3D Large-Scale Seabed Reconstruction for UUV Simultaneous Localization and Mapping

This paper proposes a new technique to reconstruct large 3D scenes from a sequence of video images by combining Bayesian filtering and state-of-art 3D computer vision. The approach performs the alignment of a sequence of 3D partial reconstructions of the seafloor thanks to the re-observations of passive landmarks by means of a linear Kalman filter-based SLAM approach. Landmarks are detected on the images and characterized considering 2D and 3D features. Landmarks are re-observed while the robot is navigating and data association becomes easier but robust. Preliminary results are performed in virtual scenarios but processing real images synthetized from underwater textures.