PCA Event-Based Optical Flow for Visual Odometry

TL;DR

This paper introduces a PCA-based method for event-based optical flow estimation tailored for real-time visual odometry, achieving twice the speed and better accuracy than existing techniques.

Contribution

It presents a novel PCA approach with regularization techniques for efficient, accurate, real-time optical flow estimation from neuromorphic event-based sensors.

Findings

Method is twice as fast as state-of-the-art

Significantly improves optical flow accuracy

Effective for real-time visual odometry

Abstract

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.