在Zynq SoC上进行高通量ORB特征提取,用于实时结构-从-运动管道
Panteleimon Stamatakis1, John Vourvoulakis1
1Department of Computer, Informatics & Telecommunications Engineering, International Hellenic University, Terma Magnisias, 62121 Serres, Greece.
Journal of imaging
|June 25, 2025
概括
本研究介绍了一个实时特征检测和描述系统,使用FPGA上的优化ORB算法,实现结构从运动管道的低延迟.
科学领域:
- 计算机视觉 计算机视觉
- 嵌入式系统 嵌入式系统
- 机器人技术 机器人技术 机器人技术
背景情况:
- 结构从运动 (SfM) 管道需要有效的实时特征检测和描述.
- 现有的方法经常面临高分辨率,高率视频处理的挑战.
- 现场可编程门阵列 (FPGA) 提供了加速计算密集型计算机视觉任务的潜力.
研究的目的:
- 为SfM管道的初始阶段开发和实施实时特征检测和描述系统.
- 在FPGA上利用优化的ORB算法来实现高吞吐量视频处理.
- 为了实现适合实时应用的低延迟.
主要方法:
- 在Xilinx Zynq-7000 SoC FPGA (Digilent Zybo Z7020) 上实现了一个优化的面向FAST和旋转BRIEF (ORB) 算法.
- 哈里斯函数采用双数据速率管道方案,以支持在全高清分辨率 (1920 × 1080) 上高速率 (60 fps).
- 功能描述符在FPGA上计算,并使用Xilinx的ILA IP块进行验证.
主要成果:
- 该系统实现了192.7微秒的低延迟,证明其适用于实时应用.
- 实施的系统处理了实时视频输入 (60fps,1920 × 1080,24位颜色).
- 评估显示,在可重复性,匹配保留和在图像转换下匹配准确性方面,准确性和性能令人满意.
结论:
- 开发的基于FPGA的系统为特征检测和描述提供了高效的实时解决方案,这是SfM的第一阶段.
- 这种硬件加速方法很有希望,可以在同一平台上实现随后的SfM阶段.
- 这项工作为推进需要快速视觉处理的实时计算机视觉和机器人应用奠定了基础.
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