Aggressive quadrotor flight through narrow gaps with onboard sensing and computing using active vision

By Davide Falanga, Elias Mueggler, Matthias Faessler, Davide Scaramuzza. 2016

196 citations recorded by the source corpus.

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 in the world reference frame, 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, achieving a success rate of 80% and gap orientations up to 45°. 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.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

Deep Drone Racing: Learning Agile Flight in Dynamic Environments (2018, methodology): “[7] by replacing our perception system (Section 3) with the handcrafted detector and leaving the control system (Section 3) unchanged.”
A 64-mW DNN-Based Visual Navigation Engine for Autonomous Nano-Drones (2018, methodology): “The front-looking conﬁguration can be used for many navigation tasks like path planning [41], obstacle avoidance [42], trajectory optimization [9], to name a few.”
PAMPC: Perception-Aware Model Predictive Control for Quadrotors (2018, background): “This made it possible to replicate some of the impressive quadrotor maneuvers seen in the last decade [1], [2], [3], [4], which relied on motion-capture systems, using only onboard sensing, such as cameras and IMUs [5], [6], [7].”
The current state and future outlook of rescue robotics (2019, background): “, 2016), for flying (Mintchev and Floreano, 2016; Falanga et al., 2017), legged (Hutter et al.”
Geometrically Constrained Trajectory Optimization for Multicopters (2021, methodology): “It should be highlighted that our method differs from traditional planners for narrow gaps (Falanga et al. 2017; Loianno et al. 2016) in its flexibility, efficiency and high fidelity.”
Extending the Lifetime of Nano-Blimps via Dynamic Motor Control (2018, unknown): “The most common rotorcraft is the quadrotor, that has four rotors and changes the rotation ratio among them to generate lift [10].”