Learning based camera pose estimation from images of an environment
US10692244B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 20, 2018 |
| Grant date | Jun 23, 2020 |
| Priority date | — |
| Expiry date | Dec 15, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30244
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A deep neural network (DNN) system learns a map representation for estimating a camera position and orientation (pose). The DNN is trained to learn a map representation corresponding to the environment, defining positions and attributes of structures, trees, walls, vehicles, etc. The DNN system learns a map representation that is versatile and performs well for many different environments (indoor, outdoor, natural, synthetic, etc.). The DNN system receives images of an environment captured by a camera (observations) and outputs an estimated camera pose within the environment. The estimated camera pose is used to perform camera localization, i.e., recover the three-dimensional (3D) position and orientation of a moving camera, which is a fundamental task in computer vision with a wide variety of applications in robot navigation, car localization for autonomous driving, device localization for mobile navigation, and augmented/virtual reality.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.