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：US20220148281A1

公开(公告)日：2022-05-12

申请号：US17593852

申请日：2021-06-09

Applicant: SHANGHAITECH UNIVERSITY

Inventor： Hao SUN ,  Yajun HA

IPC: G06V10/22 ,  G06T7/10 ,  G06V20/50

Abstract: An efficient K-nearest neighbor search algorithm for three-dimensional (3D) lidar point cloud in unmanned driving and a use of the foregoing K-nearest neighbor search algorithm in a point cloud map matching process in the unmanned driving are provided. A novel data structure for fast K-nearest neighbor search is used, such that each voxel or sub-voxel includes a proper quantity of points to reduce redundant search. The novel K-nearest neighbor search algorithm is based on a double segmentation voxel structure (DSVS) and a field programmable gate array (FPGA). By means of the novel K-nearest neighbor search algorithm, nearest neighbors are searched for only in a neighboring expected area near a search point, thereby reducing search of redundant points. In addition, an optimized data transmission and access policy is used, which makes the algorithm more fit the characteristic of the FPGA.

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.

公开(公告)号：US20240127466A1

公开(公告)日：2024-04-18

申请号：US18369884

申请日：2023-09-19

Applicant: SHANGHAITECH UNIVERSITY

Inventor： Hao SUN ,  Yajun HA

IPC: G06T7/521 ,  G06F18/2135

CPC classification number: G06T7/521 ,  G06F18/2135 ,  G06T2207/10028

Abstract: An energy-efficient point cloud feature extraction method based on a field-programmable gate array (FPGA) is mapped onto the FPGA for running. The energy-efficient point cloud feature extraction method based on the FPGA is applied to point cloud feature extraction in unmanned driving; or an intelligent robot. Compared with an existing technical solution, the energy-efficient point cloud feature extraction method based on the FPGA has following innovative points: a low-complexity projection method for organizing unordered and sparse point clouds, a high-parallel method for extracting a coarse-grained feature point, and a high-parallel method for selecting a fine-grained feature point.