# Event-based Navigation for Autonomous Drone Racing with Sparse Gated Recurrent Network

arXiv: https://arxiv.org/abs/2204.02120 · HTML: https://mlsanity.com/paper/2204.02120/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2022.

## Built on by (most cited first, 7 of 7)

- [ColibriUAV: An Ultra-Fast, Energy-Efficient Neuromorphic Edge Processing UAV-Platform with Event-Based and Frame-Based Cameras](https://mlsanity.com/paper/2305.18371/) (2023)
- [Pushing the boundaries of event subsampling in event-based video classification using CNNs](https://mlsanity.com/paper/2409.08953/) (2024)
- [Towards Task-Oriented Flying: Framework, Infrastructure, and Principles](https://mlsanity.com/paper/2504.15129/) (2025)
- [Approximate Imitation Learning for Event-based Quadrotor Flight in Cluttered Environments](https://mlsanity.com/paper/2603.07578/) (2026)
- [Edged USLAM: Edge-Aware Event-Based SLAM with Learning-Based Depth Priors](https://mlsanity.com/paper/2603.08150/) (2026)
- [Fuzzy Logic Theory-based Adaptive Reward Shaping for Robust Reinforcement Learning (FARS)](https://mlsanity.com/paper/2604.15772/) (2026)
- [Neuromorphic Object Detection: An In-Depth Study and Future Directions](https://mlsanity.com/paper/2607.23576/) (2026)

## Abstract (arXiv)

Event-based vision has already revolutionized the perception task for robots by promising faster response, lower energy consumption, and lower bandwidth without introducing motion blur. In this work, a novel deep learning method based on gated recurrent units utilizing sparse convolutions for detecting gates in a race track is proposed using event-based vision for the autonomous drone racing problem. We demonstrate the efficiency and efficacy of the perception pipeline on a real robot platform that can safely navigate a typical autonomous drone racing track in real-time. Throughout the experiments, we show that the event-based vision with the proposed gated recurrent unit and pretrained models on simulated event data significantly improve the gate detection precision. Furthermore, an event-based drone racing dataset consisting of both simulated and real data sequences is publicly released.