# Towards Low-Latency High-Bandwidth Control of Quadrotors using Event Cameras

arXiv: https://arxiv.org/abs/1911.04553 · HTML: https://mlsanity.com/paper/1911.04553/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2019.

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

- [Event-driven Vision and Control for UAVs on a Neuromorphic Chip](https://mlsanity.com/paper/2108.03694/) (2021)
- [Representation Learning for Event-based Visuomotor Policies](https://mlsanity.com/paper/2103.00806/) (2021)
- [Are High-Resolution Event Cameras Really Needed?](https://mlsanity.com/paper/2203.14672/) (2022)
- [EvDNeRF: Reconstructing Event Data with Dynamic Neural Radiance Fields](https://mlsanity.com/paper/2310.02437/) (2023)
- [Event Camera Based Real-Time Detection and Tracking of Indoor Ground Robots](https://mlsanity.com/paper/2102.11916/) (2021)
- [Adversarial Attack for Asynchronous Event-based Data](https://mlsanity.com/paper/2112.13534/) (2021)
- [Real-time event simulation with frame-based cameras](https://mlsanity.com/paper/2209.04634/) (2022)
- [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)
- [Segment Any Events via Weighted Adaptation of Pivotal Tokens](https://mlsanity.com/paper/2312.16222/) (2023)
- [Neuromorphic Attitude Estimation and Control](https://mlsanity.com/paper/2411.13945/) (2024)
- [Segment Any Events with Language](https://mlsanity.com/paper/2601.23159/) (2026)
- [Lattice-allocated Real-time Line Segment Feature Detection and Tracking Using Only an Event-based Camera](https://mlsanity.com/paper/2510.06829/) (2025)
- [Low-Latency Scalable Streaming for Event-Based Vision](https://mlsanity.com/paper/2412.07889/) (2024)
- [Learning Quantized Continuous Controllers for Integer Hardware](https://mlsanity.com/paper/2511.07046/) (2025)

## Abstract (arXiv)

Event cameras are a promising candidate to enable high speed vision-based control due to their low sensor latency and high temporal resolution. However, purely event-based feedback has yet to be used in the control of drones. In this work, a first step towards implementing low-latency high-bandwidth control of quadrotors using event cameras is taken. In particular, this paper addresses the problem of one-dimensional attitude tracking using a dualcopter platform equipped with an event camera. The event-based state estimation consists of a modified Hough transform algorithm combined with a Kalman filter that outputs the roll angle and angular velocity of the dualcopter relative to a horizon marked by a black-and-white disk. The estimated state is processed by a proportional-derivative attitude control law that computes the rotor thrusts required to track the desired attitude. The proposed attitude tracking scheme shows promising results of event-camera-driven closed loop control: the state estimator performs with an update rate of 1 kHz and a latency determined to be 12 ms, enabling attitude tracking at speeds of over 1600 deg/s.