  In this letter, we present an active visual SLAM approach for omnidirectional
robots. The goal is to generate control commands that allow such a robot to
simultaneously localize itself and map an unknown environment while maximizing
the amount of information gained and consume as little energy as possible.
Leveraging the robot's independent translation and rotation control, we
introduce a multi-layered approach for active V-SLAM. The top layer decides on
informative goal locations and generates highly informative paths to them. The
second and third layers actively re-plan and execute the path, exploiting the
continuously updated map. Moreover, they allow the robot to maximize its
visibility of 3D visual features in the environment. Through rigorous
simulations, real robot experiments and comparisons with the state-of-the-art
methods, we demonstrate that our approach achieves similar coverage and lesser
overall map entropy while keeping the traversed distance up to 36% less than
the other methods. Code and implementation details are provided as open-source.
