Automatic re-initialization and failure recovery for aggressive flight with a monocular vision-based quadrotor
By Matthias Faessler, Flavio Fontana, Christian Forster, Davide Scaramuzza. 2015
111 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Davide Scaramuzza
- Vladlen Koltun
- Antonio Loquercio
- Guoquan Huang
- René Ranftl
- Jeffrey A. Delmerico
- Davide Falanga
- Alexey Dosovitskiy
In Computer Vision and Pattern Recognition
- Davide Scaramuzza
- Vladlen Koltun
- Luc Van Gool
- René Ranftl
- Jitendra Malik
- Ming-Hsuan Yang
- Dacheng Tao
- Andrea Vedaldi
Citations in the corpus
- Deep Drone Racing: Learning Agile Flight in Dynamic Environments (2018, methodology): “Ground truth body rates for the baseline were provided by generating a minimum snap reference trajectory through all gates and then tracking it with a low-level controller [5].”
- Differential Flatness of Quadrotor Dynamics Subject to Rotor Drag for Accurate Tracking of High-Speed Trajectories (2017, methodology): “as presented in [16]) and ωref are feed-forward terms from the reference trajectory, which are computed as described in Section IV.”
- Vision-based autonomous quadrotor landing on a moving platform (2017, methodology): “We refer the reader to our previous works [27] and [28] for further details on the dynamical model and the control algorithm used in this work.”
- Autonomous aerial navigation using monocular visual‐inertial fusion (2018, unknown): “φτ (x) = { φ(x) if |φ(x)| ≤ τ undefined otherwise, (31)”
- Aggressive quadrotor flight through narrow gaps with onboard sensing and computing using active vision (2016, methodology): “We solve this problem using the automatic recovery system detailed in [12], where the authors provide a method to let a quadrotor stabilize automatically after an aggressive maneuver, e.”
- Visual-Inertial Navigation: A Concise Review (2019, methodology): “[127] developed a re-initialization and failure recovery algorithm based on SVO [113] within a loosely-coupled estimation framework, while an additional downward-facing distance sensor is required to recover the metric scale.”