# Towards Low-Latency Event-based Obstacle Avoidance on a FPGA-Drone

arXiv: https://arxiv.org/abs/2504.10400 · HTML: https://mlsanity.com/paper/2504.10400/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 26 references that are in the map.
- First posted on arXiv 14 Apr 2025.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [EVDodgeNet: Deep Dynamic Obstacle Dodging with Event Cameras](https://mlsanity.com/paper/1906.02919/) (2019)
- [Fully neuromorphic vision and control for autonomous drone flight](https://mlsanity.com/paper/2303.08778/) (2023)
- [Agilicious: Open-Source and Open-Hardware Agile Quadrotor for Vision-Based Flight](https://mlsanity.com/paper/2307.06100/) (2023)
- [NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems](https://mlsanity.com/paper/2304.04640/) (2023)
- [Retina : Low-Power Eye Tracking with Event Camera and Spiking Hardware](https://mlsanity.com/paper/2312.00425/) (2023)
- [Event-based Agile Object Catching with a Quadrupedal Robot](https://mlsanity.com/paper/2303.17479/) (2023)
- [Monocular Event-Based Vision for Obstacle Avoidance with a Quadrotor](https://mlsanity.com/paper/2411.03303/) (2024)
- [Fast Motion Understanding with Spatiotemporal Neural Networks and Dynamic Vision Sensors](https://mlsanity.com/paper/2011.09427/) (2020)
- [Fast Trajectory End-Point Prediction with Event Cameras for Reactive Robot Control](https://mlsanity.com/paper/2302.13796/) (2023)
- [A real-time dynamic obstacle tracking and mapping system for UAV navigation and collision avoidance with an RGB-D camera](https://mlsanity.com/paper/2209.08258/) (2022)
- [Free as a Bird: Event-based Dynamic Sense-and-Avoid for Ornithopter Robot Flight](https://mlsanity.com/paper/2310.09875/) (2023)
- [Night vision obstacle detection and avoidance based on Bio-Inspired Vision Sensors](https://mlsanity.com/paper/2010.15509/) (2020)

## Built on by (most cited first, 3 of 3)

- [RGB-Event Fusion with Self-Attention for Collision Prediction](https://mlsanity.com/paper/2505.04258/) (2025)
- [HOMI: Ultra-Fast EdgeAI platform for Event Cameras](https://mlsanity.com/paper/2508.12637/) (2025)
- [Event-Based Vision in Space: Applications, Trends, and Future Directions](https://mlsanity.com/paper/2606.01280/) (2026)

## Abstract (arXiv)

This work quantitatively evaluates the performance of event-based vision systems (EVS) against conventional RGB-based models for action prediction in collision avoidance on an FPGA accelerator. Our experiments demonstrate that the EVS model achieves a significantly higher effective frame rate (1 kHz) and lower temporal (-20 ms) and spatial prediction errors (-20 mm) compared to the RGB-based model, particularly when tested on out-of-distribution data. The EVS model also exhibits superior robustness in selecting optimal evasion maneuvers. In particular, in distinguishing between movement and stationary states, it achieves a 59 percentage point advantage in precision (78% vs. 19%) and a substantially higher F1 score (0.73 vs. 0.06), highlighting the susceptibility of the RGB model to overfitting. Further analysis in different combinations of spatial classes confirms the consistent performance of the EVS model in both test data sets. Finally, we evaluated the system end-to-end and achieved a latency of approximately 2.14 ms, with event aggregation (1 ms) and inference on the processing unit (0.94 ms) accounting for the largest components. These results underscore the advantages of event-based vision for real-time collision avoidance and demonstrate its potential for deployment in resource-constrained environments.