Global turmoil, terrorism and hybrid war has made cost-effective and energy-efficient surveillance of the critical infrastructure extremely important for public safety and critical functions. PROACTIF tackles the challenge by developing unmanned...
Efficient environment perception and reconstruction: Simplifying and compressing point clouds without the loss of meaningful information
Department in charge
In today's autonomous world, the transmission and interpretation of large amounts of information describing the environment is a key issue. The research aims to make self-driving cars, robots, and drones work more efficiently. This requires more...
The project aims to address the problem of the limitations of current Light Detection and Ranging (LIDAR) and camera technologies for autonomous driving. Traditional mechanical LIDAR systems are bulky, expensive, and require frequent maintenance, the...
Some premature infants spend weeks in incubators. During this time, their nervous system continues to develop. It is medically proven that prolonged periods of pain during this phase impact negatively the development of their white matter. However...
AI based Fusion of Satellite/Airborne data for Biodiversity Change Characterization
Department in charge
Project objectives: With the support of the European Space Agency, HUN-REN SZTAKI and the Lechner Knowledge Centre (LTK) are collaborating to develop a new methodology for mapping and characterizing geographical areas based on the presence and...
B-prepared builds on a freely accessible massive collaborative knowledge base and data hub, demonstrating its usefulness via three demonstrator applications: a cooperative multiplayer VR serious game, simulating real disaster scenarios for the safest...
With technology rapidly enhancing and becoming more affordable, commercial and consumer drones have become increasingly popular since the beginning of the 21st century. They pose serious threats to safety (e.g. disturbances for airports and airplanes...
The project will further develop and optimise the road defect detection algorithm developed within ARNL. The algorithm uses a self-learning neural network, LiDAR and camera fusion to determine road surface deviations in front of vehicles that are...
The Multinational Capability Development Campaign (MCDC) Artificial Intelligence Supported by Sensor Fusion project was jointly organised by HUN-REN SZTAKI (Hungarian Research Network Computer and Automation Research Institute), the MH Military...
Scene analysis and reconstruction from incomplete spatial data
Department in charge
Due to the rapid progress of sensor technology, processing a large amount of incomplete 3D spatial data is becoming a critical issue. In environment perception tasks (e.g., remote surveillance, navigation of autonomous vehicles, medical diagnostics)...
Development of an AI-supported, camera-based product family for monitoring and predicting daily activities of premature infants
Department in charge
Currently used methods rely exclusively on objective diagnostic tools for monitoring the condition of infants. However, professionals increasingly recognize the importance of individual quality of life for patients, although suitable tools for...
Integrated measurement device demonstrator for phototropic industrial microalgae reactors
Department in charge
The goal of the project is to develop a series of instruments that autonomously measures the physiological data of phototropic industrial microalgae and environmental parameters essential for automating algae cultivation. These instruments will...
