A survey on real-time motion estimation techniques for underwater robots

Abstract

Over the last few years, we have assisted to an impressive evolution in the state-of-the-art of feature extraction, description and matching. Feature matching-based methods are among the most popular approaches to the problem of motion estimation. Thus, the need of studying the evolution of the feature matching field arises naturally. The application chosen is the motion estimation of a Remotely Operated Vehicle (ROV). A challenging environment such as an underwater environment is an excellent test bed to evaluate the performance of the several recent developed feature extractors and descriptors. The algorithms were tested using the same open source framework to give a fair assessment of their performance especially in terms of computational time. The various possible combinations of algorithms were compared to an approach developed by the authors that showed good performance in the past. A data set collected by the ROV Romeo in typical operations is used to test the methods. Quantitative results in terms of robustness to noise and computational time are presented and demonstrate that the recent trend of binary features is very promising.