The robots using visual simultaneous localization and mapping (SLAM) system are generally experiencing excessive power consumption and suffer from depletion of battery energy during the course of working. The intensive computation necessary to complete complicated tasks is overwhelming for inexpensive mobile robots with limited on-board resources. To address this problem, a novel task offloading strategy combined with a new dense point cloud map construction method is proposed in this paper, which is firstly used for the improvement of the system especially in indoor scenes. 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.

使用视觉同时定位和制图（SLAM）系统的机器人通常会消耗过多的功率，并且在工作过程中会消耗电池能量。对于车载资源有限的廉价移动机器人而言，完成复杂任务所需的密集计算不堪重负。针对这一问题，提出了一种结合新的密集点云图构建方法的任务分担策略，该算法首先用于系统的改进，特别是在室内场景中。首先，我们开发了一种新颖的策略，可将计算密集型任务远程卸载到云中心，从而使原本无法在资源受限的机器人系统上本地完成的任务成为可能。其次，一种改进的迭代最近点算法（ICP），为了加速点云注册，开发了一种称为适应度得分分层ICP算法（FS-HICP）。理论分析和实验仿真都对所提出策略的正确性，效率和可扩展性进行了评估。结果表明，该方法可以有效降低能耗，同时提高多机器人视觉SLAM系统的计算能力和速度，特别是在室内环境下。