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

TL;DR

This paper presents a novel onboard sensing and computing approach enabling autonomous quadrotors to aggressively fly through narrow gaps without prior environment knowledge, by actively controlling orientation and replanning trajectories based on real-time perception.

Contribution

It introduces a new method for quadrotor navigation through narrow gaps using only onboard sensors, combining active vision, trajectory planning, and real-time replanning.

Findings

Successful real-world experiments demonstrate the effectiveness of the approach.

First autonomous flight through narrow gaps using only onboard sensing and computing.

Trajectory replanning improves robustness against state estimation uncertainty.

Abstract

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.