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准确的全球点云注册使用基于GPU的并行角拉登光谱

Ernesto Fontana¹, Dario Lodi Rizzini^1,2

¹Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze 181/A, 43124 Parma, Italy.

Sensors (Basel, Switzerland)

|October 28, 2023

概括

这项研究介绍了Cud-ARS,这是一个GPU加速算法,通过优化角度子光谱 (ARS) 计算来显著加快机器人本地化和映射. 它实现了机器人任务的更快,更准确的2D注册.

科学领域:

机器人技术机器人技术机器人技术
计算机视觉计算机视觉
并行计算是一种平行计算.

背景情况:

准确的机器人定位和映射对于自主系统至关重要.
全球最优的注册方法,如角度光谱 (ARS),提供了更高的准确性.
ARS的计算成本限制了其广泛的应用.

研究的目的:

介绍Cud-ARS,这是2D注册的ARS算法的高效并行变体.
为了利用Nvidia的图形处理单元 (GPU) 加快计算密集型的步骤.
为了提高机器人定位和映射的准确性和速度.

主要方法:

开发了Cud-ARS,用于使用点子集在GPU上并行计算ARS.
实施全球分支和结合方法用于翻译估计.
在多个数据集上测试了算法,以评估性能.

主要成果:

与现有方法相比,实现了两个数量级的加速.
证明了比最先进的基于对应的算法更准确的旋转估计.
验证了该方法在映射应用中的有效性.

结论:

Cud-ARS为2D注册提供了显著的加快速度和更高的准确性.
GPU编程是有效的机器人任务解决方案的可行方法.
该方法显示了增强机器人本地化和映射系统的巨大潜力.

关键词:

我们的GPU是GPU的GPU 绘制地图,绘制地图.平行处理是平行处理.注册注册注册注册注册注册注册注册是什么意思

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