# Event-Based Visual Teach-and-Repeat via Fast Fourier-Domain Cross-Correlation

arXiv: https://arxiv.org/abs/2509.17287 · HTML: https://mlsanity.com/paper/2509.17287/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 54 references that are in the map.
- First posted on arXiv 21 Sep 2025.

## Builds on (most cited first, 22 of 22)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios](https://mlsanity.com/paper/1709.06310/) (2017)
- [Event-based Stereo Visual Odometry](https://mlsanity.com/paper/2007.15548/) (2020)
- [DDD20 End-to-End Event Camera Driving Dataset: Fusing Frames and Events with Deep Learning for Improved Steering Prediction](https://mlsanity.com/paper/2005.08605/) (2020)
- [Event-based visual place recognition with ensembles of temporal windows](https://mlsanity.com/paper/2006.02826/) (2020)
- [Fully neuromorphic vision and control for autonomous drone flight](https://mlsanity.com/paper/2303.08778/) (2023)
- [How Many Events do You Need? Event-based Visual Place Recognition Using Sparse But Varying Pixels](https://mlsanity.com/paper/2206.13673/) (2022)
- [Dataset and Benchmark: Novel Sensors for Autonomous Vehicle Perception](https://mlsanity.com/paper/2401.13853/) (2024)
- [Fast and Robust Bio-inspired Teach and Repeat Navigation](https://mlsanity.com/paper/2010.11326/) (2020)
- [Enhancing Visual Place Recognition via Fast and Slow Adaptive Biasing in Event Cameras](https://mlsanity.com/paper/2403.16425/) (2024)
- [A compact neuromorphic system for ultra-energy-efficient, on-device robot localization](https://mlsanity.com/paper/2408.16754/) (2024)
- [EV-Planner: Energy-Efficient Robot Navigation via Event-Based Physics-Guided Neuromorphic Planner](https://mlsanity.com/paper/2307.11349/) (2023)
- [NYC-Event-VPR: A Large-Scale High-Resolution Event-Based Visual Place Recognition Dataset in Dense Urban Environments](https://mlsanity.com/paper/2410.21615/) (2024)
- [Kilometer-scale autonomous navigation in subarctic forests: challenges and lessons learned](https://mlsanity.com/paper/2111.13981/) (2021)
- [Navigation without localisation: reliable teach and repeat based on the convergence theorem](https://mlsanity.com/paper/1711.05348/) (2017)
- [Autobiasing Event Cameras](https://mlsanity.com/paper/2411.00729/) (2024)
- [Radar Teach and Repeat: Architecture and Initial Field Testing](https://mlsanity.com/paper/2409.10491/) (2024)
- [Low-Latency Event-Based Velocimetry for Quadrotor Control in a Narrow Pipe](https://mlsanity.com/paper/2507.15444/) (2025)
- [Teach and Repeat Navigation: A Robust Control Approach](https://mlsanity.com/paper/2309.15405/) (2023)
- [Multi-Platform Teach-and-Repeat Navigation by Visual Place Recognition Based on Deep-Learned Local Features](https://mlsanity.com/paper/2503.13090/) (2025)
- [Toward Teach and Repeat Across Seasonal Deep Snow Accumulation](https://mlsanity.com/paper/2505.01339/) (2025)

## Abstract (arXiv)

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.