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Autonomous drone racing is a challenging research problem at the intersection of computer vision, planning, state estimation, and control.
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Multiple View Geometry in Computer Vision
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Design and use paradigms for gazebo, an open-source multi-robot simulator
Nathan Koenig and Andrew Howard · 2004
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2009-present
IMAV Challenge · 2009
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Efficient sparse voxel octrees
Samuli Laine and Tero Karras · 2010
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Minimum snap trajectory generation and control for quadrotors
Daniel Mellinger and Vijay Kumar · 2011
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Real-time visual-inertial mapping, re-localization and planning onboard mavs in unknown environments
Michael Burri, Helen Oleynikova, , Markus W. Achtelik, and Roland Siegwart · 2015
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Flownet: Learning optical flow with convolutional networks
Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg, Philip Häusser, Caner Hazırbaş, Vladimir Golkov, Patrick Van der Smagt, Daniel Cremers, and Thomas Brox · 2015
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Rotors—a modular gazebo mav simulator framework
Fadri Furrer, Michael Burri, Markus Achtelik, and Roland Siegwart · 2016
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Ssd: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg · 2016
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2016-present
MBZIRC Challenge · 2016
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Continuous-time trajectory optimization for online uav replanning
Helen Oleynikova, Michael Burri, Zachary Taylor, Juan Nieto, Roland Siegwart, and Enric Galceran · 2016
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Polynomial trajectory planning for aggressive quadrotor flight in dense indoor environments
Charles Richter, Adam Bry, and Nicholas Roy · 2016
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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Control of a quadrotor with reinforcement learning
Jemin Hwangbo, Inkyu Sa, Roland Siegwart, and Marco Hutter · 2017
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Uncertainty-aware reinforcement learning for collision avoidance
Gregory Kahn, Adam Villaflor, Vitchyr Pong, Pieter Abbeel, and Sergey Levine · 2017
ESIM: an open event camera simulator
Henri Rebecq, Daniel Gehrig, and Davide Scaramuzza · 2018
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Airsim: High-fidelity visual and physical simulation for autonomous vehicles
Shital Shah, Debadeepta Dey, Chris Lovett, and Ashish Kapoor · 2018
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A real-time game theoretic planner for autonomous two-player drone racing
Riccardo Spica, Davide Falanga, Eric Cristofalo, Eduardo Montijano, Davide Scaramuzza, and Mac Schwager · 2018
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Are we ready for autonomous drone racing? the uzhfpv drone racing dataset
Jeffrey Delmerico, Titus Cieslewski, Henri Rebecq, Matthias Faessler, and Davide Scaramuzza · 2019
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Guillermo Gallego, Tobi Delbruck, Garrick Orchard, Chiara Bartolozzi, Brian Taba, Andrea Censi, Stefan Leutenegger, Andrew Davison, Joerg Conradt, Kostas Daniilidis, et al · 2019
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Linear vs nonlinear mpc for trajectory tracking applied to rotary wing micro aerial vehicles
Mina Kamel, Michael Burri, and Roland Siegwart · 2017
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Voxblox: Incremental 3d euclidean signed distance fields for on-board mav planning
Helen Oleynikova, Zachary Taylor, Marius Fehr, Roland Siegwart, and Juan Nieto · 2017
Cited alongside, same era.
Minimum-time trajectory generation for quadrotors in constrained environments
Sara Spedicato and Giuseppe Notarstefano · 2017
Cited alongside, same era.
The blackbird dataset: A large-scale dataset for uav perception in aggressive flight
Amado Antonini, Winter Guerra, Varun Murali, Thomas Sayre-McCord, and Sertac Karaman · 2018
Cited alongside, same era.
Perception, guidance, and navigation for indoor autonomous drone racing using deep learning
Sunggoo Jung, Sunyou Hwang, Heemin Shin, and David Hyunchul Shim · 2018
Cited alongside, same era.
Deep drone racing: Learning agile flight in dynamic environments
Elia Kaufmann, Antonio Loquercio, Rene Ranftl, Alexey Dosovitskiy, Vladlen Koltun, and Davide Scaramuzza · 2018
Cited alongside, same era.
Proflow: Learning to predict optical flow
Daniel Maurer and Andrés Bruhn · 2018
Cited alongside, same era.
Winter Guerra, Ezra Tal, Varun Murali, Gilhyun Ryou, and Sertac Karaman · 2019
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Fiesta: Fast incremental euclidean distance fields for online motion planning of aerial robots
Luxin Han, Fei Gao, Boyu Zhou, and Shaojie Shen · 2019
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Beauty and the beast: Optimal methods meet learning for drone racing
Elia Kaufmann, Mathias Gehrig, Philipp Foehn, René Ranftl, Alexey Dosovitskiy, Vladlen Koltun, and Davide Scaramuzza · 2019
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Flying through a narrow gap using neural network: an end-to-end planning and control approach
Jiarong Lin, Luqi Wang, Fei Gao, Shaojie Shen, and Fu Zhang · 2019
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Deep drone racing: From simulation to reality with domain randomization
Antonio Loquercio, Elia Kaufmann, René Ranftl, Alexey Dosovitskiy, Vladlen Koltun, and Davide Scaramuzza · 2019
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Challenges and implemented technologies used in autonomous drone racing
Hyungpil Moon, Jose Martinez-Carranza, Titus Cieslewski, Matthias Faessler, Davide Falanga, Alessandro Simovic, Davide Scaramuzza, Shuo Li, Michael Ozo, Christophe De Wagter, et al · 2019
Later among the works it cites.
Reward-driven u-net training for obstacle avoidance drone
Sang-Yun Shin, Yong-Won Kang, and Yong-Guk Kim · 2020
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