上海科技大学知识管理系统

提供了一种用于大规模点云的快速且节能的K-最近邻搜索加速器。构建了基于双分割体素结构(DSVS)搜索结构进行搜索的最近子体素选择(NSVS)框架，并在现场可编程门阵列(FPGA)上实现了大规模点云地图的K近邻搜索算法。K-最近邻搜索加速器被配置为构造DSVS搜索结构，并基于DSVS搜索结构搜索K-最近邻。在KITTI数据集上的实验结果表明，K-最近邻搜索加速器的搜索速度比最先进的FPGA实现快9.1倍。此外，K-最近邻搜索加速器还实现了最佳能量效率，并且最佳能量效率分别比最先进的FPGA和GPU实现高11.5倍和13.5倍。

A fast and energy-efficient K-nearest neighbors search accelerator for a large-scale point cloud is provided. A nearest sub-voxel-selection (NSVS) framework that performs search based on a double-segmentation-voxel-structure (DSVS) search structure is constructed, and a K-nearest neighbors search algorithm for a large-scale point cloud map is implemented on a field programmable gate array (FPGA). The K-nearest neighbors search accelerator is configured for constructing the DSVS search structure, and searching for K-nearest neighbors based on the DSVS search structure. An experimental result on a KITTI dataset shows that the K-nearest neighbors search accelerator has a search speed 9.1 times faster than a state-of-the-art FPGA implementation. In addition, the K-nearest neighbors search accelerator also achieves an optimal energy efficiency, and the optimal energy efficiency is 11.5 times and 13.5 times higher than state-of-the-art FPGA and GPU implementations respectively.