Abstract
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.
Abstract (translated)
多机器人视觉同步定位与绘图（SLAM）系统通常由多个配备摄像头和/或其他视觉传感器的移动机器人组成。网络化机器人在未知场景中独立或协同工作，以解决自主定位和映射问题。在多机器人视觉冲击中，最关键的问题之一是通常所需的密集计算，但对于车载资源有限的廉价移动机器人来说，这是压倒性的。针对这一问题，本文提出了一种新的任务卸载策略和密集点云地图构建方法。首先，我们开发了一种新的策略，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有限的机器人系统上本地完成的任务成为可能。其次，提出了一种改进的迭代最近点算法（ICP），称为适应度评分层次化ICP算法（FS-HICP），以加速点云配准。通过理论分析和实验模拟，评价了该策略的正确性、有效性和可扩展性。结果表明，该方法在提高多机器人视觉冲击系统的计算能力和速度的同时，能有效地降低能耗，特别是在室内环境中。
URL
https://arxiv.org/abs/1905.12973
