Neural network architecture system for deep odometry assisted by static scene optical flow
US10671083B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 13, 2017 |
| Grant date | Jun 2, 2020 |
| Priority date | — |
| Expiry date | Mar 5, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30248
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A system for visual odometry is disclosed. The system includes: an internet server, comprising: an I/O port, configured to transmit and receive electrical signals to and from a client device; a memory; one or more processing units; and one or more programs stored in the memory and configured for execution by the one or more processing units, the one or more programs including instructions for: extracting representative features from a pair input images in a first convolution neural network (CNN) in a visual odometry model; merging, in a first merge module, outputs from the first CNN; decreasing feature map size in a second CNN; generating a first flow output for each layer in a first deconvolution neural network (DNN); merging, in a second merge module, outputs from the second CNN and the first DNN; generating a second flow output for each layer in a second DNN; and reducing accumulated errors in a recurrent neural network (RNN).
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.