PCA Event-Based Optical Flow for Visual Odometry
This addresses the need for efficient optical flow in robotics applications using neuromorphic vision sensors, though it is incremental as it builds on existing event-based methods.
The paper tackles event-based optical flow estimation for visual odometry by proposing a PCA approach with regularization methods, resulting in a variant that is about two times faster than state-of-the-art implementations while significantly improving accuracy.
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.