  With the advent of neuromorphic vision sensors such as event-based cameras, a
paradigm shift is required for most computer vision algorithms. Among these
algorithms, optical flow estimation is a prime candidate for this process
considering that it is linked to a neuromorphic vision approach. Usage of
optical flow is widespread in robotics applications due to its richness and
accuracy. We present a Principal Component Analysis (PCA) approach to the
problem of event-based optical flow estimation. In this approach, we examine
different regularization methods which efficiently enhance the estimation of
the optical flow. We show that the best variant of our proposed method,
dedicated to the real-time context of visual odometry, is about two times
faster compared to state-of-the-art implementations while significantly
improves optical flow accuracy.
