Parallelization of Finding the Current Coordinates of the Lidar Based on the Genetic Algorithm and OpenMP Technology

Abstract

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.

Leveraging LiDAR-Based Simulations to Quantify the Complexity of the Static Environment for Autonomous Vehicles in Rural Settings

“…The literature shows that leveraging parallel computations is a highly effective strategy for addressing real-time processing challenges associated with large datasets. For instance, [ 27 ] developed an optimized algorithm using the OpenMP technology to expedite the processing time of determining the position of the LiDAR by eight times. This is especially relevant as computing system architectures are currently experiencing substantial advancements and are positioned to become even more sophisticated in the future [ 27 ].…”

“…For instance, [27] developed an optimized algorithm using the OpenMP technology to expedite the processing time of determining the position of the LiDAR by eight times. This is especially relevant as computing system architectures are currently experiencing substantial advancements and are positioned to become even more sophisticated in the future [27].…”

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