iRotate: Active Visual SLAM for Omnidirectional Robots

The work presented in this video was developed at the Max Planck Institute for Intelligent Systems and accepted for publication in Elsevier's Robotics and Autonomous Systems Journal.


Authors: Elia Bonetto, Pascal Goldschmid, Michael Pabst, Michael J. Black and Aamir Ahmad


Abstract: In this work, we present an active visual SLAM approach for omnidirectional robots. The goal is to generate control commands that allows such a robot to simultaneously localize itself and map an unknown environment, while maximizing the amount of information gained and consume as less 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 it. 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.