Identifying and/or removing false positive detections from LIDAR sensor output
US11740335B2 · kind B2 · utility
1Cited by
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20Claims
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Key dates
| Filing date | Apr 16, 2020 |
| Grant date | Aug 29, 2023 |
| Priority date | — |
| Expiry date | Aug 26, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/045
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A machine-learned (ML) model for detecting that depth data (e.g., lidar data, radar data) comprises a false positive attributable to particulate matter, such as dust, steam, smoke, rain, etc. The ML model may be trained based at least in part on simulated depth data generated by a fluid dynamics model and/or by collecting depth data during operation of a device (e.g., an autonomous vehicle. In some examples, an autonomous vehicle may identify depth data that may be associated with particulate matter based at least in part on an outlier region in a thermal image. For example, the outlier region may be associated with steam.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.