  Course estimation is a key component for the development of autonomous
navigation systems for robots. While state-of-the-art methods widely use
visual-based algorithms, it is worth noting that they all fail to deal with the
complexity of the real world by being computationally greedy and sometimes too
slow. They often require obstacles to be highly textured to improve the overall
performance, particularly when the obstacle is located within the focus of
expansion (FOE) where the optic flow (OF) is almost null. This study proposes
the FAst ITerative Half-plane (FAITH) method to determine the course of a micro
air vehicle (MAV). This is achieved by means of an event-based camera, along
with a fast RANSAC-based algorithm that uses event-based OF to determine the
FOE. The performance is validated by means of a benchmark on a simulated
environment and then tested on a dataset collected for indoor obstacle
avoidance. Our results show that the computational efficiency of our solution
outperforms state-of-the-art methods while keeping a high level of accuracy.
This has been further demonstrated onboard an MAV equipped with an event-based
camera, showing that our event-based FOE estimation can be achieved online
onboard tiny drones, thus opening the path towards fully neuromorphic solutions
for autonomous obstacle avoidance and navigation onboard MAVs.
