Efficient Optical Flow and Stereo Vision for Velocity Estimation and Obstacle Avoidance on an Autonomous Pocket Drone
By Kimberly N. McGuire, G. D. de Croon, C. de Wagter, Karl Tuyls, Hilbert J. Kappen. 2016
188 citations recorded by the source corpus.
Micro Aerial Vehicles (FOV) are very suitable for flying in indoor environments, but autonomous navigation is challenging due to their strict hardware limitations. This paper presents a highly efficient computer vision algorithm called Edge-FS for the determination of velocity and depth. It runs at 20 Hz on a 4 g stereo camera with an embedded STM32F4 microprocessor (168 MHz, 192 kB) and uses edge distributions to calculate optical flow and stereo disparity. The stereo-based distance estimates are used to scale the optical flow in order to retrieve the drone's velocity. The velocity and depth measurements are used for fully autonomous flight of a 40 g pocket drone only relying on on-board sensors. This method allows the MAV to control its velocity and avoid obstacles.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Antonio Loquercio
- Luca Benini
- Daniele Palossi
- Davide Scaramuzza
- Henri Rebecq
- Vladlen Koltun
- Daniel Cremers
- Guoquan Huang
In Computer Vision and Pattern Recognition
- Antonio Loquercio
- Davide Scaramuzza
- Francesco Conti
- Luca Benini
- Daniele Palossi
- Eric Flamand
- Vladlen Koltun
- Daniel Cremers
Citations in the corpus
- A 64-mW DNN-Based Visual Navigation Engine for Autonomous Nano-Drones (2018, methodology): “COTS nano-size quadrotors, like the Bitcraze Crazyflie 2.0 or the Walkera QR LadyBug , embed on board low-power single core MCUs, like the ST Microelectronics STM32F4 [13], [21], [24].”