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Simulation and evaluation of deep learning autoencoders for image compression in multi-UAV network systems
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Advisor(s)
Abstract(s)
Mobile multi-robot systems are versatile alternatives
for improving single-robot capacities in many applications, such
as logistics, environmental monitoring, search and rescue, photogrammetry,
etc. In this sense, this kind of system must have a
reliable communication network between the vehicles, ensuring
that information exchanged within the nodes has little losses. This
work simulates and evaluates the use of autoencoders for image
compression in a multi-UAV simulation with ROS and Gazebo
for a generic surveillance application. The autoencoder model
was developed with the Keras library, presenting good training
and validation results, with training and validation accuracy
of 70%, and a Peak Signal Noise Ratio (PSNR) of 40dB. The
use of the CPU for the simulated UAVs for processing and
sending compressed images through the network is 25% faster.
The results showed that this compression methodology is a good
choice for improving the system’s performance without losing too
much information.
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Pedagogical Context
Citation
Ramos, Gabryel Silva; Lima, Amaro Azevedo de; Almeida, Luciana F.; Lima, José; Pinto, Milena F. (2023). Simulation and evaluation of deep learning autoencoders for image compression in multi-UAV network systems. In 20th Latin American Robotics Symposium, 15th Brazilian Symposium on Robotics, and 14th Workshop of Robotics in Education (LARS/SBR/WRE). p. 41-46. ISBN 979-8-3503-1538-7
Publisher
IEEE