# Aggressive Quadrotor Flight through Narrow Gaps with Onboard Sensing and Computing using Active Vision

arXiv: https://arxiv.org/abs/1612.00291 · HTML: https://mlsanity.com/paper/1612.00291/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2016.

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

- [Demonstrating Agile Flight from Pixels without State Estimation](https://mlsanity.com/paper/2406.12505/) (2024)
- [Learning Perception-Aware Agile Flight in Cluttered Environments](https://mlsanity.com/paper/2210.01841/) (2022)
- [The Artificial Intelligence behind the winning entry to the 2019 AI Robotic Racing Competition](https://mlsanity.com/paper/2109.14985/) (2021)
- [TACO: General Acrobatic Flight Control via Target-and-Command-Oriented Reinforcement Learning](https://mlsanity.com/paper/2503.01125/) (2025)
- [Learning Agile Gate Traversal via Analytical Optimal Policy Gradient](https://mlsanity.com/paper/2508.21592/) (2025)
- [Autonomous Aerial Robot for High-Speed Search and Intercept Applications](https://mlsanity.com/paper/2112.05465/) (2021)
- [Learning Vision-Based Agile Gap Traversal: Differentiable Simulation with a Warm-Started Critic](https://mlsanity.com/paper/2609.30696/) (2026)

## Abstract (arXiv)

We address one of the main challenges towards autonomous quadrotor flight in complex environments, which is flight through narrow gaps. While previous works relied on off-board localization systems or on accurate prior knowledge of the gap position and orientation, we rely solely on onboard sensing and computing and estimate the full state by fusing gap detection from a single onboard camera with an IMU. This problem is challenging for two reasons: (i) the quadrotor pose uncertainty with respect to the gap increases quadratically with the distance from the gap; (ii) the quadrotor has to actively control its orientation towards the gap to enable state estimation (i.e., active vision). We solve this problem by generating a trajectory that considers geometric, dynamic, and perception constraints: during the approach maneuver, the quadrotor always faces the gap to allow state estimation, while respecting the vehicle dynamics; during the traverse through the gap, the distance of the quadrotor to the edges of the gap is maximized. Furthermore, we replan the trajectory during its execution to cope with the varying uncertainty of the state estimate. We successfully evaluate and demonstrate the proposed approach in many real experiments. To the best of our knowledge, this is the first work that addresses and achieves autonomous, aggressive flight through narrow gaps using only onboard sensing and computing and without prior knowledge of the pose of the gap.