在低精度数值表示下压缩高斯估计
Jose Guivant1, Karan Narula2, Jonghyuk Kim3
1School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW 2052, Australia.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
这项研究引入了一种新的最小协方差通胀 (MCI) 方法,以提高高维问题,如同时定位和映射 (SLAM) 的高维估计的计算效率. 该方法减少了处理时间,精度损失最小.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算数学 计算数学 计算数学
- 控制理论 控制理论
背景情况:
- 高维高斯估计对于同时定位和映射 (SLAM) 和随机局部微分方程 (SPDEs) 至关重要,面临着计算挑战.
- 现有的通用压缩卡尔曼波器 (GCKF) 方法可以降低复杂性,但对于嵌入式系统来说仍然是计算密集型的.
- 同变矩阵的标准双精度格式增加了计算负载.
研究的目的:
- 为高维问题提出一个计算效率高的高斯估计方法.
- 解决嵌入式处理器上通用压缩卡尔曼波器 (GCKF) 的计算成本限制.
- 为了保持过器的稳定性和准确性,尽管使用低精度的数值表示.
主要方法:
- 实现全球共变矩阵的低精度数值表示 (16位整数或32位单一精度).
- 引入最小共变量膨胀 (MCI) 技术,以抵消由共变量矩阵截断引起的不稳定性.
- 使用基于模拟的实验来评估拟议方法的性能.
主要成果:
- 与现有方法相比,拟议的方法大大减少了处理时间.
- 尽管使用低精度格式,但观察到最小的精度损失.
- 最小协差膨胀 (MCI) 方法有效地确保了过器的一致性.
结论:
- 新的最小协差膨胀 (MCI) 方法为高维系统中的高斯估计提供了一个计算效率高的解决方案.
- 这种方法使得在资源有限的嵌入式处理器上实际实现高级过器.
- 该方法平衡了计算节省与可接受的准确性,用于应用程序,如SLAM.
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