed vehicle for operating the ego-vehicle,
wherein the deep neural network comprises a Siamese neural network and wherein the Siamese neural network determines a first local angle based on a first color image and a second local angle based on a second color image and determines a contrastive loss based on a difference between the first local angle and the second local angle.