公开(公告)号：US20240230907A1

公开(公告)日：2024-07-11

申请号：US18387859

申请日：2023-11-08

Applicant: SHANGHAITECH UNIVERSITY

Inventor： Jianzhong XIAO ,  Hao SUN ,  Qi DENG ,  Yajun HA

IPC: G01S17/89 ,  G06T7/70

CPC classification number: G01S17/89 ,  G06T7/70 ,  G06T2207/10028 ,  G06T2207/20021

Abstract: An efficient K-nearest neighbor (KNN) method for a single-frame point cloud of a LiDAR and an application of the efficient KNN method for the single-frame point cloud of the LiDAR are provided, where the efficient KNN method for the single-frame point cloud of the LiDAR is accelerated by a field-programmable gate array (FPGA). In the efficient KNN method for the single-frame point cloud of the LiDAR, a data structure is established based on point cloud projection and a distance scale. The data structure ensures that adjacent points in space are organized in adjacent memories. A new data structure is efficiently constructed. An efficient nearest point search mode is provided.

公开(公告)号：US20230192123A1

公开(公告)日：2023-06-22

申请号：US17802148

申请日：2021-09-22

Applicant: SHANGHAITECH UNIVERSITY

Inventor： Qi DENG ,  Hao SUN ,  Yajun HA ,  Hui WANG

IPC: B60W60/00

CPC classification number: B60W60/001 ,  B60W2420/52 ,  B60W2554/4049

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.