Obstacle avoidance using fused depth and intensity from nnt training
US12336675B2 · kind B2 · utility
0Cited by
6References
12Claims
0Family size
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Key dates
| Filing date | Apr 11, 2022 |
| Grant date | Jun 24, 2025 |
| Priority date | — |
| Expiry date | Feb 21, 2043 |
Classification
- Technology area (CPC A)Human Necessities
- CPC primaryA47L2201/04
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Embodiments provide a method for obstacle avoidance in a mobile robot. Images of objects are captured with mobile robot image sensors with a floor level perspective. Data corresponding to the images is used to train a neural network. The identified objects are classified by indicating whether the objects are a potential hazard. In one embodiment, an image sensor on a robot provides intensity and depth data for each of a plurality of pixels of the images. The intensity and depth data are fused to produce fused data. The fused data is then used to train the neural network.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.