  Multi-robot visual simultaneous localization and mapping (SLAM) system is
normally consisted of multiple mobile robots equipped with camera and/or other
visual sensors. The networked robots work independently or cooperatively in an
unknown scene in order to solve autonomous localization and mapping problem.
One of the most critical issues in Multi-robot visual SLAM is the intensive
computation that is normally required yet overwhelming for inexpensive mobile
robots with limited on-board resources. To address this problem, a novel task
offloading strategy and dense point cloud map construction method is proposed
in this paper. First, we develop a novel strategy to remotely offload
computation-intensive tasks to cloud center, so that the tasks that could not
originally be achieved locally on the resource-limited robot systems become
possible. Second, a modified iterative closest point algorithm (ICP), named
fitness score hierarchical ICP algorithm (FS-HICP), is developed to accelerate
point cloud registration. The correctness, efficiency, and scalability of the
proposed strategy are evaluated with both theoretical analysis and experimental
simulations. The results show that the proposed method can effectively reduce
the energy consumption while increase the computation capability and speed of
the multi-robot visual SLAM system, especially in indoor environment.
