Comparison between feature-based and phase correlation methods for ROV vision-based speed estimation

The performance of different visual approaches for estimating the motion of an underwater Remotely Operated Vehicle (ROV) is discussed. The paper compares three different techniques: feature correlation, Speeded Up Robust Features (SURF), both based on feature extraction and matching, and phase correlation, which instead does not rely on image features. The three algorithms accuracy and performance are compared using a batch of data collected in typical operating conditions with the Romeo ROV. In estimating vehicle speed, phase correlation outperformed SURF in terms of robustness and precision, giving similar results to those obtained with feature correlation. In terms of computational time, phase correlation outperformed both feature-based methods.