Event-Camera VT&R: Fast, Robust Robot Navigation

• The paper presents an event-camera-based VT&R system that uses asynchronous event streams and frequency-domain cross-correlation for real-time, high-update-rate localization.
• It demonstrates robust trajectory following and heading error correction with update rates exceeding 300 Hz and latencies as low as 3.3 ms.
• Experimental results show that the system outperforms frame-based methods by reducing motion blur and computational costs while enhancing navigation accuracy.

Event-camera-based visual teach-and-repeat (VT&R) systems constitute a class of robot navigation frameworks that leverage the high temporal resolution and asynchronous nature of neuromorphic or dynamic vision sensors. These systems enable robots to autonomously and repeatedly traverse demonstrated trajectories by processing event streams—sequences of brightness change notifications—rather than conventional frame-based images. The salient properties of event cameras (microsecond temporal resolution, low latency, reduced motion blur, and wide dynamic range) distinguish them from standard cameras and motivate specialized algorithms that exploit these modalities for robust real-time navigation, obstacle avoidance, and environmental adaptation.

1. Principles of Event-Camera-Based VT&R

Event cameras operate asynchronously: each pixel independently reports intensity changes when detected, resulting in a continuous, sparse event stream. In VT&R, the teach phase involves the robot executing and recording a reference trajectory; in the repeat phase, the robot uses event-driven sensory input to correct its motion and maintain correspondence with previously demonstrated paths (Nair et al., 21 Sep 2025). Unlike classical VT&R, which depends on frame-based image matching at fixed rates (30–60 Hz), event-based systems can produce localization and control updates far above standard rates (e.g., >300 Hz), yielding rapid responsiveness and minimizing latency between environment changes and robot actions.

The event stream is often accumulated into binary event frames over time windows τ—creating a representation Iₖ(u,v) indicating the occurrence of events within τ at each pixel location (u,v). These frames serve as the basis for both localization and heading correction during the repeat phase. Event-camera VT&R systems avoid redundant data processing and are robust against motion blur, a common issue in conventional frame-based cameras during high-speed maneuvers or rapid environmental changes (Perez-Salesa et al., 2022).

2. Efficient Localization via Frequency-Domain Cross-Correlation

A central contribution in contemporary event-camera VT&R research is the frequency-domain cross-correlation approach for localization (Nair et al., 21 Sep 2025). During repeat navigation, the current event frame (ˆI) is compared against a bank of teach-phase frames (I_j) extracted from the topometric path memory. Instead of direct spatial convolution, which is computationally intensive (O(N²)), the process is accelerated via Fast Fourier Transform (FFT), yielding complexity O(N log N):

$$P_j = \mathcal{F}^{-1}( \mathcal{F}(I_j) \cdot \mathcal{F}(\hat{I})^* )$$

where $\mathcal{F}$ denotes the Fourier transform, $*$ is the biaxial flip, and the multiplication is element-wise. The resulting cross-correlation map P_j quantifies visual similarity between current and reference event frames; the offset δ_j at the maximum of P_j is translated into a heading correction:

$$\delta_j = \operatorname{argmax}_{\delta \in [-w/2,w/2]} P_j$$

$\theta_j = \frac{FOV}{w} \cdot \delta_j$

(Here, FOV is the camera field of view, w the image width.)

Computational enhancement is achieved through:

• Compression: Binary event frames allow summation kernels that reduce dimensionality (e.g., horizontal reduction by M=8), lowering FFT computational cost.
• Spatial Concatenation: Multiple teach frames are concatenated prior to FFT, enabling a single transform for rapid search of multiple trajectory locations.

Vision updates with these optimizations achieve latencies as low as 3.3 ms (302 Hz update rate) on commodity hardware.

