实现无人机群快速视觉定位的高效标记检测方法
FIMD: Fast Isolated Marker Detection for UV-Based Visual Relative Localisation in Agile UAV Swarms
- 三重优化：CPU、GPU与FPGA协同加速标记检测
- 处理速度比现有方法提升二到三个数量级
- 适用于低配无人机，支持敏捷机群实时定位
本文提出一种新型快速机载孤立标记检测方法，用于敏捷无人机群的视觉相对定位。该检测是实时定位系统的关键环节，提出了三项创新：针对CPU的优化流程、GPU着色器程序以及功能等效的FPGA流式架构。在所提出的CPU与GPU方案中，输入相机帧的每像素平均处理时间相比未优化的最先进方法提升了二至三个数量级。在定位任务中，所提FPGA架构通过最小化从摄像头曝光到检测结果输出的总延迟，实现了最显著的整体加速。此外，该方案在多种32位和64位嵌入式平台上进行了评估，验证了其在低性能无人机和微型飞行器（MAVs）应用中的高效性与可行性。因此，该技术已成为敏捷无人机群协同的重要支撑。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。