Underwater Visual 3D SLAM Using a Bio-inspired System

Considering the various challenges in robotics, one of the most important concerns to a mobile robot build a map and estimate its location in an environment. The SLAM techniques build a map and simultaneously maintain the current location of a robot in the same time. Nowadays, probabilistic approaches dominate the field, anyway the last decade have witnessed important bio-inspired studies based on biological structures related to spatial navigation. Past and recent studies found place, head direction and grid cells, among others, related to the tasks of mapping and location in mammals. Continuous Attractor Neural Networks (CANN) are being proposed to simulate the SLAM performed by this brain structures. This work present a bio-inspired SLAM system to map a sub-aquatic 3D environment, through a simulation of an underwater robot equipped with a camera, using visual references to mimic the behavior of location tasks performed by mammals.