Modular AI Software Suite for Highly Reliable Perception & Autonomy in UAVs, Drones, & Self-Driving Vehicles
VISIONAIRY for Automated Flying Modular AI-powered perception and autonomy software for UAVs & drones
VISIONAIRY for Automated Flying

Modular AI-powered perception and autonomy software for UAVs & drones

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VISIONAIRY for Automated Flying

VISIONAIRY is a modular software suite that utilizes a unique blend of large-scale data training and sophisticated sensor fusion to provide highly reliable perception and autonomy for UAVs and drones.

Compatible with all leading drone flight controllers and robotic operating systems, including PX4, Auterion and ROS, the powerful software includes a number of modules that provide advanced capabilities for unmanned aircraft platforms, including:

GPS-Denied – provides vision-based centimeter-accurate autonomous navigation even in the absence of GPS and GNSS signals, allowing for operations in challenging environments such as underground and in urban canyons

Safe Precision Landing – fully-automated precision landing capabilities, detecting all obstacles and moving objects within the landing zone and supporting all UAS cameras and LiDAR scanners

Surveillance – automated real-time detection, identification and tracking of multiple objects and object types under highly challenging conditions

Collision Avoidance – reliable object detection at ranges of up to 100 metres, essential for advanced drone operations such as BVLOS, drone delivery, and wide-area mapping and surveying

Air Risk Estimation – advanced detect-and-avoid capabilities that work even with aircraft that are non-cooperative with ADS-B and FLARM

Industrial Inspection – fully autonomous inspection of industrial assets and facilities such as power lines, wind turbines, roads and railways, and pipelines

Advancing Unmanned Systems Through Strategic Collaboration UST works with major OEMs to foster collaboration and increase engagement with SMEs, to accelerate innovation and drive unmanned systems capabilities forward.