Object detection and classification using LiDAR range images for autonomous machine applications
US11906660B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 28, 2020 |
| Grant date | Feb 20, 2024 |
| Priority date | — |
| Expiry date | Jul 14, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG01S2013/9316
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
In various examples, a deep neural network (DNN) may be used to detect and classify animate objects and/or parts of an environment. The DNN may be trained using camera-to-LiDAR cross injection to generate reliable ground truth data for LiDAR range images. For example, annotations generated in the image domain may be propagated to the LiDAR domain to increase the accuracy of the ground truth data in the LiDAR domain—e.g., without requiring manual annotation in the LiDAR domain. Once trained, the DNN may output instance segmentation masks, class segmentation masks, and/or bounding shape proposals corresponding to two-dimensional (2D) LiDAR range images, and the outputs may be fused together to project the outputs into three-dimensional (3D) LiDAR point clouds. This 2D and/or 3D information output by the DNN may be provided to an autonomous vehicle drive stack to enable safe planning and control of the autonomous vehicle.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.