Related papers: PCA Event-Based Otical Flow for Visual Odometry

PCA Event-Based Otical Flow for Visual Odometry

URL: http://arxiv.org/abs/2105.03760v1
Date: Sat, 8 May 2021 18:30:44 GMT
Title: PCA Event-Based Otical Flow for Visual Odometry
Authors: Mahmoud Z. Khairallah, Fabien Bonardi, David Roussel and Samia Bouchafa
Abstract summary: We present a Principal Component Analysis approach to the problem of event-based optical flow estimation. 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.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
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.

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