SUMMARYVisual simultaneous localization and mapping (VSLAM) is a relevant solution for vehicle localization and mapping environments. However, it is computationally expensive because it demands large computational effort, making it a non-real-time solution. The VSLAM systems that employ geometric reconstructions are based on the parallel processing paradigm developed in the Parallel Tracking and Mapping (PTAM) algorithm. This type of system was created for processors that have exactly two cores. The various SLAM methods based on the PTAM were also not designed to scale to all the cores of modern processors nor to function as a distributed system. Therefore, we propose a modification to the pipeline for the execution of well-known VSLAM systems so that they can be scaled to all available processors during execution, thereby increasing their performance in terms of processing time. We explain the principles behind this modification via a study of the threads in the SLAM systems based on PTAM. We validate our results with experiments describing the behavior of the original ORB-SLAM system and the modified version.

中文翻译：

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一种基于几何重构的视觉 SLAM 系统的分布式实现方法

摘要视觉同步定位和映射（VSLAM）是车辆定位和映射环境的相关解决方案。然而，它的计算成本很高，因为它需要大量的计算工作，使其成为非实时解决方案。采用几何重建的 VSLAM 系统基于并行跟踪和映射 (PTAM) 算法中开发的并行处理范例。这种类型的系统是为正好有两个内核的处理器创建的。基于 PTAM 的各种 SLAM 方法也不是设计为扩展到现代处理器的所有内核，也不是作为分布式系统运行的。因此，我们建议对执行众所周知的 VSLAM 系统的管道进行修改，以便它们可以在执行期间扩展到所有可用的处理器，从而提高它们在处理时间方面的性能。我们通过研究基于 PTAM 的 SLAM 系统中的线程来解释这种修改背后的原理。我们通过描述原始 ORB-SLAM 系统和修改版本的行为的实验来验证我们的结果。