# Low-Latency Event-Based Velocimetry for Quadrotor Control in a Narrow Pipe

arXiv: https://arxiv.org/abs/2507.15444 · HTML: https://mlsanity.com/paper/2507.15444/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 88 references that are in the map.
- First posted on arXiv 21 Jul 2025.

## Builds on (most cited first, 24 of 24)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Deep Recurrent Q-Learning for Partially Observable MDPs](https://mlsanity.com/paper/1507.06527/) (2015)
- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception](https://mlsanity.com/paper/1801.10202/) (2018)
- [EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras](https://mlsanity.com/paper/1802.06898/) (2018)
- [A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation](https://mlsanity.com/paper/1804.01306/) (2018)
- [Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning](https://mlsanity.com/paper/2310.10943/) (2023)
- [Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks](https://mlsanity.com/paper/2106.01862/) (2021)
- [Neural Lander: Stable Drone Landing Control using Learned Dynamics](https://mlsanity.com/paper/1811.08027/) (2018)
- [Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds](https://mlsanity.com/paper/2205.06908/) (2022)
- [How to Calibrate Your Event Camera](https://mlsanity.com/paper/2105.12362/) (2021)
- [The Double Sphere Camera Model](https://mlsanity.com/paper/1807.08957/) (2018)
- [A Benchmark Comparison of Learned Control Policies for Agile Quadrotor Flight](https://mlsanity.com/paper/2202.10796/) (2022)
- [NeuroBEM: Hybrid Aerodynamic Quadrotor Model](https://mlsanity.com/paper/2106.08015/) (2021)
- [Agilicious: Open-Source and Open-Hardware Agile Quadrotor for Vision-Based Flight](https://mlsanity.com/paper/2307.06100/) (2023)
- [Single-step deep reinforcement learning for open-loop control of laminar and turbulent flows](https://mlsanity.com/paper/2006.02979/) (2020)
- [A Neuromorphic Vision-Based Measurement for Robust Relative Localization in Future Space Exploration Missions](https://mlsanity.com/paper/2206.11541/) (2022)
- [Real-time 6-DoF Pose Estimation by an Event-based Camera using Active LED Markers](https://mlsanity.com/paper/2310.16618/) (2023)
- [Towards High-Speed Passive Visible Light Communication with Event Cameras and Digital Micro-Mirrors](https://mlsanity.com/paper/2410.14228/) (2024)
- [Robotics meets Fluid Dynamics: A Characterization of the Induced Airflow below a Quadrotor as a Turbulent Jet](https://mlsanity.com/paper/2403.13321/) (2024)
- [Autonomous Flights inside Narrow Tunnels](https://mlsanity.com/paper/2505.19657/) (2025)
- [Flying in air ducts](https://mlsanity.com/paper/2410.08379/) (2024)

## Built on by (most cited first, 2 of 2)

- [Autonomous UAV Flight Navigation in Confined Spaces: A Reinforcement Learning Approach](https://mlsanity.com/paper/2508.16807/) (2025)
- [Event-Based Visual Teach-and-Repeat via Fast Fourier-Domain Cross-Correlation](https://mlsanity.com/paper/2509.17287/) (2025)

## Abstract (arXiv)

Autonomous quadrotor flight in confined spaces such as pipes and tunnels presents significant challenges due to unsteady, self-induced aerodynamic disturbances. Very recent advances have enabled flight in such conditions, but they either rely on constant motion through the pipe to mitigate airflow recirculation effects or suffer from limited stability during hovering. In this work, we present the first closed-loop control system for quadrotors for hovering in narrow pipes that leverages real-time flow field measurements. We develop a low-latency, event-based smoke velocimetry method that estimates local airflow at high temporal resolution. This flow information is used by a disturbance estimator based on a recurrent convolutional neural network, which infers force and torque disturbances in real time. The estimated disturbances are integrated into a learning-based controller trained via reinforcement learning. The flow-feedback control proves particularly effective during lateral translation maneuvers in the pipe cross-section. There, the real-time disturbance information enables the controller to effectively counteract transient aerodynamic effects, thereby preventing collisions with the pipe wall. To the best of our knowledge, this work represents the first demonstration of an aerial robot with closed-loop control informed by real-time flow field measurements. This opens new directions for research on flight in aerodynamically complex environments. In addition, our work also sheds light on the characteristic flow structures that emerge during flight in narrow, circular pipes, providing new insights at the intersection of robotics and fluid dynamics.