FIMD：敏捷无人机集群中基于紫外视觉相对定位的快速孤立标记检测
FIMD: Fast Isolated Marker Detection for UV-Based Visual Relative Localisation in Agile UAV Swarms
AI总结：
本文提出FIMD方法，通过CPU、GPU和FPGA三重优化，实现敏捷无人机集群中孤立标记的快速检测，加速两到三个数量级，并验证了在低端嵌入式平台上的可行性。
摘要：
本文介绍了一种用于敏捷无人机集群中多队友视觉相对定位的孤立标记快速机载检测新方法。由于该检测构成实时定位系统的关键组成部分，本文提出了一项三重创新，包括针对CPU的优化程序、GPU着色器程序以及功能等效的FPGA流式架构。对于所提出的CPU和GPU解决方案，与未优化的最先进方法相比，输入相机帧每像素的平均处理时间加速了两到三个数量级。对于定位任务，所提出的FPGA架构通过最小化从相机曝光到检测结果的总延迟，提供了最显著的整体加速。此外，所提出的解决方案在多种32位和64位嵌入式平台上进行了评估，以展示其效率以及在使用低端无人机和微型飞行器（MAV）的应用中的可行性。因此，它已成为敏捷无人机集群的关键使能技术。
英文摘要：
A novel approach for the fast onboard detection of isolated markers for visual relative localisation of multiple teammates in agile UAV swarms is introduced in this paper. As the detection forms a key component of real-time localisation systems, a three-fold innovation is presented, consisting of an optimised procedure for CPUs, a GPU shader program, and a functionally equivalent FPGA streaming architecture. For the proposed CPU and GPU solutions, the mean processing time per pixel of input camera frames was accelerated by two to three orders of magnitude compared to the \rev{unoptimised state-of-the-art approach}. For the localisation task, the proposed FPGA architecture offered the most significant overall acceleration by minimising the total delay from camera exposure to detection results. Additionally, the proposed solutions were evaluated on various 32-bit and 64-bit embedded platforms to demonstrate their efficiency, as well as their feasibility for applications using low-end UAVs and MAVs. Thus, it has become a crucial enabling technology for agile UAV swarming.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
