公开(公告)号：US11934459B2

公开(公告)日：2024-03-19

申请号：US17799278

申请日：2021-09-22

Applicant: SHANGHAITECH UNIVERSITY

Inventor： Guangyao Yan ,  Xinzhe Liu ,  Yajun Ha ,  Hui Wang

IPC: G06T7/162 ,  G06F16/901 ,  G06T7/13

CPC classification number: G06F16/9024 ,  G06T7/13 ,  G06T7/162 ,  G06T2207/20072

Abstract: A ripple push method for a graph cut includes: obtaining an excess flow ef(v) of a current node v; traversing four edges connecting the current node v in top, bottom, left and right directions, and determining whether each of the four edges is a pushable edge; calculating, according to different weight functions, a maximum push value of each of the four edges by efw=ef(v)*W, where W denotes a weight function; and traversing the four edges, recording a pushable flow of each of the four edges, and pushing out a calculated flow. The ripple push method explores different push weight functions, and significantly improves the actual parallelism of the push-relabel algorithm.

公开(公告)号：US11845466B2

公开(公告)日：2023-12-19

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.