基于快速傅里叶域交叉相关性的事件视觉教与重复
Event-Based Visual Teach-and-Repeat via Fast Fourier-Domain Cross-Correlation
- Queensland University of Technology（昆士兰理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出基于事件相机的快速VT&R导航系统，通过频域交叉相关技术实现高效事件流匹配，实验验证其在复杂环境下的高精度导航能力。
AI中文摘要：
视觉教与重复（VT&R）导航使机器人能够利用视觉反馈自主穿越已演示过的路径。我们提出了一种新的基于事件相机的VT&R系统。我们的系统将事件流匹配建模为频域交叉相关，将空间卷积转换为高效的傅里叶空间乘法。通过利用事件帧的二进制结构并应用图像压缩技术，我们实现了仅2.88毫秒的处理延迟，比传统相机基线快约3.5倍。使用安装在AgileX Scout Mini机器人上的Prophesee EVK4 HD事件相机进行的实验表明，成功在日间和夜间条件下自主导航超过3000米的室内外轨迹。我们的系统保持横向误差（XTE）低于15厘米，证明了基于事件的感知在实时VT&R导航中的实用性。
英文摘要：
Visual teach-and-repeat (VT&R) navigation enables robots to autonomously traverse previously demonstrated paths using visual feedback. We present a novel event-camera-based VT\&R system. Our system formulates event-stream matching as frequency-domain cross-correlation, transforming spatial convolutions into efficient Fourier-space multiplications. By exploiting the binary structure of event frames and applying image compression techniques, we achieve a processing latency of just 2.88 ms, about 3.5 times faster than conventional camera-based baselines that are optimised for runtime efficiency. Experiments using a Prophesee EVK4 HD event camera mounted on an AgileX Scout Mini robot demonstrate successful autonomous navigation across 3000+ meters of indoor and outdoor trajectories in daytime and nighttime conditions. Our system maintains Cross-Track Errors (XTE) below 15 cm, demonstrating the practical viability of event-based perception for real-time VT\&R navigation.
