Fast Marker Detection for UV-Based Visual Relative Localisation in Agile UAV Swarms

2025-10-22

AI Summary

To address the urgent need for real-time isolated marker detection in vision-based relative localization for agile, high-speed collaborative drone swarms, this paper proposes a cross-platform efficient detection method. Methodologically, we innovatively design a CPU-optimized lightweight pipeline, a GPU shader-accelerated program, and a functionally equivalent FPGA streaming architecture, integrated with UV imaging and ultra-low-latency image processing algorithms. Our key contributions are threefold: (1) the first unified modeling and co-optimization across CPU, GPU, and FPGA platforms; (2) a 100×–1000× improvement in per-pixel processing speed over state-of-the-art methods; and (3) an FPGA implementation achieving minimal end-to-end latency from exposure to detection—demonstrating feasibility on resource-constrained, low-end drones. This work significantly enhances the robustness and scalability of real-time multi-drone pose estimation.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

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 state of the art. 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.

Problem

• Fast onboard marker detection for visual relative localization in UAV swarms
• Accelerating processing time by orders of magnitude compared to existing methods
• Enabling real-time localization for agile drone swarms using embedded platforms

Innovation

• Optimized CPU procedure for fast marker detection
• GPU shader program accelerating pixel processing speed
• FPGA streaming architecture minimizing detection latency