The problem of determining the position of the lidar with optimal accuracy is relevant in various fields of application. This is an important task of robotics that is widely used as a model when planning the route of vehicles, flight control systems, navigation systems, machine learning, and managing economic efficiency, a study of land degradation processes, planning and control of agricultural production stages, land inventory to evaluations of the consequences of various environmental impacts. The paper provides a detailed analysis of the proposed parallelization algorithm for solving the problem of determining the current position of the lidar. To optimize the computing process in order to accelerate and have the possibility of obtaining a real-time result, the OpenMP parallel computing technology is used. It is also possible to significantly reduce the computational complexity of the successive variant. A number of numerical experiments on the multi-core architecture of modern computers have been carried out. As a result, it was possible to accelerate the computing process about eight times and achieve an efficiency of 0.97. It is shown that a special difference in time of execution of a sequential and parallel algorithm manages to increase the number of measurements of lidar and iterations, which is relevant in simulating various problems of robotics. The obtained results can be substantially improved by selecting a computing system where the number of cores is more than eight. The main areas of application of the developed method are described, its shortcomings and prospects for further research are provided.

中文翻译：

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基于遗传算法和OpenMP技术的激光雷达当前坐标的并行化处理。

在各种应用领域中，以最佳精度确定激光雷达的位置的问题是相关的。这是机器人技术的一项重要任务，在计划车辆路线，飞行控制系统，导航系统，机器学习和管理经济效率，研究土地退化过程，规划和控制农业生产阶段时被广泛用作模型，土地清册，以评估各种环境影响的后果。本文对提出的并行算法进行了详细分析，以解决确定激光雷达当前位置的问题。为了优化计算过程以加速并有可能获得实时结果，使用了OpenMP并行计算技术。也可以显着降低连续变量的计算复杂度。已经对现代计算机的多核体系结构进行了许多数值实验。结果，可以将计算过程加速大约八倍，并实现0.97的效率。结果表明，执行顺序和并行算法的时间上的特殊差异设法增加了激光雷达和迭代次数的测量次数，这在模拟机器人技术的各种问题方面均具有重要意义。通过选择内核数大于八个的计算系统，可以大大改善获得的结果。描述了所开发方法的主要应用领域，其缺点和进一步研究的前景。