Speeded Up Robust Features for vision-based underwater motion estimation and SLAM: comparison with correlation-based techniques

Abstract

Abstract This paper focus on evaluating the possibility of using Speeded Up Feature Transform (SURF) features (Bay et al. (2006)) as optical features to be tracked in different applications, such as the speed estimation or the SLAM (or the more general dead reckoning) of an underwater Remotely Operated Vehicle (ROV). In particular, SURF-based system performance has been compared to results obtained with a laser-triangulation optical-correlation system using a batch of data collected in typical operating conditions with the Romeo ROV. Although conventional techniques based on automatic extraction of high local variance templates and correlation-based tracking demonstrated their effectiveness in speed estimation, the combination with SURF-based techniques dramatically increases the reliability of the data association process for SLAM and mosaicing applications.