Using Sift Algorithm for Visual-based Localization in Multi-auvs Fleet

The use of teams of Autonomous Underwater Vehicles for visual inspection tasks is a promising robotic field. The images captured by different robots can also be used to aid in the localization/navigation of the fleet. In previous works, we have proposed a distributed localization system based on the Augmented States Kalman Filter through the visual maps obtained by the fleet. In this context, this paper details a system for on-line construction of visual maps and its use to aid the localization and navigation of the robots. Different aspects related to the capture, treatment and construction of mosaics by fleets of robots are presented. The Scale Invariant Feature Transform (SIFT) algorithm is used as a method of extracting and describing keypoints between consecutive images which are robustly invariant to common image transforms. The developed system can be executed on-line on different robotic platforms. The paper is concluded with a series of tests and analysis, in different underwater conditions, for system validation. Keywords— computer vision, robotics, underwater robots, mosaic, SIFT.