Matouš Vrba presents Marker-Less Micro Aerial Vehicle Detection and Localization Using Convolutional Neural Networks

On 2020-11-19 11:00:00 at The seminar will be held online
Our recent advancements on marker-less relative localization of micro aerial
vehicles (MAVs) will be presented in the talk. Specifically, a description of a
novel relative localization system system which utilizes images from an onboard
camera to detect nearby MAVs using a convolutional neural network will be
provided. When compared to traditional computer vision-based relative
localization systems, this approach removes the need for specialized markers to
be placed on the MAVs, saving weight and space, while also enabling
of non-cooperating robots. The system is designed and implemented to run
onboard an MAV platform in order to enable relative stabilization of several
MAVs in a formation or swarm-like behavior, when operating in a closed feedback
loop with the control system of the MAVs. Viability and robustness of the
proposed method is demonstrated in real-world experiments. The method was also
designed for the purpose of autonomous aerial interception and is a fitting
complement to other MAV detection and relative localization methods for this
purpose, as is shown in the experiments.

This seminar is a summary of our publication;
M. Vrba and M. Saska, "Marker-Less Micro Aerial Vehicle Detection and
Localization Using Convolutional Neural Networks," in IEEE Robotics and
Automation Letters, vol. 5, no. 2, pp. 2459-2466, April 2020.

The seminar will be held online via zoom:
Responsible person: Petr Pošík