PIXHAWK: A system for autonomous flight using onboard computer vision

By Lorenz Meier, Petri Tanskanen, Friedrich Fraundorfer, Marc Pollefeys. IEEE International Conference on Robotics and Automation, 2011

431 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/876ef047c98a640b4153cf6f2314751aa2334d2c

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Image Retrieval for Image-Based Localization Revisited (2012, background): “With the widespread adoption of mobile camera phones, image-based localization has recently received increased attention as it enables many interesting applications such as mobile landmark recognition [4], real-time camera pose tracking [13, 21, 37], robot navigation [19], or community-based city re”
PIXHAWK: A micro aerial vehicle design for autonomous flight using onboard computer vision (2012, background): “1.1 State-of-the-art This paper extends our previous work described in Meier et al. (2011).”
Direct visual-inertial odometry with stereo cameras (2016, background): “In early works, visual-inertial fusion has been approached as a pure sensor-fusion problem: Vision is treated as an independent, black-box 6-DoF sensor which is fused with inertial measurements in a filtering framework [30] [23] [6].”
Build Your Own Quadrotor: Open-Source Projects on Unmanned Aerial Vehicles (2012, methodology): “Pixhawk [12] uses onboard computer-vision algorithms developed by ETHZ Computer Vision Group [Figure 1(e)].”
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles (2017, methodology): “The typical setup for an autonomous aerial vehicle includes the ﬂight controller ﬁrmware such as PX4 [16], ROSFlight [10], Hackﬂight[15] etc.”
Visual Odometry and Mapping for Autonomous Flight Using an RGB-D Camera (2011, methodology): “The flight computer, developed by the Pixhawk project at ETH Zurich [24], is a 1.”
Quadrotors and Accelerometers: State Estimation with an Improved Dynamic Model (2014, background): “In several publications, for example, [11], [18], and [19], a subtle mistake is made when relating gravity to the accelerometer measurements on a quadrotor.”
Vision-based state estimation for autonomous rotorcraft MAVs in complex environments (2013, methodology): “86 GHz processor with 2GB of RAM installed on a baseboard developed by the PIXHAWK team [23] with a 120GB SSD hard drive.”
A Fully-Autonomous Aerial Robot for Search and Rescue Applications in Indoor Environments using Learning-Based Techniques (2018, methodology): “The avionics of the UAV are managed by a Pixhawk [34] autopilot, which integrates an Inertial Measurement Unit (IMU), a barometer and a magnetometer.”
Vision-based autonomous mapping and exploration using a quadrotor MAV (2012, background): “Many researchers demonstrated some of these abilities by using laser range ﬁnders [2], [8], [22], artiﬁcial marker aided vision [9], [19] and/or a Vicon motion capture system [15].”
Autonomous UAVs for Structural Health Monitoring Using Deep Learning and an Ultrasonic Beacon System with Geo‐Tagging (2018, methodology): “The Pixhawk 2.1 was selected as a ﬂight controller due to its improved inertial measurement unit (IMU) (Meier et al., 2011).”
Opportunities and challenges with autonomous micro aerial vehicles (2012, background): “Similar progress is reported by researchers in the vision community leveraging recent advances in onboard processing capabilities and specialized hardware (Meier et al., 2011).”
Optimized flocking of autonomous drones in confined environments (2018, background): “, with constant acceleration at high speeds and exponential approach in time at low speeds ( 51 ): where r is the distance between an agent and an expected stopping point, a is the preferred acceleration, and p is a linear gain also determining the crossover point between the two phases of…”