We are a spinout from the University of Sheffield, building on expertise and results won from the Green Brain and Brains on Board research projects. By going beyond deep learning, our products require no training, can adapt on the fly, and are more amenable to verification. We are currently planning to begin our funding round shortly.
Our technology is developed for some of the most challenging use cases, and tested through embodiment in robots operating in environments with zero concessions to AI. We target UAVs since these impose stringent weight, power and compute restrictions, but our products are applicable in diverse areas, such as frameless camera imaging.
Our state-of-the-art optic flow estimator is more robust than the leading Deep Learning-based approach, amenable to verification, and orders of magnitude faster and more efficient. Suitable for collision avoidance , speed and distance estimation, Flow also underpins our more advanced technology.
Building on Flow, our visual compass solution allows autonomous vehicles to track heading using only camera data, suitable for environments in which GPS and magnetic compass cannot be used, such as indoors.
Bees are excellent navigators; building on Flow and Compass our approach to SLAM gives autonomous vehicles the same level of robust navigation without the traditional computational overhead while reducing edge case failures.