- 专利标题: Systems and methods for generating annotations of structured, static objects in aerial imagery using geometric transfer learning and probabilistic localization
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申请号: US16669309申请日: 2019-10-30
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公开(公告)号: US11100667B2公开(公告)日: 2021-08-24
- 发明人: Dinuka Abeywardena
- 申请人: Wing Aviation LLC
- 申请人地址: US CA Mountain View
- 专利权人: Wing Aviation LLC
- 当前专利权人: Wing Aviation LLC
- 当前专利权人地址: US CA Mountain View
- 代理机构: Christensen O'Connor Johnson Kindness PLLC
- 主分类号: G06T7/70
- IPC分类号: G06T7/70
摘要:
In some embodiments, aerial images of a geographic area are captured by an autonomous vehicle. In some embodiments, the locations of structures within a subset of the aerial images are manually annotated, and geographical locations of the manual annotations are determined based on pose information of the camera. In some embodiments, a machine learning model is trained using the manually annotated aerial images. The machine learning model is used to automatically generate annotations of other images of the geographic area, and the geographical locations determined from the manual annotations are used to determine an accuracy probability of the automatic annotations. The automatic annotations determined to be accurate may be used to re-train the machine learning model to increase its precision and recall.
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