System and method for vision-based flight self-stabilization by deep gated recurrent Q-networks
US10241520B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 22, 2016 |
| Grant date | Mar 26, 2019 |
| Priority date | — |
| Expiry date | Jul 20, 2037 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2211/441
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A system and a method for vision-based self-stabilization by deep gated recurrent Q-networks (DGRQNs) for unmanned arial vehicles (UAVs) are provided. The method comprises receiving a plurality of raw images captured by a camera installed on a UAV; receiving an initial reference image for stabilization and obtaining an initial camera pose from the initial reference image; extracting a fundamental matrix between consecutive images and estimating a current camera pose relative to the initial camera pose, wherein the camera pose includes an orientation and a location of the camera; based on the estimated current camera pose, predicting an action to counteract a lateral disturbance of the UAV based on the DGRQNs; and based on the predicted action to counteract the lateral disturbance of the UAV, driving the UAV back to the initial camera pose.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.