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Efficient FPGA Implementation of K-Nearest-Neighbor Search Algorithm for 3D LIDAR Localization and Mapping in Smart Vehicles
IEEE Transactions on Circuits and Systems II: Express Briefs ( IF 4.4 ) Pub Date : 2020-09-01 , DOI: 10.1109/tcsii.2020.3013758
Hao Sun , Xinzhe Liu , Qi Deng , Weixiong Jiang , Shaobo Luo , Yajun Ha

K-Nearest-Neighbor search (KNN) has been extensively used in the localization and mapping based on 3D laser point clouds in smart vehicles. Considering the real-time requirement of localization and stringent battery constraint in smart vehicles, it is a great challenge to develop highly energy-efficient KNN implementations. Unfortunately, previous KNN implementations either cannot efficiently build search data structures or cannot search efficiently in massive and unevenly distributed point clouds. To solve the issue, we propose a new framework to optimize the implementation of KNN on FPGAs. First, we propose a novel data structure with a spatial subdivision method, which can be built efficiently even for massive point clouds. Second, based on our data structure, we propose a KNN search algorithm which is able to search in unevenly distributed point clouds efficiently. We have implemented the new framework on both FPGA and GPU. Energy efficiency results show that our proposed method is on average 2.1 times and 6.2 times higher than the state-of-the-art implementations of KNN on FPGA and GPU platform, respectively.

中文翻译:

用于智能汽车 3D LIDAR 定位和映射的 K-最近邻搜索算法的高效 FPGA 实现

K-Nearest-Neighbor search (KNN) 已广泛用于智能汽车中基于 3D 激光点云的定位和映射。考虑到智能汽车对本地化的实时性要求和严格的电池约束,开发高能效的 KNN 实现是一个巨大的挑战。不幸的是,以前的 KNN 实现要么无法有效地构建搜索数据结构,要么无法在大量且分布不均的点云中进行有效搜索。为了解决这个问题,我们提出了一个新的框架来优化 KNN 在 FPGA 上的实现。首先,我们提出了一种具有空间细分方法的新型数据结构,即使对于大量点云也可以有效构建。其次,基于我们的数据结构,我们提出了一种 KNN 搜索算法,该算法能够有效地在不均匀分布的点云中进行搜索。我们已经在 FPGA 和 GPU 上实现了新框架。能效结果表明,我们提出的方法平均分别比 FPGA 和 GPU 平台上 KNN 的最新实现高 2.1 倍和 6.2 倍。
更新日期:2020-09-01
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