用事件相机实现毫秒级实时导航，误差小于15厘米
Event-Based Visual Teach-and-Repeat via Fast Fourier-Domain Cross-Correlation
- 将事件流匹配转为傅里叶域乘法，加速计算
- 处理延迟仅2.88毫秒，比传统方案快3.5倍
- 适用于昼夜室内外复杂场景，适合移动机器人
视觉教-重复（VT&R）导航使机器人能基于视觉反馈自主沿先前演示路径行驶。本文提出一种基于事件相机的新型VT&R系统。该系统将事件流匹配建模为频域互相关，将空间卷积转化为高效的傅里叶域乘法。通过利用事件帧的二值结构并应用图像压缩技术，实现了仅2.88毫秒的处理延迟，约为优化后的传统相机基线方案的3.5倍速度提升。在搭载Prophesee EVK4 HD事件相机的AgileX Scout Mini机器人上进行实验，成功完成3000多米室内外昼夜条件下的自主导航。系统保持横向轨迹误差（XTE）低于15厘米，证明了事件感知在实时VT&R导航中的实用性。
原文摘要 · Abstract (English)
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.
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