The Navigation and Control technology inside the AR.Drone micro UAV

By P. Bristeau, François Callou, D. Vissière, Nicolas Petit. 2011

471 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/58fe2fa01db848f3a89dcb697a1b69ec8fa31ab0

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Comprehensive Simulation of Quadrotor UAVs Using ROS and Gazebo (2012, background): “The incorporation of blade flapping effects which can be used to aid state estimation [4] is subject of future work.”
Multirotor Aerial Vehicles: Modeling, Estimation, and Control of Quadrotor (2012, background): “Drone game device makes strong use of vision for attitude and odometry [20].”
Nonlinear control of VTOL UAVs incorporating flapping dynamics (2013, background): “Only recently have these aerodynamic drag forces been taken into account for the modeling of small-scale quadrotors [13], [10], [1], [9].”
Optic-Flow Based Control of a 46g Quadrotor (2013, methodology): “For example in [24] or [25], an ultrasonic distance sensor is used together with a camera pointing downwards, which results in a very good velocity estimation and thus low position drift.”
Aerodynamics and Control of Quadrotors (2017, background): “It can be flown by novice pilots with ease and can take images and transmit them to an iOS phone using a proprietary phone application Bristeau et al. [2011]. The major limitation of these commercial quadrotors is that they do not provide the flexibility to be reprogrammed to suit different needs as”
Advanced closed loop visual navigation for micro aerial vehicles (2014, methodology): “This has been done by Bristeau et al. (2011) with the help of a ultrasonic range sensor - however, we seek for a solution purely using IMU measurements.”
Real-Time, Cloud-Based Object Detection for Unmanned Aerial Vehicles (2017, background): “The horizontal velocity is measured based on two complementary computer vision features, one based on optical flow and the other based on tracking image features (like corners), with the quality of the speed estimates highly dependent on the texture in the input video streams [29].”
Robust state estimation for Micro Aerial Vehicles based on system dynamics (2015, methodology): “Such a velocity estimator is also used successfully in the ARDrone [11].”
Local histogram matching for efficient optical flow computation applied to velocity estimation on pocket drones (2016, background): “This can be done by measuring the optical flow detected with a bottom mounted camera [1].”