Efficient and scalable three-dimensional point cloud segmentation for navigation in autonomous vehicles
US10921455B2 · kind B2 · utility
Assignee
Inventor
Key dates
| Filing date | Apr 4, 2019 |
| Grant date | Feb 16, 2021 |
| Priority date | — |
| Expiry date | Jun 21, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30252
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
Efficient and scalable three-dimensional point cloud segmentation. In an embodiment, a three-dimensional point cloud is segmented by adding points to a spatial hash. For each unseen point, a cluster is generated, the unseen point is added to the cluster and marked as seen, and, for each point that is added to the cluster, the point is set as a reference, a reference threshold metric is computed, all unseen neighbors are identified based on the reference threshold metric, and, for each identified unseen neighbor, the unseen neighbor is marked as seen, a neighbor threshold metric is computed, and the neighbor is added or not added to the cluster based on the neighbor threshold metric. When the cluster reaches a threshold size, it may be added to a cluster list. Objects may be identified based on the cluster list and used to control autonomous system(s).
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.