A comparison between different feature-based methods for ROV vision-based speed estimation

A comparison study between different state-of-the-art visual approaches for estimating the motion of an underwater Remotely Operated Vehicle (ROV) is performed. The paper compares five different techniques: the template correlation, Speeded Up Robust Features (SURF), Scale Invariant Feature Transform (SIFT), Features from Accelerated Segment Test (FAST) and Center Surround Extrema (CenSurE), all based on feature extraction and matching. All these are implemented on the same free open source library which allows a fair comparison that can establish the best technique (depending on the criteria used). Taking into account previous work where SURF and template correlation techniques were evaluated using a batch of data collected in typical operating conditions with the Romeo ROV, the other techniques are compared using the same data set. In estimating vehicle speed, SURF and SIFT presented noise levels higher but close to template correlation, though SURF and SIFT have more outliers. In terms of computational time, template correlation outperforms all other alternatives by large in some cases.