Normal distributions transform (NDT) method for LiDAR point cloud localization in unmanned driving
US11845466B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 22, 2021 |
| Grant date | Dec 19, 2023 |
| Priority date | — |
| Expiry date | Sep 22, 2041 |
Classification
- Technology area (CPC B)Performing Operations; Transporting
- CPC primaryB60W2554/4049
- WIPO fieldTransport
- WIPO sectorMechanical engineering
Abstract
A normal distributions transform (NDT) method for LiDAR point cloud localization in unmanned driving is provided. The method proposes a non-recursive, memory-efficient data structure occupation-aware-voxel-structure (OAVS), which speeds up each search operation. Compared with a tree-based structure, the proposed data structure OAVS is easy to parallelize and consumes only about 1/10 of memory. Based on the data structure OAVS, the method proposes a semantic-assisted OAVS-based (SEO)-NDT algorithm, which significantly reduces the number of search operations, redefines a parameter affecting the number of search operations, and removes a redundant search operation. In addition, the method proposes a streaming field-programmable gate array (FPGA) accelerator architecture, which further improves the real-time and energy-saving performance of the SEO-NDT algorithm. The method meets the real-time and high-precision requirements of smart vehicles for three-dimensional (3D) lidar localization.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.