基于GPU加速的多源点云融合算法研究  

Research on Multi-Source Point Cloud Fusion Algorithm Based on GPUAcceleration

作　　者：林俊杰 刘昊洋 

机构地区：[1]长安大学工程机械学院,陕西西安710064

出　　处：《大众科技》2023年第12期11-14,共4页Popular Science & Technology

摘　　要：点云融合是一种将多个来源的点云数据配准和融合为一个更完整和准确的三维模型的重要算法。目前,已经出现了许多种点云融合算法,如串行编程方式(ICP)、非刚性配准等,这些算法通常需要大量的计算资源。文章基于图形处理单元(GPU)并行计算技术,提出了一种高效的多源点云融合算法,主要包括基于GPU的点云配准算法和点云融合算法。通过对点云数据在GPU中的存储和传输进行优化,以及设计高效的GPU并行算法,可以大幅度提高点云融合的计算速度和效率,为实际应用提供有力支持。Point cloud fusion is an important algorithm in registering and fusing point cloud data from multiple sources into a more complete and accurate three-dimensional model.In the past few decades,many point cloud fusion algorithms have been proposed,such as ICP(In-circuit programmer)and non-rigid registration,etc.These algorithms typically require a large amount of computing resources.This article proposes an efficient multi-source point cloud fusion algorithm based on GPU(graphics processing unit)parallel computing technology,mainly including GPU based point cloud registration algorithm and point cloud fusion algorithm.By optimizing the storage and transmission of point cloud data in GPU and designing efficient GPU parallel algorithms,the computing speed and efficiency of point cloud fusion can be greatly improved,providing strong support for practical applications.

关 键 词：GPU加速 点云融合 并行化计算 

分 类 号：TP312[自动化与计算机技术—计算机软件与理论]