US 12,217,450 B2
Vehicle localization
Ming Xu, West End (AU); Sourav Garg, Kelvin Grove/Brisbane (AU); Michael Milford, Gaythorne (AU); Punarjay Chakravarty, Campbell, CA (US); and Shubham Shrivastava, Santa Clara, CA (US)
Assigned to Ford Global Technologies, LLC, Dearborn, MI (US)
Filed by Ford Global Technologies, LLC, Dearborn, MI (US)
Filed on Feb. 8, 2022, as Appl. No. 17/666,609.
Prior Publication US 2023/0252667 A1, Aug. 10, 2023
Int. Cl. G06T 7/70 (2017.01); G06V 10/46 (2022.01)
CPC G06T 7/70 (2017.01) [G06V 10/46 (2022.01); G06T 2207/20084 (2013.01); G06T 2207/30244 (2013.01); G06T 2207/30252 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer, comprising:
a processor; and
a memory, the memory including instructions executable by the processor to:
determine an approximate camera location on a route by inputting a first image acquired by a camera included in a vehicle to a convolutional neural network;
extract first image feature points from the first image;
select pose estimation parameters for a pose estimation algorithm based on the approximate camera location;
determine a six degree-of-freedom (DoF) camera pose by inputting the first image feature points and second feature points included in a structure-from-motion (SfM) map based on the route to the pose estimation algorithm which is controlled by the pose estimation parameters, wherein the SfM map includes a collection of three-dimensional points visible from the route generated by determining three-dimensional locations of image feature points in global coordinates from the dataset of reference images and combining them using a 3D mapping software program; and
determine a six DoF vehicle pose based on the six DoF camera pose.