Towards an efficient and robust optic flow algorithm for robotic applications

Optic flow has proven itself to be a powerful sensor modality for a wide range of robotic applications. The most popular optic flow algorithms, however, still tend to be based on classical algorithms developed in the era of analog video systems. In this work we reconsider the foundations of optic flow computation for robotic applications from a modern statistical and computer hardware point of view. We assume that high speed digital camera systems and inertial image compensation are used to reduce the problem to computing sub pixel flow and consider algorithms with the potential to be implemented on FPGA computing hardware. In this paper we propose a novel flow algorithm, based on a combined least-squares and coupled generalized-total-least-squares optimization procedure that yields high quality flow estimates for acceptable computational load.