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

• URL: http://arxiv.org/abs/2509.17287v1
• Date: Sun, 21 Sep 2025 23:53:31 GMT
• Title: Event-Based Visual Teach-and-Repeat via Fast Fourier-Domain Cross-Correlation
• Authors: Gokul B. Nair, Alejandro Fontan, Michael Milford, Tobias Fischer,
• Abstract summary: We present the first event-camera-based visual teach-and-repeat system.<n>We develop a frequency-domain cross-correlation framework that transforms the event stream matching problem into computationally efficient space multiplications.<n>Experiments using a Prophesee EVK4 HD event camera mounted on an AgileX Scout Mini robot demonstrate successful autonomous navigation.
• Score: 52.46888249268445
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual teach-and-repeat navigation enables robots to autonomously traverse previously demonstrated paths by comparing current sensory input with recorded trajectories. However, conventional frame-based cameras fundamentally limit system responsiveness: their fixed frame rates (typically 30-60 Hz) create inherent latency between environmental changes and control responses. Here we present the first event-camera-based visual teach-and-repeat system. To achieve this, we develop a frequency-domain cross-correlation framework that transforms the event stream matching problem into computationally efficient Fourier space multiplications, capable of exceeding 300Hz processing rates, an order of magnitude faster than frame-based approaches. By exploiting the binary nature of event frames and applying image compression techniques, we further enhance the computational speed of the cross-correlation process without sacrificing localization accuracy. Extensive experiments using a Prophesee EVK4 HD event camera mounted on an AgileX Scout Mini robot demonstrate successful autonomous navigation across 4000+ meters of indoor and outdoor trajectories. Our system achieves ATEs below 24 cm while maintaining consistent high-frequency control updates. Our evaluations show that our approach achieves substantially higher update rates compared to conventional frame-based systems, underscoring the practical viability of event-based perception for real-time robotic navigation.

Related papers

• Radar and Event Camera Fusion for Agile Robot Ego-Motion Estimation [27.282729603784496]
  We propose an IMU-free and feature-association-free framework to achieve aggressive ego-motion velocity estimation of a robot platform.<n>We use instantaneous raw events and Doppler measurements to derive rotational and translational velocities directly.<n>In the back-end, we propose a continuous-time state-space model to fuse the hybrid time-based and event-based measurements to estimate the ego-motion velocity in a fixed-lagged smoother fashion.
  arXiv  Detail & Related papers  (2025-06-23T09:27:22Z)
• Human-Robot Navigation using Event-based Cameras and Reinforcement Learning [1.7614751781649955]
  This work introduces a robot navigation controller that combines event cameras and other sensors with reinforcement learning to enable real-time human-centered navigation and obstacle avoidance.<n>Unlike conventional image-based controllers, which operate at fixed rates and suffer from motion blur and latency, this approach leverages the asynchronous nature of event cameras to process visual information over flexible time intervals.
  arXiv  Detail & Related papers  (2025-06-12T15:03:08Z)
• Dynamic Frame Interpolation in Wavelet Domain [57.25341639095404]
  Video frame is an important low-level computation vision task, which can increase frame rate for more fluent visual experience. Existing methods have achieved great success by employing advanced motion models and synthesis networks. WaveletVFI can reduce computation up to 40% while maintaining similar accuracy, making it perform more efficiently against other state-of-the-arts.
  arXiv  Detail & Related papers  (2023-09-07T06:41:15Z)
• EventTransAct: A video transformer-based framework for Event-camera based action recognition [52.537021302246664]
  Event cameras offer new opportunities compared to standard action recognition in RGB videos. In this study, we employ a computationally efficient model, namely the video transformer network (VTN), which initially acquires spatial embeddings per event-frame. In order to better adopt the VTN for the sparse and fine-grained nature of event data, we design Event-Contrastive Loss ($mathcalL_EC$) and event-specific augmentations.
  arXiv  Detail & Related papers  (2023-08-25T23:51:07Z)
• EV-Catcher: High-Speed Object Catching Using Low-latency Event-based Neural Networks [107.62975594230687]
  We demonstrate an application where event cameras excel: accurately estimating the impact location of fast-moving objects. We introduce a lightweight event representation called Binary Event History Image (BEHI) to encode event data at low latency. We show that the system is capable of achieving a success rate of 81% in catching balls targeted at different locations, with a velocity of up to 13 m/s even on compute-constrained embedded platforms.
  arXiv  Detail & Related papers  (2023-04-14T15:23:28Z)
• Dynamic Event-based Optical Identification and Communication [1.7289819674602298]
  temporal pattern recognition, depending on the technology, involves a trade-off between communication frequency, range and accurate tracking. We propose a solution with light-emitting beacons that improves this trade-off by exploiting fast event-based cameras. We demonstrate for the first time beacon tracking performed simultaneously with state-of-the-art frequency communication in the kHz range.
  arXiv  Detail & Related papers  (2023-03-13T15:12:30Z)
• Asynchronous Optimisation for Event-based Visual Odometry [53.59879499700895]
  Event cameras open up new possibilities for robotic perception due to their low latency and high dynamic range. We focus on event-based visual odometry (VO) We propose an asynchronous structure-from-motion optimisation back-end.
  arXiv  Detail & Related papers  (2022-03-02T11:28:47Z)
• TimeLens: Event-based Video Frame Interpolation [54.28139783383213]
  We introduce Time Lens, a novel indicates equal contribution method that leverages the advantages of both synthesis-based and flow-based approaches. We show an up to 5.21 dB improvement in terms of PSNR over state-of-the-art frame-based and event-based methods.
  arXiv  Detail & Related papers  (2021-06-14T10:33:47Z)
• Event Based, Near Eye Gaze Tracking Beyond 10,000Hz [41.23347304960948]
  We propose a hybrid frame-event-based near-eye gaze tracking system with update rates beyond 10,000 Hz. Our system builds on emerging event cameras that simultaneously acquire regularly sampled frames and adaptively sampled events. We hope to enable a new generation of ultra-low-latency gaze-contingent rendering and display techniques for virtual and augmented reality.
  arXiv  Detail & Related papers  (2020-04-07T17:57:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.