PIXHAWK: A micro aerial vehicle design for autonomous flight using onboard computer vision

By Lorenz Meier, Petri Tanskanen, Lionel Heng, Gim Hee Lee, Friedrich Fraundorfer, Marc Pollefeys. Autonomous Robots, 2012

416 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/23b560227c1e8f37348f24ff34e42113cbb11719

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Visual-Inertial Navigation for a Camera-Equipped 25g Nano-Quadrotor (2014, background): “On the other hand, there have been significant advances regarding autonomous flight in GPS-denied environments using cameras as main sensors both with on-board [10], [8] and off-board processing [5].”
Build Your Own Quadrotor: Open-Source Projects on Unmanned Aerial Vehicles (2012, background): “Vision-Based Navigation The Pixhawk UAV is designed to be a research platform for computer-vision-based autonomous flight [28].”
A Computationally Efficient Motion Primitive for Quadrocopter Trajectory Generation (2015, methodology): “The algorithm was also implemented on the STM32-F4 microcontroller, which costs approximately €10 and is, for example, used on the open-source PX4 autopilot system [29].”
Vision-based autonomous mapping and exploration using a quadrotor MAV (2012, result): “Details of the implementation of the position controller can be found in one of our previous works [17].”
Automatic re-initialization and failure recovery for aggressive flight with a monocular vision-based quadrotor (2015, methodology): “Hence, when the quadrotor is launched, we can detect a drop in the measured accelerations to a value corresponding to the currently applied thrust.”
Autonomous, Vision‐based Flight and Live Dense 3D Mapping with a Quadrotor Micro Aerial Vehicle (2016, methodology): “Drone by a PX4FMU autopilot and a PX4IOAR adapter board developed in the PIXHAWK Project (Meier et al., 2012).”
PX4: A node-based multithreaded open source robotics framework for deeply embedded platforms (2015, methodology): “It leveraged LCM [1] as middleware and added a set of computer-vision specific interfaces and algorithms [3].”
Neural-Fly enables rapid learning for agile flight in strong winds (2022, methodology): “Commonly-used off-the-shelf controllers, such as PX4, design each of these loops as proportional-integral-derivative (PID) regulation controllers [15].”