Machine-learned monocular depth estimation and semantic segmentation for 6-DOF absolute localization of a delivery drone
US12307710B2 · kind B2 · utility
Assignee
Inventor
Key dates
| Filing date | Jul 18, 2022 |
| Grant date | May 20, 2025 |
| Priority date | — |
| Expiry date | Apr 29, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20084
- WIPO fieldControl
- WIPO sectorInstruments
Abstract
A method includes receiving a two-dimensional (2D) image captured by a camera on a unmanned aerial vehicle (UAV) and representative of an environment of the UAV. The method further includes applying a trained machine learning model to the 2D image to produce a semantic image of the environment and a depth image of the environment, where the semantic image comprises one or more semantic labels. The method additionally includes retrieving reference depth data representative of the environment, wherein the reference depth data includes reference semantic labels. The method also includes aligning the depth image of the environment with the reference depth data representative of the environment to determine a location of the UAV in the environment, where the aligning associates the one or more semantic labels from the semantic image with the reference semantic labels from the reference depth data.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.