{"ID":2868286,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.17287","arxiv_id":"2509.17287","title":"Event-Based Visual Teach-and-Repeat via Fast Fourier-Domain Cross-Correlation","abstract":"Visual teach-and-repeat (VT\u0026R) navigation enables robots to autonomously traverse previously demonstrated paths using visual feedback. We present a novel event-camera-based VT\\\u0026R 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\\\u0026R navigation.","short_abstract":"Visual teach-and-repeat (VT\u0026R) navigation enables robots to autonomously traverse previously demonstrated paths using visual feedback. We present a novel event-camera-based VT\\\u0026R system. Our system formulates event-stream matching as frequency-domain cross-correlation, transforming spatial convolutions into efficient F...","url_abs":"https://arxiv.org/abs/2509.17287","url_pdf":"https://arxiv.org/pdf/2509.17287v2","authors":"[\"Gokul B. Nair\",\"Alejandro Fontan\",\"Michael Milford\",\"Tobias Fischer\"]","published":"2025-09-21T23:53:31Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.CV\"]","methods":"[]","has_code":false}