We began as a research project almost a decade ago at Ben-Gurion University’s Electrical & Computer Engineering Department, exploring the area of real-time registration. In the course of the research, we realized the incredible potential it had for helping to make autonomous flight a reality. Our fundamental challenge is this: how to analyze a drone’s ever-changing surroundings accurately and in real-time while using a minimum of hardware, computer power and network bandwidth.

Our patented innovation was to use real-time registration to give flying vehicles the ability to see where they are and understand their surroundings, by comparing what they see in real time to a reference frame or a map to successfully navigate to their destination and analyze a given scene with the highest accuracy possible.

Today, we’re working with a number of drone platforms and other Unmanned Aircraft Systems (UAS) to extend where they  can go and what they are capable of. The key to it all is something called situational awareness: our technologies enable drones and other flying vehicles to understand where they are based on what they see, at a range of altitudes and without GPS assistance. Achieving this is a significant challenge, and the underlying technology it requires is correspondingly impressive. We are constantly working on improving image- and point cloud-matching algorithms, to make them more powerful and compact. But we’re also exploring how other AI technologies can improve air mobility, too. Like how we can use big data to make drones fly with greater precision, and how to use deep learning to teach drones how to understand their surroundings more precisely, all in real time.