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引用次数: 4

摘要

同时定位和地图构建(SLAM)在机器人社区中继续引起相当大的关注,因为它可以在构建自主机器人方面提供优势。然而,很少有解决这个问题的方法可以扩展到处理现实环境中存在的大量地标。实时执行SLAM对处理资源的要求非常高。本文提出了一种利用基础设施即服务(IaaS)提供的资源和并行性进行有效处理的新系统。利用私有云基础设施,利用基于MPI的虚拟化资源,实时完成全局地图构建任务,同时对室内环境进行环检测和束调整。通过在各种具有挑战性的环境中实现移动障碍物,视觉上均匀的区域几乎没有特征,照明变化很大的区域和相对较快的相机运动,我们证明了我们的系统是有效的,也是健壮的。
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Real time visual SLAM using cloud computing
Simultaneous localization and map-building (SLAM) continues to draw considerable attention in the robotics community due to the advantages it can offer in building autonomous robots. However, few approaches to this problem scale up to handle a large number of landmarks present in real environments. The processing resource requirement to carry out SLAM in real time can be quite high. In this paper we present a novel system which employs resources provided by infrastructure as a service (IaaS) and parallelism for effective processing. Using private cloud infrastructure employing virtualized resources based on MPI the task of global map building is performed in real time simultaneously carrying out loop detection and bundle adjustment for indoor environments. Through implementation in various challenging environments with moving obstacles, visually homogeneous areas having few features, regions with large changes in lighting and relatively fast camera motion we demonstrate our system to be one which is effective as well as robust.
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