论文标题
基于球体的SLAM SLAM ACELERATOR在SOC FPGA上
ORB-based SLAM accelerator on SoC FPGA
论文作者
论文摘要
同时本地化和映射（SLAM）是自主导航系统的主要组成部分之一。随着无人机的受欢迎程度的提高，低功率系统上的自动导航正在广泛应用。大多数SLAM算法都是计算密集型的，并且很难以合理的精度在嵌入式设备上实时运行。 Orb-Slam是一种开源的基于功能的大满贯，可以通过降低计算复杂性来实现高精度。我们提出了一个基于SOC的Orb-Slam系统，该系统可以加速计算密集的视觉特征提取并在硬件上匹配。与ARM CPU，Intel Desktop CPU和最先进的FPGA系统相比，我们基于ZYNQ家庭SOC的FPGA系统的运行速度快8.5倍，1.55倍和1.35倍，而与FPGA上的先前工作相比，我们的FPGA系统分别分别为最先进的FPGA系统。
Simultaneous Localization and Mapping (SLAM) is one of the main components of autonomous navigation systems. With the increase in popularity of drones, autonomous navigation on low-power systems is seeing widespread application. Most SLAM algorithms are computationally intensive and struggle to run in real-time on embedded devices with reasonable accuracy. ORB-SLAM is an open-sourced feature-based SLAM that achieves high accuracy with reduced computational complexity. We propose an SoC based ORB-SLAM system that accelerates the computationally intensive visual feature extraction and matching on hardware. Our FPGA system based on a Zynq-family SoC runs 8.5x, 1.55x and 1.35x faster compared to an ARM CPU, Intel Desktop CPU, and a state-of-the-art FPGA system respectively, while averaging a 2x improvement in accuracy compared to prior work on FPGA.
