Invention Grant
- Patent Title: Neural network object pose determination
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Application No.: US17484159Application Date: 2021-09-24
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Publication No.: US12073588B2Publication Date: 2024-08-27
- Inventor: Mostafa Parchami , Enrique Corona , Kunjan Singh , Gaurav Pandey
- Applicant: Ford Global Technologies, LLC
- Applicant Address: US MI Dearborn
- Assignee: Ford Global Technologies, LLC
- Current Assignee: Ford Global Technologies, LLC
- Current Assignee Address: US MI Dearborn
- Agency: Bejin Bieneman PLC
- Agent Frank A. MacKenzie
- Main IPC: G06T7/80
- IPC: G06T7/80 ; G06N3/04 ; G06T7/73 ; G06V30/194

Abstract:
A camera is positioned to obtain an image of an object. The image is input to a neural network that outputs a three-dimensional (3D) bounding box for the object relative to a pixel coordinate system and object parameters. Then a center of a bottom face of the 3D bounding box is determined in pixel coordinates. The bottom face of the 3D bounding box is located in a ground plane in the image. Based on calibration parameters for the camera that transform pixel coordinates into real-world coordinates, a) a distance from the center of the bottom face of the 3D bounding box to the camera relative to a real-world coordinate system and b) an angle between a line extending from the camera to the center of the bottom face of the 3D bounding box and an optical axis of the camera are determined. The calibration parameters include a camera height relative to the ground plane, a camera focal distance, and a camera tilt relative to the ground plane. A six degree-of-freedom (6DoF) pose for the object is determined based on the object parameters, the distance, and the angle.
Public/Granted literature
- US20230145701A1 NEURAL NETWORK OBJECT POSE DETERMINATION Public/Granted day:2023-05-11
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