3. Control Strategies and Error Convergence

Control in event-based VT&R systems commonly relies on heading correction derived from the cross-correlation localization offset, supplementing onboard odometry. The foundational mathematical model, as described in (Krajnik et al., 2017), represents the robot's deviation from the reference path by the error state (e, θ) (lateral and heading error):

$$\dot{e} = v\sin\theta , \quad \dot{\theta} = \omega - \kappa(s)v$$

where v is forward velocity, ω is angular velocity, κ(s) trajectory curvature. A Lyapunov function:

$V(e,\theta) = \frac{1}{2}e^2 + \frac{1}{2}\theta^2$

demonstrates that under a heading-correction-based control law, the error is contractive:

$$\dot{V}(e,\theta) \leq -\lambda V(e,\theta), \quad \lambda > 0$$

This assures exponential stability—robot positional error does not diverge over time. When combined with high-frequency event-based corrections, the robot remains closely bound to the taught trajectory despite odometry drift and environmental disturbances, provided sufficient geometric features are present in the event stream.

4. Experimental Demonstrations and Performance Metrics

A fully event-camera-based VT&R system has been demonstrated on an AgileX Scout Mini robot equipped with a Prophesee EVK4 HD (Nair et al., 21 Sep 2025). The robot recorded >4000 m trajectories—indoors (corridors, confined spaces) and outdoors (paved walkways, grass)—with >302 Hz control updates. Absolute Trajectory Errors (ATE) remained below 24 cm in all trials, and localization failures never occurred across 16 runs. The system consistently outperformed odometry-only and conventional frame-based VT&R in both update rate and path-tracking precision.

Event frames were accumulated over τ = 66 ms, downsampled to 320 × 180 pixels, and teach frames were recorded every 0.2 m or 15°. Multiple teach frames (±4) were searched in cross-correlation to account for drift. Heading corrections derived from event correlations were fused with odometry for closed-loop path following.

5. Comparison with Frame-Based Approaches and Practical Implications

Relative to frame-based approaches, event-camera VT&R systems offer:

• Order-of-magnitude increase in update rate: 302 Hz vs. 30–60 Hz typical for standard cameras.
• Lowered latency: 3.3 ms reaction time vs. >16 ms (at 60 Hz).
• Robustness to motion blur and varying illumination: Event cameras maintain feature integrity under rapid movement and high dynamic range scenarios, giving them clear advantages in environments prone to lighting variability or fast robot motion (Perez-Salesa et al., 2022).

Computational costs are reduced due to sparse and binary event frames, and the avoidance of reconstructing full intensity images. These systems are particularly effective on resource-constrained platforms requiring real-time navigation across large or dynamic environments.

6. Future Directions and Integration with Other Modalities

Future research may focus on:

• Sensor fusion: Incorporating RGB cameras or LiDAR to provide richer environmental context and enhanced localization.
• Advanced motion estimation: Improving odometric fusion using information extracted directly from event streams.
• High-speed applications: Deploying on aerial platforms, autonomous vehicles, or rapid ground robots, where microsecond response is critical.
• Algorithmic refinement: Adaptive windowing, dynamic feature selection, and robust handling of dynamic elements in the environment.

A plausible implication is the extension of frequency-domain processing and error-convergent control to multi-modal perception, combining event-based vision with semantic features or map-based object landmarks to further enhance reliability under challenging real-world conditions.

7. Challenges and Considerations

• Feature extraction: Sparse nature of event frames may require specialized algorithms for robust geometric feature matching.
• Drift compensation: Odometry drift remains conceivable if event-based corrections become unavailable; periodic event-rich landmarks may mitigate this.
• Calibration-free operation: Event sensors possess noise and bias characteristics distinct from standard cameras—requiring careful integration for heading correction feedback.

Overall, event-camera-based visual teach-and-repeat systems harness the advantages of neuromorphic vision to deliver fast, robust, and computationally efficient autonomous navigation, demonstrating practical viability across diverse scenarios and laying a foundation for continued advancements in microsecond-latency robot guidance (Nair et al., 21 Sep 2025).