Visual 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.