Optical Flow Revisited: how good is dense deep learning based optical flow?
Jeong Min Kang, Zoran Sjanic, Gustaf Hendeby · 2023
Accurate localization is a part of most autonomous systems. GNSS is today the go to solution for localization but is unreliable due to jamming and is not available indoors. Inertial navigation aided by visual measurements, e.g., optical flow, offers an alternative. Traditional feature-based optical flow is limited to scenes with good features, current development of deep neural network derived dense optical flow is an interesting alternative. This paper proposes a method to evaluate the result of dense optical flow on real image sequences using traditional feature-based optical flow and uses this to compare six different dense optical flow methods. The results of the dense methods are promising, and no clear winner amongst the methods can be determined. The results are discussed in the context of how they can be used to support localization.