An open source and open hardware embedded metric optical flow CMOS camera for indoor and outdoor applications

By Dominik Honegger, Lorenz Meier, Petri Tanskanen, Marc Pollefeys. IEEE International Conference on Robotics and Automation, 2013

334 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/579e2e12c1e46e8636206eb5028ecf04bf5c205c

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Citations in the corpus

Toward low-flying autonomous MAV trail navigation using deep neural networks for environmental awareness (2017, methodology): “Developed for the PX4 / Pixhawk project by ETH [15], PX4FLOW provides reliable metric position and attitude estimation by computing 2D optical ﬂow on the ground, then using ground distance measurements and gyro readings to compute vehicle ground speed.”
Efficient Optical Flow and Stereo Vision for Velocity Estimation and Obstacle Avoidance on an Autonomous Pocket Drone (2016, background): “These types of techniques have proven themselves on numerous occasions [11], nonetheless do require a platform with a decent amount of computing power.”
PX4: A node-based multithreaded open source robotics framework for deeply embedded platforms (2015, background): “A wide variety of common peripherals is supported by the system, including MEMS sensors, external airspeed and pressure sensors, PWM, I2C and CAN motor controllers as well as PX4 specific peripherals, such as the flow sensor [20].”
Autonomous UAVs for Structural Health Monitoring Using Deep Learning and an Ultrasonic Beacon System with Geo‐Tagging (2018, methodology): “To overcome these problems, a distance sensor and optical ﬂow (Honegger et al., 2013) can be used with the simultaneous localization and mapping (SLAM) technique.”
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.”