NLMAP - visual-based self localization and mapping for Autonomous Underwater Vehicles

Abstract

The use of autonomous underwater vehicles (AUVs) for visual inspection tasks is a promising robotic field. The images captured by the robots can also aid in their localization/navigation. In this context, this paper proposes an approach to localization and mapping problem of underwater vehicle. Supposing the use of inspection cameras, this proposal is composed of two stages: i) the use of computer vision through the algorithm SIFT to extract the features in underwater image sequences and; ii) the development of topological maps to localization and navigation. The integration of such systems will permit simultaneous localization and mapping of the environment. A set of tests with real robots was accomplished, regarding online and performance issues. The results reveals an accuracy and robust approach to several bottom conditions, illumination and noise, leading to a promissory and original SLAM technique.