Lidar localization using RNN and LSTM for temporal smoothness in autonomous driving vehicles
US11364931B2 · kind B2 · utility
Assignees
Inventors
Key dates
| Filing date | Jan 30, 2019 |
| Grant date | Jun 21, 2022 |
| Priority date | — |
| Expiry date | Sep 5, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30241
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In one embodiment, a method for temporal smoothness in localization results for an autonomous driving vehicle includes: creating a probability offset volume that represents an overall matching cost between a first set of keypoints from the online point cloud and a second set of keypoints from a pre-built point cloud map for each of a series of sequential light detection and ranging (LiDAR) frames in an online point cloud. The method also includes compressing the probability offset volume into multiple probability vectors across a X dimension, a Y dimension and a yaw dimension; providing each probability vector of the probability offset volume to a number of recurrent neural networks (RNNs); and generating, by the RNNs, a trajectory of location results across the plurality of sequential LiDAR frames.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.