CORB2I-SLAM: An Adaptive Collaborative Visual-Inertial SLAM for Multiple Robots

The generation of robust global maps of an unknown cluttered environment through a collaborative robotic framework is challenging. We present a collaborative SLAM framework, CORB2I-SLAM, in which each participating robot carries a camera (monocular/stereo/RGB-D) and an inertial sensor to run odometry. A centralized server stores all the maps and executes processor-intensive tasks, e.g., loop closing, map merging, and global optimization. The proposed framework uses well-established Visual-Inertial Odometry (VIO), and can be adapted to use Visual Odometry (VO) when the measurements from inertial sensors are noisy. The proposed system solves certain disadvantages of odometry-based systems such as erroneous pose estimation due to incorrect feature selection or losing track due to abrupt camera motion and provides a more accurate result. We perform feasibility tests on real robot autonomy and extensively validate the accuracy of CORB2I-SLAM on benchmark data sequences. We also evaluate its scalability and applicability in terms of the number of participating robots and network requirements, respectively.

Automated summary

In this study, a collaborative SLAM framework called CORB2I-SLAM is proposed for generating robust global maps of unknown cluttered environments through the collaboration of multiple robots. The framework utilizes well-established Visual-Inertial Odometry (VIO) technology and can adapt to use Visual Odometry (VO) when the measurements from inertial sensors are noisy, resulting in more accurate results. Feasibility tests and extensive validation on benchmark data sequences demonstrate the effectiveness and accuracy of the framework, as well as its scalability and applicability in terms of the number of participating robots and network requirements.