# Monocular Event-Based Vision for Obstacle Avoidance with a Quadrotor

arXiv: https://arxiv.org/abs/2411.03303 · HTML: https://mlsanity.com/paper/2411.03303/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2024.

## Built on by (most cited first, 14 of 14)

- [Drone Detection with Event Cameras](https://mlsanity.com/paper/2508.04564/) (2025)
- [SIGN: Safety-Aware Image-Goal Navigation for Autonomous Drones via Reinforcement Learning](https://mlsanity.com/paper/2508.12394/) (2025)
- [Unleashing the Temporal Potential of Stereo Event Cameras for Continuous-Time 3D Object Detection](https://mlsanity.com/paper/2508.02288/) (2025)
- [Towards Low-Latency Event-based Obstacle Avoidance on a FPGA-Drone](https://mlsanity.com/paper/2504.10400/) (2025)
- [Event-based Graph Representation with Spatial and Motion Vectors for Asynchronous Object Detection](https://mlsanity.com/paper/2507.15150/) (2025)
- [Learning to Remove Lens Flare in Event Camera](https://mlsanity.com/paper/2512.09016/) (2025)
- [RGB-Event Fusion with Self-Attention for Collision Prediction](https://mlsanity.com/paper/2505.04258/) (2025)
- [Beyond Domain Randomization: Event-Inspired Perception for Visually Robust Adversarial Imitation from Videos](https://mlsanity.com/paper/2505.18899/) (2025)
- [Context-aware Sparse Spatiotemporal Learning for Event-based Vision](https://mlsanity.com/paper/2508.19806/) (2025)
- [EREBUS: End-to-end Robust Event Based Underwater Simulation](https://mlsanity.com/paper/2511.01381/) (2025)
- [Approximate Imitation Learning for Event-based Quadrotor Flight in Cluttered Environments](https://mlsanity.com/paper/2603.07578/) (2026)
- [E-TIDE: Fast, Structure-Preserving Motion Forecasting from Event Sequences](https://mlsanity.com/paper/2603.27757/) (2026)
- [EROAM: Event-based Camera Rotational Odometry and Mapping in Real-time](https://mlsanity.com/paper/2411.11004/) (2024)
- [An End-to-end Flight Control Network for High-speed UAV Obstacle Avoidance based on Event-Depth Fusion](https://mlsanity.com/paper/2603.27181/) (2026)

## Abstract (arXiv)

We present the first static-obstacle avoidance method for quadrotors using just an onboard, monocular event camera. Quadrotors are capable of fast and agile flight in cluttered environments when piloted manually, but vision-based autonomous flight in unknown environments is difficult in part due to the sensor limitations of traditional onboard cameras. Event cameras, however, promise nearly zero motion blur and high dynamic range, but produce a very large volume of events under significant ego-motion and further lack a continuous-time sensor model in simulation, making direct sim-to-real transfer not possible. By leveraging depth prediction as a pretext task in our learning framework, we can pre-train a reactive obstacle avoidance events-to-control policy with approximated, simulated events and then fine-tune the perception component with limited events-and-depth real-world data to achieve obstacle avoidance in indoor and outdoor settings. We demonstrate this across two quadrotor-event camera platforms in multiple settings and find, contrary to traditional vision-based works, that low speeds (1m/s) make the task harder and more prone to collisions, while high speeds (5m/s) result in better event-based depth estimation and avoidance. We also find that success rates in outdoor scenes can be significantly higher than in certain indoor scenes.