Abstract
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.
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