|Research Laboratory for Computer Graphics & Virtual/Augmented Reality (LIRV)||[ Français ]|
An IoT Platform for Disaster Response
( #réalité augmentée, #réalité virtuelle, #interfaces )
With the use of artificial intelligence and autonomous system, it is now possible to remotely control drones to carry out reconnaissance operations and supervise critical missions (firefighters, rescue, etc.) without endangering humans. The management of this system using a 2D screen limits the display possibilities and can cause confusion in human understanding during the course of a mission. The work consists of designating an innovative solution with current augmented reality technologies.
Interface well adapted to augmented reality
An interface that adapts according to the criticality of the situation, easy to learn and use, since the stress of a person is omnipresent in the course of an emergency mission. The autonomous system includes a set of drones which work in a coherent way (managed by artificial intelligence). The goal of this research work is to design an adapted interface in an emerging technology and to determine its effectiveness on human decision-making. A set of first responder subjects will test the interface prototype while being monitored for attention and stress. We will set up the experiments and collect data, which will be subsequently analyzed to estimate the overall performance of the interface.
Specific objectives :
"An AR interface to assist human agents during critical missions"
3D Reconstruction and object detection in outdoor environment
The ubiquitous use of Unmanned Aerial Vehicle (UAV), also known as drones, triggered serious risks to population or safety-sensitive infrastructures like airports. To address this concern, robust drone detection systems are required. The goal of our work is to provide the position when a drone appearing on a forbidden zone and the distance from the follower drone.
Due to the various advantages of drones (small size, low price, etc.), the number of the drones expands constantly, making them more common and more accessible. Drones are being used for wide applications like video surveillance, robotics, military, and commercial purposes. However, this pervasive use of drones bring serious risks that need to be addressed.
In this work, we envision an autonomous drone detection system to maintain surveillance and public security. This is one of the first to use computer vision in order to detect and to catch an unauthorized drone. We aim at developing a fully detection system of unauthorized drones using a deep learning based approach. The quantitative experimental results demonstrate that the performance of the detector trained on real or virtual worlds are quit similar.
Highly Qualified Personnel Training (HQP) :