A quantitative analysis of system bottlenecks in visual SLAM
A quantitative analysis of system bottlenecks in visual SLAM
复制标题
DOI：
10.1145/3508396.3512882
复制
发表时间：
2022-03
期刊：
影响因子：
--
通讯作者：
S. Semenova;Steven Y. Ko;Yu David Liu;Lukasz Ziarek;Karthik Dantu
中科院分区：
文献类型：
--
作者：
S. Semenova;Steven Y. Ko;Yu David Liu;Lukasz Ziarek;Karthik Dantu
Visual SLAM系统是并发的、性能关键的系统，其响应于实时环境条件，并且经常部署在资源受限的硬件上。以前的SLAM框架主要关注算法的进步，其系统核心在很大程度上保持不变。反过来，SLAM系统遭受性能问题，这些问题可以通过改进的系统设计来缓解。在本文中，我们提出了一个定量分析的系统面临的挑战，建立一致的，准确的，强大的SLAM系统的并发性，可变的环境条件和资源受限的硬件。我们确定了系统设计中三个相互关联的挑战-及时性，并发性和上下文感知-并阐明了它们对性能的影响。
Visual SLAM systems are concurrent, performance-critical systems that respond to real-time environmental conditions and are frequently deployed on resource-constrained hardware. Previous SLAM frameworks have primarily focused on algorithmic advances and their systems core has largely remained unchanged. In turn, SLAM systems suffer from performance problems that could be alleviated with improved systems design. In this paper, we present a quantitative analysis of the systems challenges to building consistent, accurate, and robust SLAM systems in the face of concurrency, variable environmental conditions, and resource-constrained hardware. We identify three interconnected challenges on systems design --- timeliness, concurrency, and context awareness --- and clarify their effects on performance.